System and method for lung-volume-gated x-ray imaging

Abstract

To provide a geometrically-resolved X-ray imaging system (GREX).SOLUTION: A method of imaging a patient's lung comprises the following steps of: positioning the patient at a first orientation relative to an X-ray imaging apparatus; obtaining a volumetric measurement of the patient's breathing; while the patient is positioned at the first orientation relative to the X-ray imaging apparatus, and while obtaining the volumetric measurement of the patient's breathing, determining, based on the volumetric measurement of the patient's breathing, a breathing phase of the patient; and, upon determination that the breathing phase of the patient matches a predefined breathing phase, gating the X-ray imaging apparatus to produce an X-ray projection image of the patient's lung.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] Some embodiments of the present disclosure relate to medical imaging, and more particularly to systems and methods for performing geometrically resolved X-ray imaging.Some embodiments of the present disclosure relate to radiation therapy. [Background technology]

[0002] Computational modeling of the human body has made it easier to understand the anatomical behaviors that represent various physiological states. Although state-of-the-art imaging techniques allow physicians to visualize anatomical behaviors, the latest techniques that can accurately image the complex motion of the heart and lungs are often too expensive to employ. Furthermore, cardiac motion, e.g., cardiac deformations that are complex and unrelated to the respiratory cycle, can appear as noise in CT or x-ray respiratory motion measurements. As a result, the accuracy of mathematical models that represent respiratory motion is reduced due to the seemingly random cardiac motion. One way to address this issue is to have the patient hold their breath, preventing lung motion from appearing in the images. Because the patient's respiratory motion is stopped, it can be difficult to determine important information about lung health from the images taken during breath-holding. Summary of the Invention

[0003] SUMMARY OF THE DISCLOSURE Embodiments of the present disclosure aim to alleviate, in part, at least one of the problems of the prior art by providing a geometrically resolved X-ray imaging system (GREX).

[0004] According to some embodiments, the GREX imaging system acquires images of the patient's chest by calculating and targeting specific respiratory periods and specific cardiac phases. The GREX imaging system acquires a time-independent anatomical shape by taking snapshots of the chest at targeted respiratory periods in a series of imaging planes. The time-independent anatomical shape is registered and interpolated based on the collected respiratory and cardiac signals to create an interactive volumetric movie of the chest. The interactive movie has important advantages over traditional still imaging. Using the interactive movie, GREX creates a biomechanical model that quantitatively describes the shape changes of the chest that occur during the patient's breathing. The biomechanical model is based on fundamental physical laws and provides estimates of important metrics such as elasticity, stress, strain, and respiratory compliance of lung tissue. These characteristics are currently not widely available to medical professionals, such as physicians. Physicians can therefore use this information provided by the GREX imaging system to gain insight into the etiology of lung disease in patients.

[0005] In some embodiments, the GREX imaging system includes proprietary hardware and software add-on packages for use with existing digital diagnostic X-ray units. The combination of the hardware and software add-on packages enhances the diagnostic quality of conventional digital diagnostic X-ray units with new and improved imaging capabilities that use the patient's vital signs to provide custom imaging procedures that provide more diagnostically useful information. The hardware and software add-on packages are generic upgrades that can be applied to any commercially available digital diagnostic X-ray unit (e.g., conventional X-ray units).

[0006] According to some embodiments, there is provided a method of imaging the lungs of a patient. The method includes positioning the patient in a first orientation relative to an x-ray imaging device (e.g., an x-ray unit) and acquiring respiratory volume measurements of the patient. The method includes determining a respiratory phase of the patient based on the respiratory volume measurements of the patient while the patient is positioned in the first orientation relative to the x-ray imaging device and while acquiring the respiratory volume measurements of the patient. The method includes determining a respiratory phase of the patient based on the respiratory volume measurements of the patient while the patient is positioned in the first orientation relative to the x-ray imaging device and while acquiring the respiratory volume measurements of the patient. and gating an x-ray imaging device to generate x-ray projection images of the patient's lungs when the determined respiratory phase coincides with a predetermined respiratory phase.

[0007] In some embodiments, the predefined respiratory phase is a first predefined respiratory phase of a plurality of predefined respiratory phases, and the method further includes gating an X-ray imaging device to generate an X-ray projection image of the patient's lungs when the patient's respiratory phase is determined to coincide with any of the plurality of predefined respiratory phases while obtaining the patient's respiratory volume measurements.

[0008] In some embodiments, x-ray measurements of the patient's lungs are taken only if the patient's respiratory phase, as determined by the patient's respiratory volume measurements, corresponds to any one of a number of predefined respiratory phases.

[0009] In some embodiments, the plurality of predefined respiratory phases comprises an early expiratory phase, a late expiratory phase, a maximum expiratory phase, an early inhalation phase, a late inhalation phase, and a maximum inhalation phase in a complete respiratory cycle of the patient.

[0010] In some embodiments, the X-ray projection image is a first X-ray projection image, and the method further includes repositioning the patient to a second orientation relative to the X-ray imaging device. In some embodiments, the method further includes continuing to determine a respiratory phase of the patient based on the patient's respiratory volume measurements while the patient is positioned in the second orientation relative to the X-ray imaging device and while continuing to obtain respiratory volume measurements of the patient, and gating the X-ray imaging device to generate a second X-ray projection image of the patient's lungs when the patient's respiratory phase is determined to match a predefined respiratory phase. In some embodiments, the method includes generating a still image cube corresponding to the predefined respiratory phase using the first X-ray projection image and the second X-ray projection image.

[0011] In some embodiments, the still image cube corresponding to a given respiratory phase is generated from less than 10 x-ray projection images taken from different angles during the given respiratory phase.

[0012] In some embodiments, the patient's respiratory volume measurement comprises a chest rise measurement of the patient.

[0013] In some embodiments, the patient's respiratory volume measurements are obtained using one or more of the following volumetric respiratory sensors: a 3D scanner, a spirometer, and an abdominal belt.

[0014] In some embodiments, the method further comprises creating a point cloud on a surface of the patient's chest, and the respiratory volume measurements of the patient are determined from the point cloud on the surface of the patient's chest.

[0015] In some embodiments, a cloud of points on the surface of the patient's chest is acquired using 3D imaging techniques to measure one or more locations on the patient's chest.

[0016] In some embodiments, the method further includes identifying one or more anatomical landmarks on the patient's chest surface using the cloud of points on the patient's chest surface, and inferring locations of one or more internal anatomical landmarks within the patient's chest from the cloud of points on the patient's chest surface.

[0017] In some embodiments, the patient's respiratory phase is a future respiratory phase, and determining the patient's respiratory phase based on the patient's respiratory volume measurements includes predicting the future respiratory phase from one or more current and / or past respiratory phases.

[0018] In some embodiments, a method is provided, the method comprising: The method includes positioning the patient in a first orientation relative to the radiation source, acquiring respiratory measurements from the patient, and acquiring cardiac measurements from the patient. The method further includes, while the patient is positioned in a first orientation relative to the radiation source and while acquiring the respiratory measurements from the patient, determining a respiratory phase of the patient from the respiratory measurements and determining a cardiac phase of the patient from the cardiac measurements from the patient. The method further includes gating the radiation source based on determining that the respiratory phase of the patient corresponds to a predetermined respiratory phase and determining that the cardiac phase of the patient corresponds to a predetermined window of the cardiac cycle to expose the patient to radiation.

[0019] In some embodiments, the radiation source is an X-ray imaging device, and gating the radiation source to expose the patient to radiation includes gating the X-ray imaging device to generate an X-ray projection image of the patient's lungs.

[0020] In some embodiments, the radiation source is a radiation therapy source, and gating the radiation source to expose the patient to radiation includes gating the radiation therapy source to deliver a therapeutic dose to a lung region of the patient.

[0021] In some embodiments, the method further includes obtaining cardiac function measurements of the patient from a plurality of cardiac cycles of the patient prior to gating the radiation source to expose the patient to radiation, and determining an average interval between a predefined cardiac phase and a start of the predefined window of the cardiac cycle using the cardiac function measurements of the patient from the plurality of cardiac cycles. In some embodiments, determining that the patient's cardiac phase coincides with the predefined window of the cardiac cycle includes predicting the predefined window of the cardiac cycle by detecting the predefined cardiac phase in real time and waiting a length of time corresponding to the average interval between the predefined cardiac phase and a start of the predefined window of the cardiac cycle.

[0022] In some embodiments, the measurements obtained from multiple cardiac cycles of the patient are waveform measurements of the multiple cardiac cycles, and the method further includes validating the waveform measurements of the multiple cardiac cycles as being statistically stable.

[0023] In some embodiments, the predetermined window of the cardiac cycle is a sedation window of the cardiac cycle.

[0024] In some embodiments, a method for determining a biophysical model of a patient's lungs from a plurality of X-ray measurements corresponding to different respiratory phases of the lungs is provided. The method includes extracting a plurality of displacement fields of lung tissue from a plurality of X-ray measurements corresponding to different respiratory phases of the lungs. Each displacement field represents a motion of the lung tissue from a first respiratory phase to a second respiratory phase, each respiratory phase having a corresponding set of bioparameters. The method further includes calculating one or more biophysical parameters of the lung biophysical model using the plurality of displacement fields of the lung tissue between the different respiratory phases of the lungs and the corresponding set of bioparameters.

[0025] In some embodiments, the one or more biophysical parameters define a physical relationship between a bioparameter associated with different respiratory phases of the lung and a plurality of displacement fields of the lung tissue.

[0026] In some embodiments, the physical relationship between the biological parameters associated with different respiratory phases of the lungs and the multiple displacement fields of the lung tissue is defined by the following equation:

number

[0027]

number

number

number

number

number

[0028] In some embodiments, the method further comprises generating a plurality of medical image cubes corresponding to different respiratory phases of the lungs from a plurality of X-ray measurements corresponding to different respiratory phases of the lungs. A plurality of displacement fields of the lung tissue are extracted from the plurality of medical image cubes corresponding to different respiratory phases of the lungs by further delineating the lung tissue from the remaining portion of the first medical image cube by image segmentation. The method comprises: for each voxel in the first medical image cube, determining a displacement vector between the voxel in the first medical image cube and the second medical image cube using an intensity-based structure mapping between the first medical image cube and the second medical image cube; and iteratively adjusting the displacement vectors of different voxels in the first medical image cube and corresponding voxels in the second medical image cube.

[0029] In some embodiments, the set of physiological parameters associated with each respiratory phase includes tidal volume and airflow of the lungs during each respiratory phase, and the cardiac phase corresponding to each respiratory phase of the lungs.

[0030] In some embodiments, the method further includes generating a plurality of medical image cubes corresponding to different respiratory stages of the lungs from a plurality of X-ray measurements corresponding to different respiratory stages of the lungs. In some embodiments, the method includes selecting one or more of the plurality of medical image cubes as a reference medical image cube, determining a set of bio-parameters associated with each reference medical image cube, and selecting a set of bio-parameters based on the bio-measurements of the lungs between two sets of bio-parameters associated with the two reference medical image cubes. In some embodiments, the method further includes simulating the medical image cube between the two reference medical image cubes by applying the set of bio-parameters based on the bio-measurements of the lungs to a bio-physical model.

[0031] In some embodiments, the different respiratory phases of the lungs include an early expiratory phase, a late expiratory phase, a maximum expiratory phase, an early inspiratory phase, a late inspiratory phase, and a maximum inspiratory phase in a complete respiratory cycle of the patient.

[0032] In some embodiments, one or more sensors are used to measure the patient's biosignals as one or more time-series sequences, and the one or more sensors include one or more 3D spatial position localizers, respiratory phase sensors, and cardiac phase sensors.

[0033] In some embodiments, the 3D spatial position localizer is configured to measure and output a time series of the patient's body movements caused by breathing and heartbeat in real time.

[0034] In some embodiments, the respiratory phase sensor is configured to measure one or more physiological indicators related to the patient's respiration, including tidal volume and its first time derivative.

[0035] In some embodiments, the cardiac phase sensor is configured to measure a periodic, steady-state electrical signal generated by the patient's heart.

[0036] In some embodiments, a patient's vital signs, measured by one or more sensors, are used to trigger an x-ray unit to acquire x-ray images of the patient at specific respiratory and cardiac phases.

[0037] In some embodiments, the X-ray unit includes a clock, and the patient's vital signs measured by the one or more sensors are synchronized with the clock of the X-ray unit, hi some embodiments, the respective values ​​of the vital signs are recorded in association with the acquired X-ray image.

[0038] In some embodiments, the patient's vital signs measured during the training frame are used to build an optimized respiratory prediction model to predict the desired respiratory phase for triggering the X-ray unit to take an X-ray image of the patient.

[0039] In some embodiments, a method for generating a 3D X-ray image cube movie from 2D X-ray images of a patient is provided, the method comprising: transforming a first plurality of sets of X-ray images of the lungs taken at different projection angles into a second plurality of sets of X-ray images of the lungs corresponding to different respiratory phases, the method further comprising generating still image cubes from each of the second plurality of sets of X-ray images at each respiratory phase using a backprojection method, and combining the still image cubes corresponding to the different respiratory phases of the lungs into a 3D X-ray image cube movie by a time interpolation method.

[0040] In some embodiments, transforming the first plurality of sets of x-ray images of the lungs taken at different projection angles into a second plurality of sets of x-ray images of the lungs corresponding to different respiratory phases further comprises taking the first plurality of sets of x-ray images of the lungs at different projection angles, each set of the first plurality of sets of x-ray images of the lungs corresponding to a different respiratory phase of the lungs at a particular projection angle, and the transforming further comprises reorganizing the first plurality of sets of x-ray images of the lungs into a second plurality of sets of x-ray images of the lungs according to associated respiratory phases, each set of the second plurality of sets of x-ray images corresponding to a different respiratory phase of the lungs.

[0041] In some embodiments, the x-ray images in any particular set are geometrically resolved and temporally independent.

[0042] In some embodiments, different respiratory phases of the lungs correspond to different tidal volume percentiles of lung movement.

[0043] In some embodiments, the different respiratory phases of the lungs include an early expiratory phase, a late expiratory phase, a maximum expiratory phase, an early inspiratory phase, a late inspiratory phase, and a maximum inspiratory phase in a complete respiratory cycle of the patient.

[0044] In some embodiments, multiple x-ray images of the lungs taken at different projection angles all correspond to the same respiratory phase.

[0045] In some embodiments, one or more sensors are used to measure the patient's biosignals as one or more time-series sequences, and the one or more sensors include one or more 3D spatial position localizers, respiratory phase sensors, and cardiac phase sensors.

[0046] In some embodiments, the method further includes identifying a cardiac phase gate window using measurements from one or more cardiac phase sensors, predicting a respiratory phase using measurements from one or more respiratory phase sensors, identifying a coincidence between the cardiac phase gate window and the predicted respiratory phase to generate an X-ray imaging pulse, and tagging an X-ray image corresponding to the X-ray imaging pulse with the respiratory phase, cardiac phase, and 3D spatial position localizer measurements.

[0047] In some embodiments, the 3D spatial position localizer is configured to measure and output a time series of the patient's body movements caused by breathing and heartbeat in real time.

[0048] In some embodiments, the respiratory phase sensor is configured to measure one or more physiological indicators related to the patient's respiration, including tidal volume and its first time derivative.

[0049] In some embodiments, the cardiac phase sensor is configured to measure a periodic, steady-state electrical signal produced by the patient's heart and having characteristics corresponding to cardiac phases.

[0050] In some embodiments, after being synchronized with the X-ray unit's clock, two separate filters are used to remove signal drift and noise from the patient's biosignals.

[0051] In some embodiments, a patient's vital signs, measured by one or more sensors, are used to trigger an x-ray unit to acquire x-ray images of the patient at specific respiratory and cardiac phases.

[0052] In some embodiments, the X-ray unit includes a clock, and the patient's vital signs measured by the one or more sensors are synchronized with the clock of the X-ray unit, hi some embodiments, the respective values ​​of the vital signs are recorded in association with the acquired X-ray image.

[0053] In some embodiments, the patient's vital signs are measured during a training frame prior to taking an x-ray image of the patient, and the patient's vital signs measured during the training frame include a number of complete respiratory cycles of the patient.

[0054] In some embodiments, multiple tidal volume percentiles for a complete respiratory cycle are identified using the patient's biosignals measured during a training frame, each tidal volume percentile corresponding to one of the different respiratory phases.

[0055] In some embodiments, the patient's vital signs measured during the training frame are used to build an optimized respiratory prediction model to predict the desired respiratory phase for triggering the X-ray unit to take an X-ray image of the patient.

[0056] In some embodiments, the optimized respiratory prediction model is based on an autoregressive integrated moving average (ARIMA) model.

[0057] In some embodiments, the desired respiratory phase for taking x-ray images of the patient is configured to coincide with a cardiac gating window during which cardiac-induced lung motion is slowly changing.

[0058] In some embodiments, the cardiac gating window is selected based on the location of the T and P waves in an electrocardiogram (ECG) signal, which slowly changes the cardiac-induced pulmonary motion.

[0059] In some embodiments, different respiratory phases of the lungs at a particular projection angle are collected from at least two respiratory cycles.

[0060] Further aspects and advantages of the embodiments of the disclosure will be apparent from the following description, or will be understood from the practice of the embodiments of the disclosure. [Brief description of the drawings]

[0061] In order to more clearly describe the embodiments of the present disclosure or the technical solutions in the prior art, the drawings necessary for describing the embodiments or the prior art will be briefly described. A person skilled in the art can derive other drawings from the structures shown in the drawings without exerting his or her imagination.

[0062] [Figure 1] FIG. 1 is a schematic block diagram of a GREX imaging system including a hardware box, acquisition software, and post-processing software, in accordance with some embodiments of the present disclosure.

[0063] [Diagram 2] A simplified flowchart of a GREX image acquisition process according to some embodiments of the present disclosure.

[0064] [Diagram 3] FIG. 1 is a schematic block diagram illustrating a top view of a 3D spatial position localizer of a GREX imaging system, in accordance with some embodiments of the present disclosure;

[0065] [Figure 4]FIG. 11 is an example of two breaths that share the same maximum inspiratory phase in time but have radically different tidal volumes, according to some embodiments of the present disclosure.

[0066] [Diagram 5] 14A-14C are examples of tidal volume percentiles (bottom two subplots) for a patient breathing normally (top left subplot) and for a patient breathing irregularly (top right subplot), according to some embodiments of the present disclosure.

[0067] [Figure 6] A simplified flowchart of a synchronization process according to some embodiments of the present disclosure.

[0068] [Figure 7] Synchronized cardiac electrocardiogram (ECG) and pulmonary respiratory signals according to some embodiments of the present disclosure

[0069] [Figure 8] 1 is a schematic flow chart of a drift and signal noise removal process, according to some embodiments of the present disclosure;

[0070] [Figure 9] 1 is a plot of tidal volume versus time for an exemplary breath, according to some embodiments of the present disclosure;

[0071] [Figure 10] 10 is a plot of airflow versus tidal volume for the same breath illustrated in FIG. 9 , according to some embodiments of the present disclosure.

[0072] [Figure 11] A schematic flowchart of a respiratory phase prediction process according to some embodiments of the present disclosure.

[0073] [Figure 12] 13 is an example of a gating window in time chosen based on the location of T and P waves in an ECG signal such that cardiac-induced pulmonary motion changes slowly, according to some embodiments of the present disclosure.

[0074] [Figure 13] 1 is a schematic flow chart of another GREX procedure using cardiac phase prediction instead of real-time identification of cardiac gating windows, in accordance with some embodiments of the present disclosure;

[0075] [Figure 14] 1A-1C are examples of trigger images in the early expiratory phase, late expiratory phase, maximum expiratory phase, early inspiratory phase, late inspiratory phase, and maximum inspiratory phase (from left to right) according to some embodiments of the present disclosure.

[0076] [Figure 15] A simplified flowchart of an image capture triggering process according to some embodiments of the present disclosure.

[0077] [Figure 16] FIG. 1 is a schematic block diagram of variables used in illustrating the GREX image reconstruction algorithm according to some embodiments of the present disclosure;

[0078] [Figure 17] FIG. 1 is a schematic block diagram of operating components of a GREX imaging system during an imaging procedure in accordance with some embodiments of the present disclosure.

[0079] [Figure 18] 1A and 1B are examples of transverse x-ray projection images at tissue depths of 0° (A) and 90° (B), according to some embodiments of the present disclosure.

[0080] [Figure 19] 1 shows examples of depth resolution from a narrow projection angle range (1) and a wide projection angle range (2) according to some embodiments of the present disclosure.

[0081] [Figure 20] 14A-14C are examples of β imaging angle positions relative to the three primary imaging planes (left) and the imaging isocenter of the GREX imaging system in a top view (right), according to some embodiments of the present disclosure.

[0082] [Figure 21]FIG. 1 is an example block diagram illustrating how the GREX imaging system converts 2D X-ray projection images into still image cubes and ultimately into movie image cubes, according to some embodiments of the present disclosure.

[0083] [Figure 22] 22 is an illustration of the still image cube creation process depicted in FIG. 21 according to some embodiments of the present disclosure.

[0084] [Figure 23] A simplified flowchart of an image filtering process according to some embodiments of the present disclosure.

[0085] [Figure 24] 1 is an example of a closed-loop lung tissue trajectory of a tissue fragment located in the left lung close to the heart caused by heart-lung interaction, according to some embodiments of the present disclosure.

[0086] [Diagram 25] Schematic flow chart illustrating the operation of components of a biomechanical model according to some embodiments of the present disclosure.

[0087] [Figure 26] (A) An illustration of the motion trajectories of lung tissue elements during a respiratory cycle according to some embodiments of the present disclosure.

[0088] [Figure 26] (B) An illustration of displacement vectors between different respiratory phases, according to some embodiments of the present disclosure.

[0089] [Figure 27] 1 is a schematic flow chart of a multi-resolution 3D optical flow algorithm according to some embodiments of the present disclosure;

[0090] [Figure 28] 1A is a schematic flow chart illustrating movie creation using biointerpolation, according to some embodiments of the present disclosure.

[0091] [Figure 28](B) Block diagram illustrating intermediate image cube creation using biometric data matrices, according to some embodiments of the present disclosure.

[0092] [Figure 29] Example of a standard radiograph of a healthy individual and a standard radiograph of a patient with a stage 1b upper left lung tumor (arrow).

[0093] [Diagram 30] 10A-10C are examples of GREX parameter maps showing health status indicators for healthy subjects and for patients, according to some embodiments of the present disclosure.

[0094] [Diagram 31] 1A and 1B are flow diagrams of a method for imaging the lungs of a patient, according to some embodiments of the present disclosure.

[0095] [Diagram 32] 1A and 1B are flow diagrams of a method for gating a radiation source according to some embodiments of the present disclosure.

[0096] [Diagram 33] 1A-1C are flow diagrams of a method for determining a biophysical model of a patient's lungs, according to some embodiments of the present disclosure.

[0097] [Diagram 34] 1A-1C are flow diagrams of a method for generating a 3D X-ray image cube movie according to some embodiments of the present disclosure.

[0098] [Diagram 35] 1 is an example of a Patient Positioning Fixture (PPF) supporting a patient (e.g., a swivel chair), according to some embodiments;

[0099] [Diagram 36] Example of a Biological Event Monitoring Process (BEMP) card, according to some embodiments. Detailed Description of the Invention

[0100] The embodiments of the present disclosure are described in detail below. The same or similar elements or elements having the same or similar functions are assigned the same reference numerals throughout the specification. The embodiments described in the present specification with reference to the drawings are merely examples for enhancing the understanding of the present disclosure, and should not be construed as limiting the present disclosure.

[0101] In this specification, unless otherwise specified, relative terms such as "center," "longitudinal," "lateral," "front," "rear," "right," "left," "inside," "outside," "lower," "top," "horizontal," "vertical," "upper," "lower," "top," and derivatives thereof (e.g., "horizontal," "downward," "upward," etc.) should be construed to refer to the orientations described or illustrated in the associated drawings. The above relative terms are used for convenience of explanation and do not imply construction or operation in a particular orientation.

[0102] In the present invention, unless otherwise specified, the terms "attached," "connected," "coupled," "fixed," and the like are used broadly, which may refer to various forms that one skilled in the art would understand depending on the particular situation, such as a fixed connection, a detachable connection, an integral type, a mechanical or electrical connection, a direct connection, an indirect connection through an intervening structure, internal communication between two elements, and the like.

[0103] In the present invention, unless otherwise specified, a structure in which a first feature element is "above" or "below" a second feature element includes an embodiment in which the first feature element is in direct contact with the second feature element, and may further include an embodiment in which the first feature element is not in direct contact with the second feature element, or an embodiment in which the first feature element is in contact with the second feature element via an additional element. In addition, when a first feature element is "above", "upper", or "top" of a second feature element, it may include an embodiment in which the first feature element is directly above or diagonally above the second feature element, or it may simply mean that the first feature element is at a higher position than the second feature element. On the other hand, when a first feature element is "below", "below", or "lower" of a second feature element, it may include an embodiment in which the first feature element is directly below or diagonally below the second feature element, or it may simply mean that the first feature element is at a lower position than the second feature element.

[0104] FIG. 1 is a schematic block diagram of a GREX imaging system 100 including a hardware box 102, acquisition software 104 (e.g., stored in non-transitory memory), and post-processing software 106 (e.g., stored in non-transitory memory), in accordance with some embodiments of the present disclosure.

[0105] The hardware box 102 has three measurement sensors (e.g., a 3D spatial position localizer 300, a respiratory phase sensor 110, and a cardiac phase sensor 112) that independently collect the patient's biosignals as time series. The collected time series are input to the acquisition software 104. The acquisition software 104 processes and filters the biosignals time series measurements and generates an imaging trigger signal (e.g., a signal to activate the X-ray unit 108). The imaging trigger signal targets a specific respiratory phase, or optionally a cardiac phase of the patient. The respiratory phase and cardiac phase are each defined by the biosignals time series measurements. A connecting cable transmits the imaging trigger signal from the acquisition software 104 to the X-ray unit 108, which acquires radiographic images of the respiratory and cardiac phases at the targeted respiratory phases. A series of images captured at the targeted respiratory and cardiac phases that define a complete respiratory cycle are input to the post-processing software 106. The post-processing software builds a biomechanical model of lung motion from the images captured at the desired respiratory and cardiac phases. The biomechanical model is then used by the post-processing software to generate other diagnostic results. Although X-ray images are used as an example in this application, one skilled in the art should understand that the methods disclosed herein can be applied to other medical images with little effort. For example, the biomechanical model building process is not limited to X-ray images, but other medical images (e.g., CT scans and MRIs) can also be used.

[0106] FIG. 2 is a schematic flow chart of a GREX image acquisition process 200 (e.g., performed by the acquisition software 104 of FIG. 1) according to some embodiments of the present disclosure. Specifically, the acquisition software 104 synchronizes and processes the time series measurements from the hardware box 102 to remove potential signal drift and noise. The acquisition software 104 then implements a phase prediction algorithm to predict the respiratory and cardiac phases from the synchronized, drift-free, and noise-free time series input. Based on the predicted respiratory and cardiac phases, the acquisition software 104 uses a logic algorithm 118 to search for a match with the target respiratory and cardiac phases. Matching with the target respiratory and cardiac phases is a condition that allows the generation of an imaging trigger and thus the acquisition of an image by the x-ray unit 108. Once the GREX images are acquired, the post-processing software uses the GREX images to diagnose new lung diseases. Until recently, GREX images were not widely used by medical professionals. For example, using the acquired GREX images, we construct a biomechanical model that defines the shape of the chest as a function of respiratory and cardiac phases without using any time parameters.

[0107] The GREX-based imaging system is described herein in three parts. Chapter 1 relates to an embodiment of the hardware box 102. Chapter 2 deals with an embodiment of the acquisition software 104. Chapter 3 is an embodiment of the post-processing software 106. Each chapter describes in more detail the components and functions that make up the hardware box 102, acquisition software 104 and post-processing software 106 shown in FIG. 1.

[0108] Chapter 1. Hardware Box 102

[0109] In some embodiments, the hardware box 102 serves at least two purposes: to collect biosignals that define the anatomical shape of the chest, and to communicate with a digital diagnostic X-ray unit (e.g., X-ray unit 108).

[0110] The biosignal inputs that define the thorax anatomical shape include thorax dimensions (measured by 3D spatial localizer 300, FIG. 3), respiratory phase (measured by respiratory phase sensor 110), and cardiac phase (measured by ECG monitor). In hardware box 102, the biosignals are sampled in real time at 100 Hz to create a time series curve for each signal input. The output of hardware box 102, i.e., the time series curve, is sent to the acquisition software.

[0111] FIG. 3 is a schematic block diagram illustrating a top view of a 3D spatial position localizer 300 of the GREX imaging system 100, according to some embodiments of the present disclosure. 1.1.3D Spatial Position Localizer 300

[0112] The 3D spatial position localizer 300 measures the patient's body movements caused by breathing and heartbeat in real time and outputs them as a time series in a coordinate space (e.g., Cartesian, polar, or hyperspherical coordinate system). As shown in FIG. 3, the 3D spatial position localizer 300 includes three 3D cameras (e.g., cameras 302a-302c) fixed to a balanced circular path on the ceiling of a room. The three cameras 302a are spaced 120° apart from each other. The patient is located between the X-ray unit 108 and the detector panel 304, and is therefore located at the center of the ceiling-mounted 3D spatial position localizer 300. The system measures the patient's chest expansion (e.g., rise and fall) in a predefined coordinate space. Each camera 302 has an unobstructed view of the patient's torso and one of the X-ray unit 108 and the detector panel 304.

[0113] Using the real-time depth map, each 3D camera 302 creates a surface rendering of the patient. The information from the three 3D cameras 302 is combined to form volumetric skin surface position measurements (e.g., using light projection techniques) that change in real time based on the patient's respiratory and cardiac phases. The 3D spatial position localizer 300 uses the volumetric skin surface position measurements in at least two ways: (i) to define the spatial boundaries of the patient; and (ii) to determine the tissue locations of the patient. Based on prior information such as skin thickness, rib thickness, muscle thickness, and skeletal location from a formal Medical Internal Radiation Dose (MIRD) anatomical database, the 3D spatial position localizer 300 estimates the real-time spatial location of the lungs within the patient. The 3D spatial position localizer 300 uses the MIRD data to calculate the spatial boundary conditions of the lungs, which will later be utilized by the post-processing software 106. For example, the 3D spatial position localizer 300 estimates of the lung spatial boundary conditions create an initial chest shape that the post-processing software 106 uses to simulate the cumulative tissue density along rays generated by the x-ray unit 108 . 1.2. Respiratory Phase Sensor 110

[0114] The respiratory phase sensor 110 of the hardware box 102 measures the main physiological indices related to breathing: tidal volume and its first time derivative (e.g., rate of change of tidal volume over time, or flow rate). There are two methods for measuring tidal volume: direct tidal volume measurement and indirect tidal volume measurement. Direct tidal volume measurement uses a mouth spirometer, which consists of a turbine in a tube that rotates at a speed proportional to the flow rate of the patient's inhalation or exhalation. Indirect tidal volume measurement uses an abdominal belt (or other geometric measurement of the patient's chest, described below) that measures the change in the patient's abdominal circumference during breathing (see FIG. 1). A larger abdominal circumference indicates inhalation and a smaller abdominal circumference indicates exhalation. An abdominal belt cannot directly measure tidal volume. To convert the abdominal circumference change into a physiologically meaningful quantity, the hardware box 102 correlates the abdominal circumference change with the estimated lung volume determined by the 3D spatial position localizer 300. For example, chest expansion is proportional to abdominal expansion during breathing. The abdominal belt and 3D spatial localizer 300 measurements may also be used together to estimate lung air content.

[0115] As used herein, "tidal volume" refers to the difference between the current lung volume and a predefined baseline (e.g., maximal exhalation without extra effort, maximal inhalation without extra effort, or other baseline volume). Based on the ideal gas law, the air in the lungs expands by 11% over the tidal volume due to the density difference between the room air and the air inside. Conservation of mass dictates that the body expands by 11% over the inhalation volume. Thus, the tidal volume of the lungs is can be calculated using external measurements of the body calibrated to internal air volume. The 3D spatial position localizer 300 also knows the patient's volume expansion rate during breathing to provide a second validation of the accuracy of the air volume and tidal volume measurements by comparing the body's volume expansion rate with air volume estimates in the trachea, lungs, and bronchi obtained from x-ray images. 1.3. Heart Rate Phase Sensor 112

[0116] As shown in FIG. 1, cardiac phase is measured using either an electrocardiogram (ECG) monitor or a blood volume pressure device (e.g., cardiac phase sensor 112). For example, a clinician may use an ECG monitor with leads attached to the patient's arm and a ground lead attached to the left lower side of the patient's abdomen (away from the diaphragm and abdominal belt). The human heart produces periodic, stationary electrical signals with characteristics that correspond to the phase of the heartbeat. Stationary signals are stochastic processes whose joint probability distribution does not change over time. Blood volume pressure devices use a light source and a light sensor to measure the optical attenuation of the patient's finger. The volume of circulating blood pumped with each heartbeat changes the optical attenuation of the finger. The optical attenuation is directly proportional to the cardiac phase.

[0117] Typically, a digital diagnostic x-ray unit (e.g., x-ray unit 108) is actuated by an analog plunger that attaches to a plug port. The plug port is uniquely configured to accept a plunger having a particular pin configuration. Each pin configuration includes an "acquisition pin" that receives a voltage signal that activates (e.g., gates) the x-ray unit. When a user presses the plunger, it sends a voltage pulse to the digital diagnostic x-ray unit. The digital diagnostic x-ray unit activates an imaging beam that penetrates the patient's body.

[0118] As shown in FIG. 1, the hardware box 102 sends the same voltage pulse (e.g., gate signal) from the acquisition software to the digital diagnostic x-ray unit 108 using a connecting cable. When the voltage pulse signal exceeds a predefined voltage threshold, it activates the digital diagnostic x-ray beam (e.g., gates the x-ray beam), and when the voltage pulse signal at the pin is below the predefined voltage threshold, the x-ray unit 108 is turned off. In some embodiments, the hardware box 102 generates a square wave signal with a pulse height greater than the predefined voltage threshold and maintains the pulse height during the image exposure. That is, it generates a square pulse of Y voltage lasting X seconds so that the x-ray unit 108 can take an x-ray image of the patient. A threshold voltage less than Y will not trigger the scanner, so the value of Y must be greater than the predefined voltage threshold to start x-ray imaging. The pulse duration X refers to the exposure time of the image, which starts from the moment the voltage Y exceeds the predefined voltage threshold and ends when the voltage falls below the predefined voltage threshold. When the voltage falls below the predefined voltage threshold, the x-ray unit 108 is turned off. The pulse duration is defined by the manufacturer's specifications, but is typically on the order of a few milliseconds.

[0119] Chapter 2. Acquisition Software 104

[0120] The acquisition software 104 collects the spatial, cardiac and pulmonary time series measured by the hardware box 102 and determines when to trigger the X-ray unit 108 to acquire X-ray images at specific respiratory and cardiac phases. The acquisition software 104 accurately overlays (e.g., synchronizes) the measured cardiac and pulmonary phases, processes (e.g., filters) the collected biometric time series, figures out the appropriate imaging time, and generates an electronic trigger signal for the digital diagnostic X-ray unit 108. The electronic trigger signal (e.g., gating signal) activates the X-ray unit 108 to acquire a snapshot image of the chest geometry. It records the spatial, cardiac and pulmonary values ​​associated with the snapshot, defining the surface geometry of the chest at the time the image was taken. The process is a fully automated process and does not depend on user input. As illustrated in Figures 1 and 2, the process implemented by the acquisition software 104 is divided into four subcomponents: 2.1 (synchronization module 114), 2.2 (signal acquisition module 115), 2.3 (signal acquisition module 116), 2.4 (signal acquisition module 118), 2.5 (signal acquisition module 119), 2.6 (signal acquisition module 200), 2.7 (signal acquisition module 202), 2.8 (signal acquisition module 204), 2.9 (signal acquisition module 206), 2.10 (signal acquisition module 208), 2.11 (signal acquisition module 204), 2.12 (signal acquisition module 206), 2.13 (signal acquisition module 208), 2.14 (signal acquisition module 208), 2.15 (signal acquisition module 208), 2.16 (signal acquisition module 208), 2.17 (signal acquisition module 208), 2.18 (signal acquisition module 208), 2.19 (signal acquisition module 2 2.3 (a processing module 116), 2.3 (a logic algorithm 118), and 2.4 (a trigger generation module 120). 2.1. Synchronization Module 114

[0121] The inputs to the synchronization module 114 include the signals of the 3D spatial position localizer 300, the respiratory phase sensor 110, and the cardiac phase sensor 112. The synchronization module 114 synchronizes these inputs with the clock of the X-ray unit 108. The physiological bio-signals are collected asynchronously and require subsequent synchronization. The reason for the asynchronous nature is that the respiratory cycle is slower than the cardiac cycle and is completely independent of it. As mentioned above, the respiratory cycle and the cardiac cycle are measured separately by different sensors. The synchronization module 114 is configured to synchronize the respiratory phase and cardiac phase sensors with the acquired images. When an image is taken, the image shows the anatomical shape of the chest at a certain point in time, which is recorded by the intrinsic timing system of the X-ray unit 108 and is not necessarily synchronized with the bio-time series of the respiratory and cardiac sensors.

[0122] Time alone cannot distinguish between periods of irregular breathing and periods of normal breathing. In other words, if time were the only dimension defining a respiratory phase, images taken during normal breathing and abnormal breathing (e.g., coughing) would be computationally indistinguishable. FIG. 4 is an example of two breaths 400 (e.g., 400a and 400b) that share the same maximum inspiratory phase in time, but have radically different tidal volumes, according to some disclosed embodiments. When overlaid, it can be seen that the two breaths 400 are in fact different, despite their similar maximum inspiratory phase, because the tidal volumes are different in size. In some embodiments, the tidal volume refers to a measured volume value (e.g., in ml) relative to a baseline volume value. For example, the baseline volume value corresponds to the minimum volume value (e.g., maximum expiratory phase) of the lungs when the patient is breathing normally (e.g., without extra effort or forced exhalation). In some embodiments, the baseline volume value is different for each patient. In some embodiments, the baseline volume value is zero. In some embodiments, the tidal volume is measured at a point in time. The change in tidal volume over time is shown graphically in FIG.

[0123] To overcome the time dimension problem, the GREX imaging system 100 defines the respiratory phases by physiological values ​​obtained from various physiological sensors in the hardware box 102, which is a more useful respiratory phase dimension than time. The function of the synchronization module 114 is mainly to ensure a seamless transition between the X-ray unit 108 and the GREX imaging system 100. In some embodiments, the acquisition software 104 uses a 30 second training window (described in detail in 2.2 below) to calculate the tidal volume percentiles from the tidal volume time series observations collected during the training window. The acquisition software 104 defines the respiratory phases using the tidal volume percentiles rather than the peak-to-peak periods of the periodic cosine waves. The acquisition software 104 tidal volume percentiles are much more useful for defining the lung shape than the peak-to-peak periods of the periodic cosine waves, due to the variation of tidal volume from breath to breath.

[0124] FIG. 5 is an example of tidal volume percentiles (bottom two subplots) for a patient breathing normally (top left subplot) and for a patient breathing irregularly (top right subplot), according to some embodiments of the present disclosure. For quantitative assessment of tidal volume histograms for irregular breathing, the ratio of tidal volume during normal inspiration to tidal volume during abnormal inspiration is used as an index of respiratory phase. The ratio has a threshold value that defines the probability of irregular breathing of the patient. As shown in the bottom two subplots of FIG. 5, vertical lines indicate the location of 85th, 90th, 95th, and 98th percentile tidal volumes in the tidal volume histogram. During normal breathing (bottom left subplot of FIG. 5), normal tidal volume percentiles (85 and 90) are significantly higher than abnormal tidal volume percentiles (95 and 98) compared to irregular breathing (bottom right subplot of FIG. 5). exists nearby.

[0125] FIG. 6 is a schematic flow chart illustrating a synchronization process 600 between the various sensors of the hardware box 102 and the diagnostic X-ray unit 108 according to some embodiments of the present disclosure. The hardware box 102 continuously collects respiratory phases defined by tidal volume percentiles, cardiac phases defined by ECG, and chest geometry in coordinates defined by the 3D spatial position localizer 300. The sensor signals on which these measurements are based must first be synchronized with each other before being synchronized with the X-ray unit 108. This is achieved by compensating for differences in the measurement channels of the various sensors and differences in electrical resistivity of the cables by matching trace lengths using impedance matching of the digital-to-analog converters. As the clock (e.g., timing system) of the X-ray unit 108 is typically not synchronized with the clock of the hardware box 102, a connecting cable is configured to connect to a data acquisition board and interface the board with the clock of the X-ray unit 108. The clock of the X-ray unit 108 passes through a distribution buffer and is trace matched for each channel of the analog-to-digital converter. The converted digital signals are sent to a field programmable gate array where all signals are synchronized. Once the acquisition software 104 has properly synchronized the respiratory and cardiac phase sensor signals, the result will be similar to the example shown in FIG.

[0126] To avoid unnecessary radiation exposure to the patient, the acquisition software 104 will not send a trigger signal to activate the X-ray unit 108 via the connecting cable unless the components of the GREX imaging system 100 are properly synchronized. In some embodiments, a 30-second training window is used to verify the synchronization of the clocks of the hardware box 102 and the X-ray unit 108. Thus, the 30-second training window must include 30 seconds worth of samples. If the clocks of all the sensors of the hardware box 102 and the X-ray unit 108 do not show exactly 30 seconds worth of samples, they are not synchronized. If the above verification procedure reveals a discrepancy, the synchronization system will be restarted to correct the discrepancy. However, the 30-second training window is merely an example, and one skilled in the art will understand that the length of the training window is not critical as long as there is enough data to perform the synchronization process. 2.2. Signal Processing Module 116

[0127] After the spatial location, respiratory period, and cardiac phase signals are synchronized, the acquisition software 104 processes the sensor signals to remove noise and finally estimate the patient's tidal volume with high accuracy. The noise present in the lung and heart measurement time series is due to the sensor electronics, electrodes, and background electrical signals. A series of filters to ensure temporal accuracy removes noise from the lung and heart measurement time series. The biological time series filtered by the series of filters can maintain temporal accuracy.

[0128] In some embodiments, two separate filters (e.g., wavelet filters) are used to remove signal drift and noise from the biological time series. Signal drift distorts measurements over time, such that measurements at the beginning of data collection do not match measurements at the end of data collection. Signal noise is not physiological in nature and can be a serious problem when calculating patient airflow from tidal volume measurements. FIG. 8 is a schematic flow chart of a drift and signal noise removal process 800 according to some embodiments of the present disclosure.

[0129] The acquisition software 104 requires a smooth tidal volume time series to calculate the first time derivative of tidal volume, e.g., airflow. If the tidal volume time series is not smooth, the first time derivative of tidal volume cannot generate a smooth curve. The curve will contain discontinuities that violate the actual biophysical reality of breathing. FIG. 9 shows a schematic diagram of a tidal volume time series according to some embodiments of the present disclosure. 10 is a plot of airflow versus tidal volume for the same breath as illustrated in FIG 9, a continuous closed loop used by the post-processing software 106 for biomechanical modeling (see 3.1) according to some embodiments of the present disclosure.

[0130] The acquisition software 104 uses the filtered, time-accurate time series curve to perform two different functions. The first function of the acquisition software 104 is to generate respiratory phase predictions using a short prediction horizon. FIG. 11 is a schematic flow chart of a respiratory phase prediction process 1100 according to some embodiments of the present disclosure. The short prediction horizon is the "look ahead" of the prediction algorithm. The prediction algorithm predicts the future moment when the desired tidal volume and airflow (e.g., respiratory phase) will occur. The "desired" respiratory phase is the target that can trigger the diagnostic x-ray unit 108 to obtain the desired chest shape.

[0131] The short prediction horizon mitigates further sources of inaccuracy in respiratory prediction, such as breath-to-breath variations in respiratory amplitude and duration. As the limits of the prediction horizon approach zero, the lung shape change also approaches zero (e.g., the lung shape becomes substantially constant). In other words, the lung shape is unlikely to change dramatically during the short prediction horizon. Thus, a short prediction horizon mitigates the impact of human respiratory variability on the prediction accuracy of the respiratory motion model.

[0132] The time-accurate filtered tidal volume time series serves as input for a respiratory prediction algorithm. The respiratory prediction algorithm allows for fast, real-time, and accurate prediction of respiratory phases. For example, the respiratory prediction algorithm is based on an autoregressive integrated moving average (ARIMA) model. The ARIMA model does not assume that the inputs are stationary and is composed of polynomials, making it suitable for respiratory prediction. The polynomial coefficients of the ARIMA model are estimated during a 30-second training window starting at the beginning of the imaging examination. The number of polynomial coefficients of the ARIMA model, i.e., the order of the model, is verified by nonlinear optimization to minimize an information criterion search function to reduce or eliminate overfitting. If the order of the model is optimal for the collected training data, a tidal volume histogram (see 2.1) is created and a probability density function is calculated using the log-likelihood objective function. As explained in 2.1, the distribution of tidal volumes is intended to capture irregular breathing. If irregular breathing is detected, the training data is discarded and reacquired. If irregular breathing is not detected, the ARIMA model coefficients are estimated by maximum likelihood using the training data and the probability density function. The 30-second training window also serves as a pre-imaging device validation. Figure 11 shows the flow chart of the respiratory phase prediction process.

[0133] The second function of the acquisition software 104 is to identify the heartbeat phase so that the heart is in phase at each desired chest shape. The duration of the prediction range serves as an important parameter in the acquisition software 104's efforts to accurately predict human respiration since human respiration is a quasi-random function (each breath has its own profile). In some embodiments, the duration of the prediction range is longer than the sum of the latency of the digital diagnostic x-ray unit 108 and the exposure time of the x-ray image. The sum of the latency of the digital diagnostic x-ray unit 108 and the exposure time of the x-ray image is very short, on the order of 10 milliseconds. As a result, the prediction period is also short (on the order of 1-2 sensor measurement sample values ​​at an operating frequency of 100-1000 Hz).

[0134] When the acquisition software 104 looks for coincidences between cardiac and respiratory phases, it is unlikely that the phases represented by a single point in each time series will line up. As a result, imaging methods that look for single point coincidences take longer to complete because the acquisition software 104 must wait to find a highly unlikely match. However, the cardiac gating window expands the size of the coincidence window, so imaging takes less time.

[0135] To further reduce the computation time of the signal processing software, the signal processing software does not predict the cardiac phase. Instead, the signal processing software targets a specific gating window where the heart does not cause rapid displacement of the lungs. FIG. 12 is an example of an in-time gating window selected based on the position of the T and P waves of an ECG signal such that the cardiac-induced lung motion changes slowly, according to some embodiments of the present disclosure. The dotted line indicates the lung motion due to the cardiac beat. The rate of change (e.g., velocity) of the lung motion is represented by the slope of the dotted line. If the slope of the dotted line is small, the rate of change is small and the corresponding cardiac phase is the ideal gating window. FIG. 12 shows that the gating window occurs (consistently) between the T and P waves and is more skewed toward the P waves. The lung motion due to the cardiac beat occurs minimally in the gating window.

[0136] The above describes a method to identify an ideal cardiac gating window that minimizes the physical impact of the heart on the lungs while maintaining a window in which the target respiratory phase can coincide with the desired cardiac phase. In some embodiments, the GREX imaging system 100 predicts the cardiac phase instead of gating it based on signal processing differences that distinguish the cardiac phase from the respiratory phase (e.g., the cardiac phase is periodic and stable). Because the cardiac phase is periodic and stable, an unsupervised multi-layer perceptron with backpropagation can predict the next heartbeat based on pattern extraction instead of a time series prediction process.

[0137] 13 is a schematic flow diagram of another GREX process 1300 using cardiac phase prediction instead of real-time identification of cardiac gate window, according to some embodiments of the present disclosure. In this case, 20 seconds (heart rates 20-22) of the training window are used to train the algorithm, and the remaining 10 seconds (heart rates 10-11) are used to validate the multi-layer perceptron node weights. The node weights are determined iteratively by gradient descent optimization until the model error on the training set is minimized. The trained model is applied to 10 seconds of validation data, and if the multi-layer perceptron's cardiac phase prediction on the validation data is poor, the node weights are recalculated. 2.3. Logical Algorithms 118

[0138] Medical algorithms and systems already exist to identify the ECG T and P waves in the cardiac cycle. Because the cardiac phase sensor 112 continuously measures the cardiac cycle, the time interval between the T wave and the following P wave (equal to a certain percentage of the cardiac cycle and therefore proportional to the heart rate) is also known. The logic algorithm 118 uses ECG features within the time interval to introduce a short time lag before starting the gating window, such that the gating window starts, for example, halfway between the T and P waves and closes after the logic algorithm 118 identifies the P wave.

[0139] FIG. 14 is an example of triggered x-ray image frames corresponding to different respiratory phases of a respiratory cycle, such as early expiratory phase, late expiratory phase, maximum expiratory phase, early inspiratory phase, late inspiratory phase, and maximum inspiratory phase (from left to right), according to some embodiments of the present disclosure. In this case, the acquisition software 104 identifies at least six respiratory phases that represent a single respiratory cycle. During a training period, the acquisition software 104 generates a distribution of samples, and the logic algorithm 118 calculates tidal volume percentiles that define the target respiratory phase of the logic algorithm 118. The arrows shown in FIG. 14 point to the six cardiac gated frames that coincide with the target respiratory phases for which the logic algorithm 118 should generate an imaging trigger signal. Once the acquisition software 104 has acquired a respiratory phase, it generates an automatic verification to prevent duplicate imaging of the same respiratory phase.

[0140] FIG. 15 is a schematic flow chart of an image capture trigger process 1500 according to some embodiments of the present disclosure. The cardiac phase sensor measurements are used to identify the cardiac gate window as described above with reference to FIG. 12. The respiratory phase sensor 110 measurements are used to predict the respiratory phase as described above with reference to FIG. 11. A logic algorithm 118 determines the cardiac phase gate window. The system then identifies a match between the route window and the predicted respiratory period. If a match is found, it examines the list of respiratory periods to determine if the respiratory period has already been acquired. If the respiratory period has been previously acquired, it does not generate an imaging trigger pulse. If the respiratory period has not yet been acquired, it generates an imaging trigger pulse and acquires a snapshot of the patient's anatomy. The respiratory periods, cardiac phase, and 3D spatial position localizer 300 measurements are recorded and tagged with the image. Once all respiratory periods have been acquired, the respiratory period list is updated to prevent duplicate image capture. 2.4. Trigger Generation (Gate Control)

[0141] The X-ray unit 108 has a port that contains a series of electrical pins. One of the pins accepts an electrical impulse that defines the timing and duration of radiation exposure. Based on the identified respiratory phase of the logic algorithm 118 within the cardiac gate window, a trigger generator generates a square wave trigger as an electrical impulse. A fiber optic cable with a vendor-specific mating plug transmits the generated trigger signal to the X-ray unit 108. Chapter 3. Post-processing software

[0142] The biometric imaging triggers identified by the hardware box 102 (Chapter 1) and acquisition software 104 (Section 2) provide higher quality data inputs (and filter out lower quality inputs) to the image reconstruction algorithms. Specifically, the biometric targeted image acquisition process during normal breathing improves image reconstruction and image post-processing techniques, thereby improving quality. Because a basic assumption of the underlying radiology mathematics presupposes anatomical equivalence at various probe imaging angles, the biometric targeted image acquisition process during normal breathing allows for more accurate correlation of multiple images of anatomical geometry taken from different angles at different times (e.g., different breaths) for the same patient. The improved quality images serve as observations, and the biometric signals serve as inputs to a complex biomechanical model of chest geometry. 3.1. GREX image acquisition using digital tomosynthesis

[0143] For 3D reconstruction, multiple imaging angles are required. For GREX imaging, each angle must be acquired for each respiratory phase. The acquisition software 104 (Chapter 2) creates trigger signals that allow the X-ray unit 108 to repeatedly image the chest at a particular geometry. The same geometry imaged at different imaging angles and different breaths results in a set of 2D projection images that are used to reconstruct the 3D. Many methods are already known for reconstructing 3D from multiple 2D projection images. An example is the convolution backprojection algorithm, which uses a set of 2D projections to directly reconstruct a 3D density function. This is called the "FDK image reconstruction algorithm" and is disclosed in Feldkamp LA, Davis LC, Kress JW, "Practical cone beam algorithm", J Opt Soc Am 1, 612-619 (1984).

[0144] FIG. 16 is a schematic block diagram of variables used in an image reconstruction algorithm according to some embodiments of the present disclosure. To acquire a single projection image, the X-ray unit 108 moves through an angle β and the detector plane moves to maintain a perpendicularity to the X-ray unit 108. In some embodiments, the patient is moved and the X-ray unit 108 remains in the same position during the projection image acquisition. FIG. 17 is a schematic diagram illustrating an example of a process in which the X-ray unit 108 and the detector panel 304 of FIG. 3 move to obtain multiple imaging angles. For example, the X-ray unit 108 moves from position 1700a to position 1700b to position 1700c. The detector panel 304 moves from position 1702a to position 1702b to position 1702c. The detector plane rotates about an axis parallel to the detector plane and the image plane rotates about its parallel axis z. The positions of pixels in the detector plane and corresponding pixels in the image plane are separated by a distance s. Anatomical information (f(x, z / y)) of the imaging plane ((x, z) plane) at any depth y is calculated using the following equation (1).

number

[0145] In formula (1), N O where is the total number of projection images, β is the angle of each projection image, d is the distance from the source to the image plane, s is the distance from the pixel to the detector, p is the detector axis perpendicular to the rotation axis, ξ is the detector axis parallel to the rotation axis, R(β,p,ξ) corresponds to the cone beam projection data (e.g., function R is the detector reading for a particular angle, p and ξ are coordinates), h is the convolution filter, and W(p) is the weighting function. Essentially, equation (1) represents a combination of convolution, backprojection, and weighting.

[0146] The information of points lying in the mid-plane can be calculated from the projection data along the intersection of the detector plane with the mid-plane (y=0). Projections (constant, non-zero y) that intersect the detector plane along a line parallel to the mid-plane but are not in the mid-plane define a plane of their own. The plane is treated as another tilted mid-plane. Once a complete set of projections has been acquired ("complete" meaning that all rotation angles around the normal have been acquired), the density of the tilted plane is reconstructed using the Radon transform. To acquire a complete set of projections, the light source needs to be rotated 360° around the object along the tilted circle. For example, in CT imaging, a full 360° rotation is performed around the object. The bolded "object" implies (or, more precisely, states) that violation of the basic assumption of the Radon transform (also combined in the R(β,p,ξ) term in Eq. (1)), i.e., considering the Radon transform inputs as time- and space-invariant, varying angle probes for a stationary object with a single fixed, non-moving anatomical structure, compromises the representativeness of the ground truth data in the reconstruction of the anatomical structure on which the imaging is based. GREX imaging subjects only accurate (occurring in different breaths, but with geometrically identical respiratory phases) Radon transform inputs to biodata-based targeting (i.e., "preselection"). Here, the respiratory phases (i.e., time-domain changes) used in GREX are geometrically, anatomically, and physically rigorously defined (i.e., definitionally correct formulation) and in fact uses the short prediction horizon and biodata-based prospective targeting specific to GREX imaging, so that the basic assumption of the transform is definitionally and practically adhered to.

[0147] However, 360° rotation is not practical for GREX imaging because the large number of projections required for 3D reconstruction of the torso increases the clinical procedure time and increases the patient's radiation dose. Instead, GREX imaging uses a projection angle range of -45°≦β≦45° or 0°≦β<90°, with a maximum of 90°. Those skilled in the art should understand that a more optimal projection angle range may be identified through experimental testing, but the theoretical projection angle range does not exceed 90°. In some cases, -45°≦β≦45° is preferred over 0°≦β<90° in order to keep the radiation dose of the -45°≦β≦45° imaging procedure as low as possible. X-ray photons penetrate human tissue less frequently in the projection angle range of -45°≦β≦45° than in the projection angle range of 0°≦β<90°, which results in lower photon energy and lower radiation dose, as also shown in Figures 18A and 18B.

[0148] To produce high quality 2D images without excessive radiation dose, the photon energy must be high enough to partially penetrate the patient's body, but not so high that it penetrates the patient completely. Patients with a thicker body require higher photon energy than those without. For |β|>45°, the body is much thicker than for |β|<45°. In general, as β→0°, the photon energy decreases. In some embodiments, the GREX imaging system 100 acquires six respiratory periods at five different projection angles for a total of 30 projections, although the number of projections may vary depending on the application of the GREX imaging technique. For example, in breast tomosynthesis, the symmetric curvature of the breast means that the breast surface is essentially equidistant from the light source at all projection angles. This means that tomosynthesis is well suited for breasts. Furthermore, the breast does not move when placed in the cradle, making this a classic example of clinical tomosynthesis similar to breath-hold lung imaging.

[0149] Since the challenges of lung and heart motion can be solved by the GREX-based geometric definition of the respiratory phase as well as the GREX-based highly accurate and fast probabilistic targeting prediction algorithms (2.1 and 2.2), the GREX imaging system 100 can handle the changing curvature of the torso using the biosurface information collected by the 3D spatial position localizer (1.1). The biosurface information assists in post-processing of the images to address the attenuation of the tissue density in the imaging field, thereby quantifying previously ignored sources of attenuation and ultimately achieving a more faithful image reconstruction. In summary, the GREX imaging technology allows imaging of the lungs and heart without breath-holding ("dynamic") by digital tomosynthesis.

[0150] GREX-based 3D reconstructions may be patient-specific (custom medicine using individualized angles and corresponding arcs) or used as a "universal minimum procedure time and universal minimum delivered dose" (taking into account past reconstruction method statistics and the number of previous GREX-based datasets for the patient at approximately 5 discrete angles ±3 angles).

[0151] As shown in Figure 19, when the X-ray unit 108 rotates through a narrow range of angles, the depth resolution is lower than when the X-ray unit 108 rotates through a wider range of angles. Any motion, regardless of magnitude, will cause image artifacts in the reconstructed image and will typically result in false positive cancer detections. However, the unique hardware box 102 (Chapter 1) of the GREX imaging system 100 collects biosignals that inform the acquisition software 104 (Chapter 2) when to take images so that the breast geometry is clinically identical, thereby enabling smart breast digital tomosynthesis.

[0152] In some embodiments, the GREX imaging technique allows for varying projection angles during one respiratory phase, but multiple projection angles captured at different times (as defined by the quantitative definition of a "respiratory phase" in GREX imaging) all correspond to the same respiratory phase, i.e., all are considered to be imaging one chest shape. Furthermore, with GREX-based tomosynthesis, depth information can be obtained because the final photon counts of the detector across all pixels and the distribution of photon counts in the space of the detector plane reflect a single chest shape probed at multiple angles.

[0153] In the GREX imaging technique, the x-ray unit 108 and detector panel 304 may also be mounted on a non-motorized arm or stand. An example of a manual arm in a GREX imaging procedure is illustrated in FIG. 1) In the β1 position, all six essential chest geometries ("GREX quantitatively defined respiratory phases") are imaged. 2) The clinician physically reorients the x-ray unit 108 and detector panel 304 to image the patient at the β2 position. 3) The orientation of the X-ray unit 108 and detector panel 304 relative to the patient is verified with a 3D spatial localizer so that it can be imaged at β2. 4) If in β2, only four of the six required chest geometry images are acquired during the first breath, the patient can continue to breathe normally in the β2 position until the remaining two chest geometry images are acquired. 5) Now that all six respiratory shapes have been acquired at β1 and β2, reposition the X-ray arm and obtain each imaging angle in the order (β2→β3; β3→β4; β4→β5). 6) As indicated by the → arrows in the workflow, the X-ray arm moves a total of only four times until the end of the procedure (β1 → β2; β2 → β3; β3 → β4; β4 → β5).

[0154] With the GREX imaging technique, the arm is moved only four times during the procedure, and the clinician only interacts with the X-ray arm to the extent of moving the arm from β1 to β5 over six respiratory phases (e.g., reorienting the X-ray machine 30 times), minimizing procedure time, clinician-equipment interaction during the procedure, and the degree of wear on the X-ray arm.

[0155] In some embodiments, the 3D spatial positioning localizer allows for pre-position verification and safety interlocking of three individual elements, i.e. patient pose / position, X-ray unit 108 position, detector position in space, and collective consistency in the position / location of each element with respect to the axes and other element positions. Pre-position verification and safety interlocking are also possible because GREX imaging defines the respiratory phases geometrically. Thus, the GREX imaging system 100 (prospective respiratory phase prediction algorithm) can provide the user with a software-based safety and quality assurance control function that prevents the "beam on" initiation of the trigger algorithm (in case of safety interlock) even if either the X-ray arm or the detector (or both) or the patient pose is mispositioned (or inconsistent) in space with respect to a certain angle β.

[0156] In some embodiments, a 3D spatial positioning localizer records all of the instrument's coordinates during the procedure, so that image reconstruction techniques have the advantage of allowing (post-hoc) quantification of each β angle and its associated uncertainty.

[0157] FIG. 21 is an example block diagram illustrating how the GREX imaging system 100 reconstructs a still image cube from 2D projection image data taken from each imaging angle position, according to some embodiments of the present disclosure. Specifically, FIG. 21 illustrates an example of GREX imaging where the coronal and sagittal planes themselves form the outer limits / boundaries of the imaging angle positions (e.g., projection angles). The x-ray projection images of the ith plane are calculated for each of the six respiratory shapes at a single imaging angle position (β i). The sum of the planar projections of i = 1 to i = n reconstructs (Eq. 1) the depth information at (x, y, z) of the (v1, f1) shape probed with the projections of i = 1 to i = n, each with a focal plane at a unique depth (relative to the other projections).

[0158] 2D projection image data is acquired six times at each projection angle (s) (early inspiration (EI), late inspiration (LEI)). Inspiration (LI), Maximum Inspiration (MI), Early Exhalation (EE), Late Exhalation (LE), Maximum Exhalation (ME). Briefly, the projection angles range from 0° to 90°. The X-ray unit 108 moves to the next projection angle only when all respiratory phases have been acquired at the previous projection angle. The 2D projections are classified according to the respiratory phase, so that a still image cube is reconstructed (e.g., β, which probes the chest shape at (v1, f1)). i =1 to β i = n projection angles), representing the volume of the thorax at each respiratory phase. The still image cubes are temporally interpolated using the method described in 3.3 below.

[0159] The x-ray projection images acquired at each angle β are also acquired at the target respiratory phase identified using the ARIMA model (2.2). After the target respiratory phase is acquired, the x-ray unit 108 moves to the next imaging angle position. FIG. 22 illustrates an example where only the maximum inspiratory and expiratory phases are acquired by the acquisition software 104. In this example, the maximum inspiratory and expiratory phases are imaged at β1, and then the x-ray unit 108 moves to β2 and acquires the maximum inspiratory and expiratory phases at β2 as well. Once the target respiratory phases are acquired at all imaging angle positions, the images are classified according to their respective respiratory phases. The x-ray images are sorted according to the respiratory phases, and the projection images are grouped according to the six biologically defined respiratory phases. Although the respiratory phases are acquired during different breaths, the accurate ARIMA model ensures that the tidal volume and airflow parameters are the same even for x-ray projection images taken at different projection angles. GREX imaging biologically defines the "same chest shape" so that the lungs are present in the "same chest shape" at multiple time points. The ARIMA model (2.2) is a fast prediction method, so GREX imaging probes the same biorespiratory phase at different angles. Chest shape prediction error is minimized using the short prediction horizon of the ARIMA model. The associative equality of the depth information of the individual angles (to collectively produce the actual structural depth information) relies on the equality (within a consistent, approximate tolerance) of the geometrical position of the chest across the various probe angles. Thus, the accuracy of the respiratory phase prediction ensures successful image reconstruction.

[0160] The respiratory phase classified projection images are used to create static image cubes using the standard FDK image reconstruction algorithm given in Equation (1) (or a similar cone beam shape image reconstruction algorithm). The image reconstruction algorithm uses the respiratory phase classified projection images to create static image cubes classified by respiratory phase. Each respiratory phase has a separate image cube. The static image cubes are called static cubes because they represent an anatomical structure at a single respiratory phase. A 3D image cube movie is created from the 3D static image cubes by combining and temporally interpolating (see 3.3) the static cubes representing all the targeted respiratory phases.

[0161] The GREX imaging system 100 keeps radiation dose at the lowest reasonably achievable level through statistical image reconstruction. Each image acquired increases the dose of the entire imaging process (clinically undesirable) but provides additional information that is used in image reconstruction (clinically desirable). Conventional image reconstruction based on Fourier transform or filtered backprojection techniques are prone to image artifacts because they cannot handle missing information, e.g., missing projection angles β. For example, if projections are made every 10° instead of every 5°, half the information is available to create a still image cube, and roughly half the dose is used. Statistical iterative image reconstruction techniques can also handle missing information caused by incomplete image data sets.

[0162] The GREX imaging system 100 may use various existing statistical image reconstruction algorithms to complete the image reconstruction task (e.g., construct a still image cube), while the GREX imaging system 100 can improve upon conventional statistical image reconstruction algorithms by implementing a unique feedback step and adhering to mass conservation-based boundary conditions.

[0163] Since GREX images are biophysically defined by biophysical quantities governed by physical laws, fundamental principles of physics can be applied to the statistical image reconstruction of GREX. By biotagging each image and the resulting image cube, and further collecting a continuous biodata stream during the procedure even when the patient is not being imaged, the respiratory dynamics of mass exchange (inspiration-expiration) and volume change are known (solving the problem of an invariant lung tissue volume that is consistent over the course of the scan). Since still image cubes can be constructed for the first time from moving organs such as the lungs, the law of conservation of mass can be applied. This is due to the speed and accuracy of the prospective prediction / triggering algorithms (2.2 and 2.3) that accurately label and obtain the same chest shape at different times. In other words, the mass of tissue in a still image cube must not change (e.g., by the law of conservation of mass) from any still image cube to the next still image cube. Based on the ideal gas law, the ratio of air at room temperature to the air in the lungs is 1.11. Obtaining the tidal volume of the image cube from the sensor data and using the mass / volume air curve with a ratio of 1.11 and deviation relative to room temperature, the inspired mass (absolute value as well as relative value as a ratio of the image cubes of different respiratory phases) can be determined.

[0164] The mass conservation-based boundary conditions of GREX imaging are extremely useful, as, for example, the presence of air can artificially darken voxels and adversely affect the ability of statistical image registration algorithms to accurately determine the density of objects. By correcting acquired projections for differences in air volume and consistently isolating volumes that need to remain constant over the course of a scan (e.g., the mass of lung tissue), GREX imaging eliminates the need for a single boundary condition used to generate the static image cube. This produces a more accurate image reconstruction than is possible with conventional methods.

[0165] Considering two GREX projections taken at different imaging angles and the same anatomically defined respiratory phase, the lung air volume is the same, but the way the air displaces the tissue may be different in the two projections, which causes the affected and erroneous nodule to have a darker intensity in the second projection compared to the first projection. This error results in a part of the tissue (the "nodule") that was visible in the first projection not being visible in the second projection, and finally in the resulting image cube the "nodule" appears dull (or is mistaken for background). The mass conservation-based boundary condition of the GREX imaging is realized as a feedback step that ensures that the lung mass is preserved between the aforementioned image cube with the dim nodule and another image cube (with a correct anatomical representation of the "nodule" intensity) from a later respiratory phase. The feedback step of the GREX imaging corrects the second projection of the erroneous image cube during reconstruction by updating the expected shape based on a simulation using the first projection as an absolute reference. This allows the reconstruction of the GREX statistical image to generate a more accurate still image cube.

[0166] In addition to existing statistical image reconstruction algorithms, the GREX post-processing software 106 improves anatomical imaging by incorporating edge definition filters (see 3.2), spatial boundary conditions (see 3.2) and smooth transitions between respiratory phases (described in section 3.3) into the digital diagnostic X-ray images. 3.2.Image Filters

[0167] The quality of a digital diagnostic X-ray image depends on the settings of the X-ray unit 108 and the anatomical region being imaged. Each patient and anatomical region has a different electron density through which X-rays penetrate to produce an image. For example, imaging the femur requires more X-ray energy than imaging the chest because the lungs are primarily air and the femur is composed of bone. Given that high-energy X-rays penetrate the body more than low-energy X-rays, the number of X-rays that emanate from the body to reach the flat panel detector differs for high-energy X-rays and low-energy X-rays imaging the same anatomical shape. Too many X-rays emanate from the body, resulting in overexposure of the flat panel detector, similar to overexposure in optical photography. If the settings of the X-ray unit 108 are not optimal for the anatomical region being imaged, image quality will be significantly degraded. In clinical practice, commercial vendors devise imaging protocols for their digital diagnostic X-ray units that roughly estimate the optimal X-ray unit settings for a selected anatomical region. However, the estimation of optimal tube settings has the disadvantage that it is not customized to address potentially significant anatomical variations in each patient's anatomical region (e.g., the stomach of an obese man is different from that of an average weight man). In fact, existing imaging protocol settings by vendors are only rough estimates and are not suitable for producing high-quality images.

[0168] When optimal x-ray unit settings and other imaging parameters are unknown prior to imaging (as is the case in today's medical community), digital image filters can be strategically utilized to improve image quality even when x-ray unit settings are not optimal. The improved image enhances the visibility of anatomical features that are largely invisible to the human eye. For example, in the coronal plane, not all ribs may be visible in a digital diagnostic x-ray image. The post-processing software 106 filters the coronal image with an edge enhancement filter, such as a Laplacian filter, to display all rib boundaries in the resulting image, even when the rib boundaries in the original image are not visible to the human (e.g., radiologist) eye. The post-processing software 106 overlays the filtered image with the original image, highlighting previously invisible rib edges that have been enhanced (e.g., after filtering) in the original image. Image filters that the user can apply include Laplacian filters, Hanning filters, Butterworth filters, Parzen filters, and Wiener filters. These include the Kerner filter, Metz filter, Ramp filter, nonlinear spatial average filter, and hybrid filter.

[0169] In some embodiments, the post-processing software 106 uses the skin surface measurements of the 3D spatial position localizer 300 to calculate optimal imaging parameters for images taken at each respiratory phase. As the patient breathes, the electron density of the body changes as more air is inhaled and chest circumference increases. As the patient's diameter increases, the distance between the patient and the x-ray unit 108 decreases, and the distance between the patient and the detector panel 304 also decreases, introducing additional image noise into the resulting x-ray image. The 3D spatial position localizer 300 tracks the patient's skin surface position in each image relative to the x-ray unit 108 and the detector panel 304. Tracking the skin surface position can provide unique measurements used in digital diagnostic x-ray examinations.

[0170] Current digital diagnostic radiology consists of scaling the dose index readings measured with an ionization chamber to the approximate body mass index of the patient. Radiologists currently perform only two measurements: one using a solid water cylindrical phantom with a diameter of 16 cm, which corresponds to the tissue density, and another using a substantially identical phantom with a diameter of 32 cm. The X-ray unit 108 includes "one size fits all" protocols for various anatomical locations, as defined by the vendor. For example, the vendor provides only a single protocol that includes image settings that the radiologist can select, regardless of the diameter of the patient's chest. This means that images are acquired with similar imaging settings for men with and without thick chests.

[0171] The 3D spatial position localizer 300 of the GREX imaging system 100 generates individual measurements of the patient's chest diameter in real time. The measurement is started when the technician selects the patient's customized X-ray unit settings. When the patient breathes, the chest diameter changes. If the patient's chest diameter changes, the technician will not be able to set the optimal imaging parameters to match the patient's chest diameter. On the other hand, with the real-time customized chest diameter measurements, it is possible to remove image noise in post-processing and simulate an X-ray image taken with optimal imaging parameters.

[0172] FIG. 23 is a schematic flow chart of an image filtering process 2300 for computing a noise-free lung image to simulate an image taken with optimal x-ray unit settings, according to some embodiments of the present disclosure. After acquiring the image as described in Chapter 2, the trachea is identified using a line profile of a segment passing laterally (left to right) through the neck region. While the neck is composed of muscles, bones, and arteries, the trachea only contains air, which is significantly less dense than tissue, making it stand out from all other tissues. The line segment indicates the location of air, and small regions containing pixels assigned to air are identified. The image noise of the x-ray image is calculated by dividing the entire image into small patches. Gaussian noise is estimated separately for each patch, and the patch with the least amount of noise is used for texture mapping. The texture mapping method uses the gradient covariance matrix to estimate the initial texture level for each patch. For the patch with the least initial noise level, the noise level is re-estimated in an iterative process that continues until the noise estimate for the patch converges with additional iterations of the gradient covariance matrix. The weak texture patches are assumed to be in air. Air is likely to be present in locations far from the patient, e.g., the top corners of an x-ray image. Estimating the noise level of the patch gives a baseline noise level for the entire image. The baseline noise level identified by the iterative gradient covariance matrix is ​​then subtracted from the entire image to obtain an estimate of the noise-free air density in the trachea.

[0173] The post-processing software 106 performs 3D spatial localization to estimate the initial position of the lungs. A human skeletal model (individually scaled for each patient) is overlaid on the surface location estimates provided by the imager 300. The individually scaled patient skeleton is rigidly registered to the x-ray image using visible landmarks present on the skin surface (e.g., clavicle, rotator cuff, scapula, vertebrae). Rigid registration of the skeleton to the x-ray image reveals the location of the rib cage from the skeleton. The rib cage itself presents boundary conditions for the edges of the lungs and pixel values ​​close to the noise-free air density estimate of the trachea. Both the edges of the lungs (through the location of the rib cage) and pixel values ​​close to the noise-free air density estimate of the trachea are automatically identified by the region formation algorithm as seed locations (locations where the region formation algorithm starts and then grows radially outward). The region formation method is a region-based segmentation method. The segmentation first identifies a set of initial seed points in the image, then examines the neighboring pixels of the initial seed points to determine whether they should be added to the region. This process is repeated in the same manner as in general data clustering algorithms. In other words, the region-forming algorithm uses an initial placement of seed pixels and expands outward using a statistical process that adds "similar" pixels. The region-forming algorithm continues (continues to merge similar pixels) until the identified pixel is statistically different from the merged cluster.

[0174] In practice, in the GREX imaging system, the region forming algorithm "stops" (e.g., detects pixel dissimilarity) at important anatomical landmark interfaces (e.g., lungs surrounded by enhanced pixels of the rib cage). Pixels that the region forming algorithm does not identify as belonging to lung tissue are masked (a mask image is defined as an image that enhances the structure when subtracted from the original image) to form two separate images: (i) the segmented lungs, and (ii) other body tissues. To provide an accurate and noise-free segmented lung volume (e.g., lung volume that is not visually hidden by tissues other than the lungs) that improves the diagnostic visibility of the radiologist, tissues related to the body mask (e.g., not related to the lungs and therefore less visually informative) are removed from the lung image. For example, pixels belonging to the intercostal muscles of each simulated imaging ray projection are completely subtracted from the segmented lung image. The body mask is subtracted as described above from the lung image to improve the visualization of the lung tissue, and the body mask is used to perform a second validation against the patient surface position calculated by the 3D spatial position localizer 300. For example, the post-processing software 106 calculates the number of pixels that the region-forming algorithm identifies as the body mask, and then calculates the body diameter at various locations along the height of the torso. This body diameter calculation should closely match the 3D spatial position localizer 300's estimate of the patient's body diameter. If it does not, this may indicate the need to recalibrate the 3D spatial position localizer 300 to improve accuracy.

[0175] If the clinical user desires a more accurate view of the body mask (e.g., for clinical or educational reasons), the body mask image is simulated with optimal X-ray unit settings to remove noise features and potential sources of artifacts from the body mask. The body mask and the segmented lung are then recombined to form an artifact-free and noise-free X-ray image, further improving overall clinical applications such as structure contouring. 3.3. Biomechanical Modeling

[0176] The biomechanical model used in the post-processing software 106 is based on first principles of physics, namely the law of conservation of mass and the ideal gas law. The goal of the biomechanical model in the post-processing software 106 is to determine biophysical indices that will enhance the clinician's ability to diagnose disease. Biophysical indices of interest include, but are not limited to, stress and strain in lung tissue elements.

[0177] When a force is applied, a mechanical system generates stress. In the case of the lung, the components of the mechanical system The lung tissue that constitutes the lung tissue. The lung tissue visible in medical images consists of parenchyma (including alveolar sacs, alveolar walls, bronchi, and blood vessels). Parenchyma is directly involved in lung function. Tissue elements suitable for biomechanical modeling must be small so as to be internally homogeneous, and statistically stable in response to respiratory stimuli. A typical voxel size in lung medical imaging is 1 mm. 3 ~3mm 3 , which corresponds to 125–375 alveoli. A voxel is considered to have approximately uniform density and to contain enough alveoli to provide a stable response to respiratory stimuli. The alveoli are arranged in a hexagonal array and expand due to the expansion normal stress from each shared alveolar wall. The sum of the expanding normal stresses of the lung tissue elements gives an estimate of the pressure experienced by the alveoli and caused by the respiratory stimuli. The expansion stress is countered by the reaction stress of the alveolar wall, and the two stresses are in equilibrium when the airflow through the tissue element is zero. Any spatial movement of the location of the lung tissue element in any direction due to changes in the lung tidal volume can be modeled by the response of the tissue element to the normal stresses acting on the corresponding surface of the lung tissue element. In other words, the biomechanical model includes a vector term , which describes the response of the lung tissue element to an increase in tidal volume, and the vector term is related to the normal stresses.

[0178] Strain is defined as the response of a mechanical system to a stress. From the perspective of an element (e.g., tissue), stress is a deformation force and strain is a restoring force. The stress vector on the lung tissue surface contains two components: (i) normal stress (associated with the outward or inward motion of the lung tissue elements resulting in expansion or contraction, respectively), and (ii) shear stress, which is orthogonal to the normal stress and results from the airflow-induced pressure imbalance. By definition, the orthogonal component of shear stress does not contribute to changes in lung volume. The relationship between normal stress / shear stress and tidal volume / airflow is illustrated in the "airflow vs. tidal volume" plot shown in Figure 10. Exhalation corresponds to the lowest tidal volume where airflow is zero (left end of the curve). During inhalation, airflow is positive and tidal volume is increasing (top of the curve). As airflow slows, tidal volume is at its maximum. As the tidal volume approaches its maximum (the far right of the curve), airflow decreases rapidly and the air expelled from the lungs begins to increase. As the lungs expel more air, the tidal volume also begins to decrease. Tidal volume decreases when airflow is negative and continues to decrease until the tidal volume reaches residual capacity and airflow becomes zero (e.g., exhalation). In essence, the motion of the lung tissue elements is defined by the tidal volume and the airflow respiratory stimulus. The motion of the lung tissue elements is due to normal and shear stresses acting on the elements. Shape changes of the lung tissue elements (e.g., compression and extension) are modeled by strain, not stress.

[0179] Since medical analysis using models relies on the quantitative analysis of forces, models enable new diagnostic possibilities in the medical field: they allow visualization and analysis of the functional behavior of the lungs (e.g. the movement of the lung tissue) from a clinical perspective, essentially assessing the health of the lungs by the balance of stress and strain forces at each voxel of the lung.

[0180] The first step in building the model is to identify which tissues are part of the lungs and which are not. To this end, the GREX imaging technique takes several snapshots of the chest shape in multiple configurations (six different respiratory phases). The lungs are segmented based on a region-forming segmentation algorithm that uses the air density of the trachea as an initial starting point for the region-forming algorithm. The GREX imaging system 100 registers two deformable images to build a biomechanical model: one acting on the lung tissue and the other acting on the non-lung tissues (e.g. chest wall, ribs, liver, heart, trachea, esophagus, etc.). The results from the region-forming algorithm distinguish between lung and non-lung tissues before the registration of the deformable images.

[0181] The GREX imaging system uses two deformable registrations, one for the lungs and one for other tissues, based on the fact that the motion and material properties of the lungs are different from other tissues. When only one registration is used, the registration assigns a higher weight to other tissues than to lung tissue, i.e., it gives an unrealistically low weight to lung tissue, thus unrealistically limiting the range of motion. The GREX imaging technology was built to visually resolve insidious lung motion, including computationally complex insidious motion at the lung surface.

[0182] Deformable image registration algorithms that can track tissue motion in images include optical flow algorithms. FIG. 27 is a schematic flow diagram of a multi-resolution 3D optical flow algorithm 2700 according to some embodiments of the present disclosure. Specifically, the 3D multi-resolution optical flow algorithm performs deformable image registration that identifies structures in two different images based on the brightness and / or contrast of each structure. Given that non-lung tissues contain anatomical structures (e.g., ribs, pectoral muscles, sternum, etc.) that have brighter (e.g., at least 10 times denser) characteristics than lung tissue, applying a single 3D multi-resolution optical flow algorithm to both non-lung and lung regions would prevent the algorithm from artificially prioritizing the less dense lung tissue. The algorithm's computational resources are preferentially allocated to bright contrast structures (e.g., ribs, etc.) present in the non-lung tissues. Since the computational resource allocation preferentially ignores the dynamics of the lung tissue, it directly violates the purpose of GREX imaging, which is to visually resolve the insidious motion of the lung tissue.

[0183] Since preferential resource allocation to high contrast structures is a feature of the 3D multi-resolution optical flow algorithm, the deformable image registration task of the thorax is separated into two subtasks (e.g., two internally more homogeneous regions): (i) image registration of the lungs and (ii) image registration of non-lung tissues. In some embodiments, in order to analyze two entirely separate working regions from the thorax, identification of the lung surface (e.g., the boundary between the lungs and non-lung tissues) is required prior to 3D multi-resolution optical flow or deformable image registration. The lung surface can be identified with a region formation algorithm that starts inside the lung in the air-filled (e.g., visually dark) region, extends outward toward the lung surface boundary, and encounters high pixel contrast at the lung surface boundary. First, the region formation algorithm is performed.

[0184] The lungs are not attached to the chest wall, so lung motion is relatively independent of chest motion. In other words, rather than a predictable push-pull response at the lung surface boundaries, other tissue dynamics come into play. For example, a horizontally adjacent chest voxel will move vertically downwards, so a lung voxel will move horizontally into the space where the chest voxel previously resided.

[0185] To accurately model the complex motion dynamics, the GREX imaging system 100 quantifies the shear forces acting on the surfactant layer at the lung surface boundary. Two separate segmentations are applied for lung and non-lung tissues to obtain the basis for force estimation. The force estimation procedure can be performed (performed for each reconstructed respiratory phase image) by subtracting (e.g. removing the assigned values) the segmented lung pixels from the X-ray image. The pre-segmented lungs are masked from the original images providing an image containing all other tissues. The segmented chest shape from each image needs to be aligned with the respective chest shapes in the other images so that the position of each lung tissue element is known in all images. A multi-resolution optical flow algorithm performs image registration by calculating a displacement vector field that describes the displacement of all pixels in two images taken at different respiratory phases. Indeed, knowledge of the displacement vector field allows the spatial position of all lung tissue elements in the chest shape to be described with high accuracy. The difference between the displacement vector field of the segmented lung registration and the displacement vector field of non-lung tissues gives the magnitude and direction of the shear forces in the lung and chest wall.

[0186] After observing over 150 patients, we found that the relationship between displacement and tidal volume is linear. The relationship between displacement and airflow is also linear. The output of the multi-resolution optical flow algorithm is a displacement vector in coordinate space for each measured tidal volume and airflow magnitude. When the displacement vector is calculated for every respiratory phase, the result is a closed-loop trajectory (see Figure 10), which corresponds to the observation of the biomechanical model. As explained in more detail below, the parameters of the biomechanical model (which may include parameters representing the normal stress associated with tidal volume, the normal stress associated with airflow, and the shear stress associated with airflow) are obtained by applying a QR decomposition of each lung tissue element individually. The parameters are specific to each lung tissue element (e.g., each lung tissue element has a unique solution) and collectively describe the response of the lung tissue elements to a respiratory stimulus. The parameters of the biomechanical model are vectors that are globally scaled by the measured tidal volume and airflow (e.g., tidal volume and airflow are scalar values). The relationship of biomechanical model parameters, such as the angle between two (vectorized) parameters, is useful for diagnosing lung potential disease. Based on the intrinsic biomechanical model vector parameters of the lung tissue elements (e.g., each piece of tissue has different vector parameters), the displacements of the lung tissue elements are scaled to the chest shape. The chest shape is defined by measurements of tidal volume and airflow. The biomechanical model can be calculated for each patient or shared across multiple patients.

[0187] In some embodiments, the biomechanical model models the motion of the lung tissue in terms of the tidal volume of the lung (T v ), airflow (A f ), heart rate phase (H c ) as functions of several factors including the tissue phase. These values ​​are global values. For example, the cardiac phase is the same for all tissue elements in the thorax. The global values ​​are treated as scalar numbers and are measured by the hardware described in Chapter 1. Tidal volume, airflow, and cardiac phase are all time-varying measurements. The intrinsic stress and strain values ​​for each tissue element are represented by the vector p1 → , p2 → , p3 → , p4 →It is expressed by the following formula using

number

[0188] FIG. 24 is an example of a closed-loop lung tissue motion trajectory of a tissue piece located in the left lung near the heart, caused in part by the heart-lung interaction, according to some embodiments of the present disclosure. The wave motion behavior illustrated in FIG. 24 results from the heart-lung interaction. FIG. 24 is a schematic block diagram illustrating a method for determining the position of a lung tissue piece moving within a closed-loop lung tissue motion trajectory based on the biomechanical model described above, according to some embodiments of the present disclosure. FIG. 24 illustrates a method for summing three vectors of the biomechanical model described in the above equations to calculate the displacement of a single tissue element from the origin to any position on the closed-loop trajectory.

[0189] Using a physiologically based biomechanical model to interpolate images acquired during the respiratory phase has the great advantage of being able to verify the accuracy of the output results of the biomechanical model with quantitative physical quantities. According to the ideal gas law, the ratio of the change in lung volume to the tidal volume is 1.11 at room temperature. In other words, the ratio of the room air density to the lung air density is 1.11. Therefore, the volume integral of the divergence of the normal stress vector must also be 1.11 (e.g.,

number

[0190] An example of how biomechanical modeling of the GREX imaging technique enhances diagnosis is the case of early stage lung tumors that are not visible to the radiologist during imaging examinations. The tumor is much smaller due to the sensitivity of the image, so it is invisible to the radiologist. Even if the tumor is invisible to the radiologist, its presence can affect the balance of forces within the lungs, as its electron density is greater than that of healthy lung tissue. The high electron density of the tumor means that the different material and mechanical properties of the tumor (e.g., different characteristic stress and strain parameters) will affect both the motion of the tumor and the motion of the local area around the tumor (e.g., healthy tissue close to the tumor site). The effect of the tumor on the local healthy lung tissue can be roughly analogized as the effect of mass on the space-time continuum under the theory of general relativity. When a massive object is present, space-time around the object is curved, and light behaves differently around the object than it would if that mass were not present. The same analogy applies to the lungs, where the tumor distorts the trajectory of motion of the adjacent healthy tissue, resulting in a different motion than that of the healthy lung. From the displacement vector map of the biomechanical model, the physician can immediately understand the changes in the composition and biomechanical properties of the lung tissue. In the presence of a pre-defined tumor, the displacement vector field shows unnatural vector curl and other altered characteristics. The creation of the parameter map by the GREX imaging post-processing software 106 (see 3.4) visually displays important diagnostic information that was previously invisible to the user. The GREX imaging parameter map is an example of how the GREX platform is opening new diagnostic possibilities for the medical community.

[0191] FIG. 25 is a schematic flow chart illustrating the operation 2500 of components of a biomechanical model according to some embodiments of the present disclosure.

[0192] The process of the biomechanical model is to biologically interpolate two images (2D or 3D) acquired at different respiratory phases in a quantitative manner. Equation (2) shows that the solution of the biomechanical model is the displacement of the two respiratory phases (U → -U0 → ). As mentioned above, the displacement between the two respiratory phases (U → -U0 → ) is detected by performing deformable image registration to mutually index the two respiratory phases, which typically consists of three steps: · Step 1: Perform region-based segmentation to delineate the structural boundaries between lung and non-lung tissues. · Step 2: Perform intensity-based structure mapping using 3D multi-resolution optical flow to match the “same” structures in the two image cubes. Step 3: In some embodiments, an initial estimate of the displacement between two respiratory phases (U → -U0 → ) is iteratively improved until it is optimized.

[0193] FIG. 26A is an illustration of the motion trajectory of lung tissue elements during a respiratory cycle according to some embodiments of the present disclosure. In FIG. 26A, there are six images for each image angle, which correspond from left to right to each of the six respiratory phases of a complete respiratory cycle, namely, early inspiratory phase (EI), late inspiratory phase (LI), maximum inspiratory phase (MI), early expiratory phase (EE), late expiratory phase (LE), and maximum expiratory phase (ME), as illustrated in FIG. 14. According to the finite strain theory, the vector connecting the position of a particle in its undeformed and deformed configurations is called the particle's displacement vector. Using a particular voxel in the EI image cube as a reference, the six images will show the deformation of the reference voxel in its original position, size, and shape during the respiratory cycle. As more and more air enters the lungs, the reference voxel starts to "bubble". In other words, the lung tissue corresponding to the reference voxel is deformed as the voxel "bubbles". The displacement (U) of two respiratory periods is calculated as the displacement (U) of the reference voxel in the EI image cube. → -U0 →) quantifies the degree of voxel bubbling and corresponding lung tissue deformation. Deformable image registration assumes that a reference voxel in the EI image cube moves to a new position in the LI image cube and simultaneously deforms due to the inhalation of more air into the lung tissue. As the respiratory cycle progresses ,The reference voxel maintains its motion trajectory as shown in the ,MI, EE, LE, and ME image cubes, respectively.,In other words, for every voxel in the reference image cube, a set of displacement vectors is ,calculated across all six image cubes.

[0194] FIG. 26B illustrates that there are five deformable registrations (2→1, 3→1, 4→1, 5→1, 6→1) between the EI image cube and five other image cubes. These six image cubes represent the patient's thoracic anatomy at six corresponding predefined respiratory phases. For each voxel, a vector (U → ) exists. Assuming that the vector corresponding to the EI image cube is zero, the six displacement vectors of a particular voxel are

number

[0195] Similarly, for a particular respiratory phase “n”, the tidal volume of the lung (T v ), airflow (A f ), heart rate phase (H c ) is the biometric data matrix,

number

[0196] For each voxel in the image cube, the parameter matrix of the biomechanical model

number

number

number

number

[0197] As mentioned above, there are many deformable image registration algorithms that can perform image registration of GREX imaging, including 3D multi-resolution optical flow algorithms. 3D multi-resolution optical flow algorithms calculate a smooth (e.g., fluid-like) transition between images taken at different observed tidal volumes. The calculated displacement (U → -U0 → ) are the observation points in the tissue trajectory shown in FIG. 24. The parameters of the biomechanical model (p1 → , p2 → , p3 → , p4 → ) can be solved, for example, by least squares regression to obtain the tidal volume (T v ), airflow (A f ), heart rate phase (H c ) is varied to generate the entire closed-loop trajectory. Obtaining the closed-loop trajectories of all tissue elements in the breast results in a new image cube that is considered to be biointerpolated. Table 1 summarizes each component of the GREX biomechanical model and how it is detected. [Table 1]

[0198] The post-processing software 106 performs biointerpolation between the acquired image cubes to input data for all potential respiratory phases and create a complete movie of the chest. Typically, a movie requires at least 30 simulated images for smooth frame-to-frame transitions. FIG. 28A is a schematic flow chart illustrating movie creation 2800 using biointerpolation according to some embodiments of the present disclosure. As illustrated in FIG. 28B, the biodata matrix corresponding to the EI image cube is T v = 20 ml, (A f )=20 ml / s, H c =0.10.

[0199] Each voxel in the intermediate simulation image cube at a particular instant of the respiratory cycle, e.g., EI+Δt, EI+2Δt, EI+3Δt, etc., is calculated using a biomechanical model with a parameter matrix [P → ], and the biometric data matrix corresponding to a particular instant in time. 3.4. Parameter Map

[0200] The clinical benefits of GREX imaging include unique parametric maps that present previously unavailable information about a patient's chest health to the user using 2D plots, 2D color washes, 3D plots and 3D vector field maps.

[0201] FIG. 29 shows an example of a standard radiograph of a healthy individual (left) and a patient with a stage 1b upper left lung tumor (right, indicated by an arrow). Both show standard radiographs used in the current radiography field. The diseased left lung on the right side of FIG. 29 is an early stage lung tumor that is not easily found on the standard radiograph because it is difficult to read and only displays anatomical information. In contrast, FIG. 30 shows the accompanying GREX parameter maps showing health status indicators of the same two patients, according to some embodiments of the present disclosure. The parameter map on the right clearly shows that the patient has disease in the upper left lung, whereas the standard radiograph is ambiguous. Changing the frame level and image contrast will eventually show the non-ventilated area of ​​the upper left lung. However, if the user does not follow these steps, the chances of finding this early stage lung cancer tumor will be low. In other words, the GREX-generated parameter maps can significantly reduce the risk that the user will miss the disease.

[0202] First, let us consider p1 in Figure 30, which represents the normal stress related to the ventilation volume. → Consider a 2D colormap of p1 → Based on the definition of p1, the user → is expected to be greater near the diaphragm compared to the apex of the lungs (e.g., as tidal volume increases, tissue displacement at the diaphragm is greater than at the apex of the lungs). Generally speaking, p1 → The magnitude of p1 varies smoothly across a healthy lung. → If the gradient of is calculated over the entire lung, a smooth function According to the example illustrated in FIG. 30, for both patients, p1 → Even though the parameters are similarly distributed, healthy subjects show twice as much tissue displacement as patients.

[0203] Next, we use the sum of the normal and shear stresses associated with the airflow, (p2 → +p3 →Consider a 2D color wash of the lungs. Generally speaking, the parameters are higher near the regions where the bronchial tree takes air into the lungs at a faster rate (the middle of the lungs). However, in diseased lungs, the presence of a tumor can substantially change the elastic behavior of the lungs, making the tumor visually distinct from the functionally healthy lung tissue distribution. In the example illustrated in Figure 30, the presence of a lung tumor is clearly visible from the left lung, since their sizes are significantly different. When disease affects a lung region, other lung tissue regions "take up the slack" by ventilating the healthy region but not the diseased region. However, as illustrated in the left diagram of Figure 29, the healthy lung is more elastic than the diseased lung. In other words, the airflow resistance increases significantly at the location of the tumor, highlighting the presence of the tumor and allowing a quantitative analysis of the impact of the tumor on the patient's ability to ventilate properly during breathing. The ratio of parameters related to tidal volume and parameters related to airflow clearly shows that the upper left lung has a significantly different pattern from the healthy one. Physically, the higher percentage can be interpreted as tissue moving in a more circular pattern than the example tissue trajectory illustrated in Figure 24. Under the 2D color wash, the tidal volume (T v p1 → ) and airflow (A f (p2 → +p3 → The observed total motion component histogram due to the left lung also shows clear indications of disease. A healthy lung has a bimodal distribution A f (p2 → +p3 → ). However, when one lung is diseased, the overall distribution pattern is different in both lungs. GREX imaging technique involves a Bayesian process, leveraging patient interview (information volunteered by the patient prior to examination) and the distribution of parameter histograms of the biomechanical model to better classify disease etiology. In summary, consistent indicators of lung disease (e.g., presence of tumor as shown in Figure 30) for multiple new GREX parameters would be of great help in early detection and disease diagnosis. 3.5. Diagnostic Disease Pointers

[0204] Lung cancer, chronic obstructive pulmonary disease (COPD), lower respiratory tract infections, and tuberculosis all have disease pointers that can be seen on existing digital diagnostic x-ray images. However, diagnostic disease pointers are not always visible in the early stages of disease. However, as shown in Figures 29 and 30, GREX parameters can serve as diagnostic disease pointers in the early stages of disease. If the medical team only reviews standard x-rays, the presence of lung cancer may go undetected because the tumor is not large enough to be clearly visible on standard x-rays. GREX imaging uses biosignals in combination with biodata-based targeted multiple radiographs to diagnose disease using previously unavailable information about lung motion. The GREX process directly addresses early lung cancer, where digital diagnostic x-ray images tend to have low specificity. The existing sensitivity limitations of digital diagnostic x-ray-based diagnosis are addressed by biodata-based imaging, which uses patient-specific respiratory information to indicate disease. Clinicians can diagnose disease at an early stage of onset and continue to monitor patients as the disease progresses with visualization and parameter maps of lung tissue motion. By applying machine learning and finite element analysis techniques to uncover insidious patterns and baseline changes in lung function, users can detect disease at its onset, improving patient survival and expanding treatment options.

[0205] Another example of how GREX will have a ripple effect on diagnostics is in the classification of the etiology of lung disease. In biopsies, asbestos-induced lung disease presents differently from tobacco-induced lung disease. Asbestos is a natural silicate mineral made up of long, thin fibrous crystals made up of millions of microscopic fibers. When inhaled, asbestos fibers penetrate the alveoli and eventually form a dense meshwork, impairing alveolar function and reducing lung function. The dense meshwork collects cancerous tissue, hence the name mesothelioma. On standard x-rays, mesothelioma can only be identified when plaque builds up. Plaque buildup appears as dense consolidation (blurring) on ​​the lung image. GREX allows users to see the alveoli and the lungs. Insidious asbestos may be detected through parameter maps before its presence is visible. When asbestos forms a network, the elasticity of the lung is locally reduced. This local and subtle reduction in elasticity is visible in parameter maps (2D color wash, parameter ratios, histograms). For example, in tissues that have lost elasticity, the trajectory of the tissue during breathing becomes circular rather than elliptical. This is due to the fact that p1 → and (p2 → +p3 → ) is higher than in healthy tissue. Looking at the color map, we can clearly see areas of the lung that show patterns indicative of disease.

[0206] Inhaled particles (non-fibrous) are completely different from fibrous asbestos particles. Inhaled particles are deposited in the lungs, "clogging" the airways, forming scar tissue, forming tumors instead of reticulum, and reducing lung function. GREX indicates the onset of particle deposition by detecting minute local changes in the dynamics of lung motion that are inconsistent with healthy characteristics. Tobacco contains tar and radon that deposit in the alveoli and dramatically alter lung function. GREX gives users a unique tool that can track the decline in lung function and show patients the destructive power of their smoking habit. COPD is another disease that can be elucidated more effectively with parameter maps than with current clinical methods. COPD is currently detected with spirometry tests and standard x-rays. These methods are not particularly sensitive and do not allow early detection of COPD. Detecting the disease as early as possible gives patients the opportunity to apply preventive treatment and change bad habits before it is too late. 3.6.Video Presentation

[0207] The advantage of the Video Presentation Graphic User Interface (GUI) is that it provides a clean and intuitive canvas for displaying planar and 3D video results. Features such as rotate view, pause video, and rulers are arranged in bins represented by tabs at the top of the screen. When a feature is selected, the tooltip changes to show the user which function is in use. The GUI is designed to be lightweight, so it works well even on older computer systems. Finally, the Video Presentation GUI works in conjunction with annotations / contours, possible disease pointers, and cloud computing platforms.

[0208] Healthcare professional users prefer to highlight and leave notes directly on medical images instead of attaching additional documentation to images. Healthcare professional users want image annotation ("attention-grabbing" arrows and treatment notes) and anatomical contouring (drawing virtual lines around anatomical structures) capabilities in their imaging exams. Healthcare professional users are often part of a larger care team that collaborates in treating patients. The following example describes the current workflow and technical capabilities of a clinical care team.

[0209] When lung cancer patients undergo imaging tests, the images are read by a radiologist, the radiation oncologist prescribes radiation therapy, and the operating surgeon prescribes surgical intervention. Specifically, the radiologist draws a contour around the tumor site and passes the contoured image to the radiation oncologist.

[0210] A radiation oncologist prescribes radiation therapy and / or tumor resection surgery based on the images.

[0211] Based on the contour drawn by the radiologist and the radiation oncologist's tumor resection instructions, the surgeon removes the tumor.

[0212] The post-processing software 106 provides tools for users to annotate and contour directly on the video, further streamlining the healthcare workflow. This easy-to-use functionality enhances the healthcare workflow and reduces the chance of medical errors. The interactive nature of the user interface ensures that treatment indications and medical concerns are clearly visible to all users, and clinical notes are displayed right in the corresponding anatomical regions of interest.

[0213] 31A-31B are flow diagrams of a method 3100 for imaging the lungs of a patient. In some embodiments of the present disclosure, some or all of the operations described below may be performed without human intervention (e.g., without intervention by a technician). In some embodiments, the method 3100 is performed by any of the devices described herein (e.g., the GREX imaging system 100 illustrated in FIG. 1). Some operations of the method 3100 are performed by a computer system including one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the operations of the method 3100. Some operations of the method 3100 may be combined or reordered in any manner.

[0214] The method includes positioning (3102) the patient in a first orientation relative to an X-ray imaging device (e.g., the GREX imaging system of FIG. 1). In some embodiments, the method 3100 is performed on an existing medical imaging system that is modified to perform certain operations described below. In some embodiments, positioning the patient in the first orientation includes moving (e.g., rotating) the patient to a first orientation (e.g., see FIG. 35), while the X-ray imaging system (e.g., the X-ray unit and detector panel) remains in a fixed position. For example, in some embodiments, the patient is seated or standing in a patient positioning fixture (PPF), such as the PFF 3501 described with reference to FIG. 35. In some embodiments, positioning the patient in the first orientation relative to the X-ray imaging device includes rotating the patient positioning fixture. For example, in some embodiments, at the start of the method 3100, the patient positioning fixture is rotated such that the sagittal plane of the patient is positioned at a predetermined angle relative to the optical axis of the X-ray imaging device (e.g., the axis along which the X-ray imaging device transmits X-rays). In some embodiments, the pre-defined angle is selected from the group consisting of -45 degrees, -22.5 degrees, 0 degrees, 22.5 degrees, and 45 degrees.

[0215] In some embodiments, positioning the patient in the first orientation includes moving (e.g., rotating) the X-ray imaging system while the patient remains in a fixed position (see, e.g., FIG. 17).

[0216] In some embodiments, while the patient is positioned in a first orientation relative to the X-ray imaging device, a patient positioning fixture maintains the patient in a fixed position (e.g., stabilizes the patient's position) and a 3D image of the patient's lungs can be reconstructed based on a "stationary object" assumption (e.g., see herein).

[0217] The method includes obtaining (3104) a patient respiratory volume measurement (e.g., the patient is breathing normally and does not need to hold their breath). In some embodiments, the patient respiratory volume measurement is a measurement (e.g., a direct measurement) of the patient's lung volume (e.g., instantaneous lung volume) or a derivative of the patient's lung volume (e.g., flow rate). In some embodiments, as described in more detail below, the patient respiratory volume measurement is a measurement that can be converted to the patient's tidal volume (e.g., measuring chest rise and fall). Tidal volume, as used herein, refers to the difference between the current lung volume and a predetermined baseline (e.g., maximal exhalation without extra effort, maximal inhalation without extra effort, or other baseline volume).

[0218] In some embodiments, the patient's respiration volume measurement is a geometric (spatial or positional) measurement of the patient's respiration.

[0219] In some embodiments, the patient's respiratory volume measurements include a chest rise (and / or chest fall) measurement of the patient (3106). In some embodiments, the patient's respiratory volume measurements are obtained (3108) using one or more of the following volumetric respiratory phase sensors (e.g., respiratory phase sensor 110 of FIG. 1 ): a 3D scanner, a spirometer, or an abdominal belt. In some embodiments, The method further includes creating 3110 a point cloud on the patient's chest surface, the respiratory volume measurements of the patient being determined from the point cloud on the patient's chest surface.

[0220] In some embodiments, the 3D point cloud is used to determine the patient's respiratory volume measurements without generating a mesh reconstruction of the patient's chest (e.g., the raw output of the 3D point cloud is used to generate the patient's respiratory volume measurements without generating a mesh). In some embodiments, the point cloud on the patient's chest surface is acquired using 3D imaging techniques to measure one or more locations on the patient's chest (3112). For example, 3D imaging techniques include laser scanning techniques such as light detection and ranging (LIDAR). Laser scanning techniques are highly beneficial because some lasers can accurately measure the location of the patient's chest even when the patient is wearing clothing (e.g., the patient wears LIDAR transparent clothing while undergoing method 3100).

[0221] In some embodiments, the method 3100 further includes identifying (3114) one or more anatomical landmarks on the patient's chest surface (e.g., externally visible landmarks such as clavicle, trachea, sternal notch, sternum, xiphoid process, spine, etc.) using the point cloud on the patient's chest surface. The method further includes inferring the location of one or more internal anatomical landmarks within the patient's chest (e.g., the location of the patient's lungs) from the point cloud on the patient's chest surface. In some embodiments, the method includes generating a reconstruction of the patient (e.g., generating a computer model for the patient, also referred to as a "virtual patient"). In some embodiments, generating the reconstruction of the patient includes generating a reconstruction of the patient's internal anatomy. In some embodiments, the reconstruction of the patient's internal anatomy includes a computer model (e.g., a 3D model) of tissue density. In some embodiments, the reconstruction includes or is used to determine the location of absorption cross-sections (e.g., x-ray absorption cross-sections) at multiple locations within the body. In some embodiments, the reconstruction of the patient is used to determine the x-ray dose delivered by the x-ray imaging device (e.g., for each image).

[0222] The method further includes determining 3116 a respiratory phase of the patient from volume measurements of the patient while the patient is positioned in the first orientation relative to the radiation source and while obtaining the respiratory measurements of the patient (e.g., in real time when the patient is breathing normally). In some embodiments, the patient's respiratory phase is defined by a lung volume. Thus, in some embodiments, determining the respiratory phase of the lungs includes determining a lung volume.

[0223] In some embodiments, operations 3102 and onward are performed as part of the imaging period of method 3100. Method 3100 further includes providing a training period prior to the imaging period during which information about the patient's normal breathing is obtained. For example, during the training period, patient breathing volume measurements are obtained at regular intervals over multiple breathing cycles of the patient (e.g., 15, 20, 50 cycles of the patient's breathing cycle are considered to correspond to one breath). The volume measurements from the training period are then used to associate specific volume measurements with specific breathing phases. For example, a volume measurement corresponding to a tidal volume of 400 ml is associated with the maximum inspiration phase, and a volume measurement corresponding to a tidal volume of 0 ml is associated with the maximum expiration phase. Additionally, statistics (e.g., histograms) regarding the patient's breathing may be obtained during the training period and used to verify that the breathing acquired during imaging is "normal" breathing (e.g., no deep breathing, no abnormal breathing, etc.).

[0224] During imaging, in some embodiments, the patient's respiratory phase that is determined (3118) is a future (e.g., predicted) respiratory phase. That is, in some embodiments, determining the patient's respiratory phase based on the patient's respiratory volume measurements includes predicting the future respiratory phase from one or more current and / or past respiratory phases. For example, the prediction is based on a time series of respiratory phases. In some embodiments, determining the future respiratory phase from one or more current and / or past respiratory phases is based on a time series of respiratory phases. The predicting includes generating an autoregressive integrated moving average (ARIMA) model. In some embodiments, the predicting uses data from a training period.

[0225] The method includes gating (3120) an X-ray imaging device to generate X-ray projection images (sometimes referred to as projection images) of the patient's lungs when it is determined that the patient's respiratory phases match the predefined respiratory phases (e.g., the lung volume matches the predefined lung volume). In some embodiments, the X-ray projection images are images taken at a particular angle (e.g., determined by the patient's orientation relative to the X-ray imaging device). In some embodiments, the X-ray projection images are obtained using a single X-ray exposure. In some embodiments, the method includes abandoning gating of the X-ray imaging device (e.g., abandoning exposing the patient to X-ray radiation) when it is determined that the patient's respiratory phases do not match the predefined respiratory phases.

[0226] In some embodiments, the method includes determining whether a current breath is irregular from the patient's breath volume measurements, and further including abandoning gating (e.g., acquisition) of the x-ray imager if the current breath is determined to be irregular (e.g., waiting for a suitable breath for acquiring x-ray projection images for a respiratory phase).

[0227] In some embodiments, the predefined respiratory phase is a first predefined respiratory phase of a plurality of predefined respiratory phases (3122). In some embodiments, the method further includes gating an x-ray imager to generate respective x-ray projection images of the patient's lungs when the patient's respiratory phase is determined to coincide with any of the plurality of predefined respiratory phases while acquiring the patient's respiratory volume measurements. In some embodiments, the x-ray projection images of the patient's lungs (e.g., x-ray measurements) are acquired (3124) only if the patient's respiratory phase as determined by the patient's respiratory volume measurements coincides with any one of the plurality of predefined respiratory phases. Thus, a total x-ray exposure to the patient is reduced.

[0228] In some embodiments, the plurality of predefined respiratory phases includes an early expiratory phase, a late expiratory phase, a maximum expiratory phase, an early inhalation phase, a late inhalation phase, and a maximum inhalation phase in a complete respiratory cycle of the patient (3216) (see, e.g., FIG. 14 ). In some embodiments, x-ray projection images are obtained for each of the plurality of predefined respiratory phases while the patient is positioned in a first orientation relative to the x-ray imaging device.

[0229] In some embodiments, the x-ray projection image is a first x-ray projection image (3128), and the method further includes repositioning the patient (e.g., rotating the patient or the x-ray imaging system) to a second orientation relative to the x-ray imaging device. In some embodiments, the method includes continuing to determine a respiratory phase of the patient based on the patient's respiratory volume measurements while the patient is positioned in the second orientation relative to the x-ray imaging device and while continuing to obtain respiratory volume measurements of the patient. In some embodiments, the method includes gating the x-ray imaging device to generate a second x-ray projection image of the patient's lungs when the patient's respiratory phase is determined to match a predefined respiratory phase.

[0230] In some embodiments, the method further includes generating a still image cube corresponding to a predefined respiratory phase using the first and second X-ray projection images (see, e.g., FIG. 21 ). In some embodiments, the still image cube is a three-dimensional reconstruction of the patient's lung volume. In some embodiments, the X-ray projection images are obtained for each of a plurality of predefined respiratory phases while the patient is positioned in each of a plurality of orientations (including a first orientation and a second orientation) relative to the X-ray imaging device. In some embodiments, the plurality of orientations includes at least five orientations (e.g., −45 degrees, −22.5 degrees, 0 degrees, 22.5 degrees, 45 degrees). In some embodiments, the plurality of orientations includes more than five orientations (e.g., six, seven, eight, or more orientations). In some embodiments, the patient is positioned in each of a plurality of orientations (including a first orientation and a second orientation) relative to the X-ray imaging device. While the subject is positioned in each of a plurality of orientations (including a first orientation and a second orientation) with respect to the position, X-ray projection images are not acquired except during a plurality of predetermined respiratory phases. Thus, X-ray projection images are acquired in five orientations and six different respiratory phases, resulting in a total of 30 X-ray projection images (e.g., the X-ray imaging device acquires only these images by gate control as described above).

[0231] As previously described herein, these 30 x-ray projection images can be used to reconstruct a 3D biomechanical model of lung motion. In some embodiments, a still image cube corresponding to a given respiratory phase (see, for example, FIG. 21) is generated 3130 from less than 10 x-ray projection images taken from various angles during the given respiratory phase.

[0232] Those skilled in the art should appreciate that the method 3100 may be applied to motions other than lung motion due to breathing. For example, in some embodiments, the method includes positioning a patient in a first orientation relative to an X-ray imaging device. The method further includes obtaining 3D measurements of a portion of the patient's body (e.g., 3D measurements of a position of the patient's body part). The method further includes determining whether a trigger criterion is met for triggering radiation exposure by the X-ray imaging device based on the 3D measurements of the patient's body part while the patient is positioned in the first orientation relative to the X-ray imaging device and while obtaining the 3D measurements of the patient's body part, and gating the X-ray imaging device to generate an X-ray image of the patient if the trigger criterion is determined to be met. In some embodiments, the X-ray image is an image of the patient's body part (e.g., a leg, abdomen, skull, etc. of the patient). In some embodiments, the trigger criterion includes a criterion that is met when the 3D measurements of the patient's body part indicate that the patient's body part is in a predetermined position (e.g., a position relative to the imaging device). In some embodiments, the method includes relinquishing gating control of the X-ray imager (e.g., relinquishing exposure of the patient to X-rays) if it is determined that the trigger criteria have not been met. Additionally, in some embodiments, the method 3100 may be applied to other imaging that is not strictly X-ray based, such as Positron Emission Tomography (PET) imaging and MRI imaging.

[0233] Method 3100 may also be applied to radiation therapy and radiation imaging. For example, in some embodiments, the method includes positioning a patient in a first orientation relative to a radiation therapy source. The method further includes acquiring 3D measurements of a body part of the patient (e.g., 3D measurements of a position of the body part of the patient). The method further includes determining whether a trigger criterion is met for triggering radiation exposure by the radiation therapy source based on the 3D measurements of the body part of the patient while the patient is positioned in the first orientation relative to the radiation therapy source and while acquiring the 3D measurements of the body part of the patient, and gating the radiation therapy source and exposing the patient to radiation (e.g., exposing the body part of the patient to radiation) if the trigger criterion is determined to be met. In some embodiments, the trigger criterion includes a criterion that is met when the 3D measurements of the body part of the patient indicate that the body part of the patient is in a predefined position (e.g., a position relative to the imaging device). In some embodiments, the method includes abandoning exposing the patient to radiation if the trigger criterion is determined to be not met.

[0234] The particular order of operations described in Figures 31A-31B is merely an example, and the order described is not meant to imply that the operations are the only order in which they can be performed. Those skilled in the art will appreciate that the operations described herein can be permuted in various ways. Other processes described herein with respect to other methods described herein can also be applied in a manner similar to method 3100 described above with reference to Figures 31A-31B. Such processes can be applied in a manner similar to method 3100 described above with reference to Figures 31A-31B, for example, as described in Figures 2, 6, 8, 11, 13, 15, 21, 23, 25, 27, 28A-28B, 32A-32B, 33A-33C, 34A-34B, 35A-35C, 36A-36C, 37A-37D, 38A-38B, 39A-39C, 40A-40C, 41A-41D, 42A-42D, 43A-43C, 44A-44C, 45A-45C, 46A-46C, 47A-47C, 48A-48C, 49A-49C, 50A-50C, 51A-51C, 52A-52C, 53A-53C, 54A-54C, 55A-55C, 56A-56C, 57A-57C, 58A-58C, 59A-59C, 60A-60C, 61A-61C, 62A-62C, 63A-63C, 64A-64C, 65A-65C, 66A-67C, 4C. For the sake of brevity, detailed description will be omitted.

[0235] 32A-32B are flow diagrams of a method (3200) for gating a radiation source during patient respiratory-cardiac phase coincidence. In some embodiments, some or all of the operations described below can be performed without human intervention (e.g., without intervention by a technician). In some embodiments, the method 3200 is performed by any of the devices described herein (e.g., the GREX imaging system 100 illustrated in FIG. 1). Some operations of the method 3200 are performed by a computer system including one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the operations of the method 3200. Some operations of the method 3200 may be combined or reordered in any manner.

[0236] The method includes positioning 3202 a patient in a first orientation relative to a radiation source. In some embodiments, the radiation source is an x-ray imaging device (3204). In some embodiments, the radiation source is a radiation therapy source (3206). For example, as illustrated in FIGS. 3 and 35, a patient (e.g., patient 3502) is positioned in a first position relative to a radiation source 3504 (e.g., x-ray unit 108). In some embodiments, positioning the patient includes moving (e.g., rotating) the patient, as in operation 3102 (FIG. 31A), and in some embodiments, positioning the patient includes moving (e.g., rotating) the x-ray device (e.g., x-ray source and detector).

[0237] The method includes obtaining 3208 a patient respiration measurement (e.g., using a respiratory phase sensor). In some embodiments, the patient respiration measurement is a patient respiration volume measurement in operation 3104 (FIG. 31A). In some embodiments, the patient respiration measurement is not a patient respiration volume measurement (e.g., a timing-based patient respiration measurement).

[0238] The method includes obtaining 3210 a measure of the patient's cardiac function. In some embodiments, the patient's cardiac function is measured 3304 using one or more sensors. In some embodiments, an electrocardiogram (ECG) is used to measure the patient's cardiac function (e.g., a 3-lead or 12-lead ECG). In some embodiments, the method includes obtaining a plurality of measures of the patient's cardiac function that provide a time series of electrical signals that control the movement of the patient's heart.

[0239] Returning to imaging, while the patient is positioned in a first orientation relative to the radiation source and while obtaining the patient's respiration measurements (3212), the method includes determining the patient's respiratory phase from the patient's respiration measurements (3214) and determining the patient's cardiac phase (e.g., in real time) from the patient's cardiac function measurements (3216). For example, in some embodiments, the patient's cardiac phase is determined using a Zero Instruction Set Computer (ZISC) processor, as described with reference to Figure 36. The ZISC processor can identify the occurrence of a predefined landmark in the cardiac cycle (e.g., an S wave or a T wave) during a single cardiac cycle or portion of a cardiac cycle.

[0240] In some embodiments, the method further includes obtaining (3218) measurements of the patient's cardiac function from a plurality of cardiac cycles of the patient prior to gating the radiation source to expose the patient to radiation (operation 3222, described below). In some embodiments, the method includes using the measurements of the patient's cardiac function obtained from the plurality of cardiac cycles to determine an average interval between a predefined cardiac phase and a start of a predefined window in the cardiac cycle. For example, the predefined window in the cardiac cycle represents the interval between the top of the R wave and a start of a gating window. For example, in some embodiments, operations 3202 and subsequent operations are performed as part of an imaging period of method 3200. Method 3200 further includes providing a training period prior to the imaging period during which information regarding the patient's cardiac function is obtained. For example, During the training period, ECG measurements of the patient are obtained at regular intervals over multiple cardiac cycles (e.g., 15, 20, 50 cycles of the patient's cardiac cycle, where one cycle corresponds to one full period of cardiac motion, such as from any T wave to the next T wave). The ECG measurements from the training period are then used to predict periods of calm of cardiac motion during the imaging period, as described below.

[0241] In some embodiments, the measurements obtained from multiple cardiac cycles of the patient are waveform measurements (3220) of the multiple cardiac cycles (e.g., ECG measurements), and the method includes validating the waveform measurements of the multiple cardiac cycles as being statistically stable.

[0242] The method further includes gating (3222) the radiation source to expose the patient to radiation based on determining that the patient's respiratory phase coincides with a predefined respiratory phase and determining that the patient's cardiac phase coincides with a predefined window of the cardiac cycle. In some embodiments, gating the radiation source exposes the patient's lungs to radiation. In some embodiments, the predefined cardiac window corresponds to a period of quiescence of cardiac motion (e.g., a period during the cardiac cycle during which cardiac motion is minimal, as described with reference to FIG. 12). In some embodiments, gating the radiation source during the same cardiac cycle as the determined cardiac phase. In this manner, gating the exposure of the patient's lungs based on the coincidence of the respiratory phase with the predefined cardiac phase window ensures that a precise region of the lungs (e.g., a precise region of lung tissue) is exposed to radiation without perturbation or motion due to cardiac motion.

[0243] In some embodiments, the radiation source is an x-ray imager. Gating the radiation source to expose the patient to radiation includes gating the x-ray imager to generate x-ray projection images of the patient's lungs (3224). In some embodiments, as in method 3100 of FIGS. 31A-31B, the x-ray projection images can be acquired for the x-ray imaging source and multiple orientations of the patient for multiple respiratory phases. These x-ray projection images can be used to generate movies of lung motion and / or biophysical models of the lungs (e.g., relating lung tissue motion to biophysical parameters such as stress, strain, elasticity, etc.). In some embodiments, according to method 3200, these images are acquired within a predefined cardiac window to minimize lung perturbation or motion due to cardiac motion.

[0244] In some embodiments, the radiation source is a radiation therapy source. Gating the radiation source to expose the patient to radiation includes gating the radiation therapy source to deliver a therapeutic dose to a lung region of the patient (3226). In these situations, it is important to deliver as much radiation dose as possible to diseased tissue (e.g., cancerous tissue) and as little radiation dose as possible to healthy tissue. Method 3200 improves radiation therapy devices by precisely delivering a dose to diseased tissue while minimizing radiation delivered to healthy tissue.

[0245] In some embodiments, determining that the patient's cardiac phase coincides with the predefined window of the cardiac cycle is to predict (3228) the predefined window of the cardiac cycle by detecting the predefined cardiac phase in real time (e.g., using the BEMP card of FIG. 36) and waiting a length of time corresponding to the average interval between the predefined cardiac phase and the beginning of the predefined window of the cardiac cycle. For example, in some embodiments, the peak of the T wave is detected and the average interval from the T wave to the ideal gate window is waited at which point the radiation source is gated.

[0246] The particular order of operations depicted in Figures 32A-32B is merely an example, and the order depicted is not meant to imply that this is the only order in which the operations can be performed. Those skilled in the art will appreciate that the operations described herein can be permuted in various ways. Also, other processes described herein with respect to other methods described herein may be implemented in various ways with reference to Figures 32A-32B. Such a process can be applied in a manner similar to method 3100 described above as. Such a process is described, for example, with reference to Figures 2, 6, 8, 11, 13, 15, 21, 23, 25, 27, 28A-28B, 31A-31B, 33A-33C, and 34A-34C. For the sake of brevity, detailed description will be omitted.

[0247] 33A-33C are flow diagrams of a method 3300 for fitting data from registered images to generate a model of lung mechanical properties, according to some embodiments of the present disclosure. In some embodiments, some or all of the operations described below can be performed without human intervention (e.g., without intervention by a technician). In some embodiments, the method 3300 is performed by any of the devices described herein (e.g., the GREX imaging system 100 illustrated in FIG. 1). Some operations of the method 3300 are performed by a computer system including one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the operations of the method 3300. Some operations of the method 3300 may be combined or reordered in any manner.

[0248] The method includes extracting (3302) a plurality of displacement fields of the lung tissue from a plurality of x-ray measurements (e.g., x-ray images, also referred to as x-ray projection images) corresponding to different respiratory phases of the lung. Each displacement field represents a motion of the lung tissue from a first respiratory phase to a second respiratory phase, each respiratory phase having a corresponding set of biometric parameters. In some embodiments, the x-ray measurements are x-ray projection images (also referred to as x-ray projection images) acquired by method 3100 and / or method 3200. In some embodiments, extracting the displacement field from the lung tissue includes identifying a portion of the lung tissue in one or more first x-ray projection images from the first respiratory phase as corresponding to the same portion of the lung tissue in one or more second x-ray projection images from the second respiratory phase, and determining a displacement of the portion of the lung tissue from the first respiratory phase to the second respiratory phase (see, e.g., FIG. 28B). In some embodiments, the identification is performed using the deformable image registration algorithm described above.

[0249] In some embodiments, the plurality of x-ray measurements includes a plurality of x-ray images obtained at a first respiratory phase for each of a plurality of orientations of the x-ray imaging device relative to the patient, thereby forming a plurality of x-ray images corresponding to the first respiratory phase and a plurality of x-ray images corresponding to a second respiratory phase. In some circumstances, at least one of the x-ray images corresponding to the first respiratory phase was obtained during a different patient respiratory cycle than the different x-ray image corresponding to the first respiratory phase (e.g., images obtained in the same respiratory phase, but at different breaths). In some embodiments, the method includes grouping the plurality of x-ray images by respiratory phase.

[0250] In some embodiments, the method includes extracting a number of vector fields, of which the displacement field is an example.

[0251] In some embodiments, one or more sensors are used to measure (3304) the patient's biosignals as one or more time series sequences, including a 3D spatial position localizer (e.g., 3D spatial localizer 300 of FIG. 3), a respiratory phase sensor, and a cardiac phase sensor. In some embodiments, the 3D spatial position localizer is configured to measure and output (3306) the patient's body movements caused by breathing and cardiac rhythm in real time as a time series. In some embodiments, the respiratory phase sensor is configured to measure (3308) one or more physiological indicators related to the patient's breathing, including tidal volume and its first time derivative. In some embodiments, the cardiac phase sensor is configured to measure (3310) a periodic-stationary electrical signal (e.g., an ECG signal) generated by the patient's heart. For example, the cardiac phase sensor measures a periodic-stationary electrical signal having characteristics corresponding to cardiac phases.

[0252] In some embodiments, a patient's vital signs measured by one or more sensors are used to trigger (3312) an X-ray unit to acquire X-ray images of the patient at specific respiratory and cardiac phases (see, e.g., method 3200).

[0253] In some embodiments, the x-ray unit includes a clock (3314), and the patient's vital signs measured by the one or more sensors are synchronized with the x-ray unit's clock. In some embodiments, the respective values ​​of the vital signs are recorded in association with the acquired x-ray image.

[0254] In some embodiments, the patient's vital signs measured during the training frame are used to build 3316 an optimized respiratory prediction model to predict the desired respiratory phase for triggering the X-ray unit to take an X-ray image of the patient.

[0255] The method includes calculating (3318) one or more biophysical parameters of a lung biophysical model using a plurality of displacement fields of the lung tissue between different respiratory phases of the lung and a corresponding set of bioparameters. In some embodiments, calculating the one or more biophysical parameters includes calculating one or more derivatives (e.g., curl, slope, etc.) of the displacement fields. In some embodiments, the biophysical parameters are biomechanical parameters (e.g., stress, strain, modulus of elasticity, elastic limit, etc.). In some embodiments, the one or more biophysical parameters define a physical relationship between the bioparameters associated with the different respiratory phases of the lung and the plurality of displacement fields of the lung tissue (3320). In some embodiments, the set of bioparameters associated with each respiratory phase includes a tidal volume and airflow of the lung at each respiratory phase, and a cardiac phase corresponding to each respiratory phase (3322). In some embodiments, the physical relationship between the bioparameters associated with the different respiratory phases of the lung and the plurality of displacement fields of the lung tissue is defined by the following equation:

number

[0256] p1 → The vector represents the normal stress caused by the tidal volume, p2 → is the normal stress caused by the air flow, and p3 → is the shear stress caused by the airflow, and p4 → is the tissue motion due to cardiac motion, and the tissue displacement at any point in the closed loop trajectory (U → -U0 → ) is the tidal volume (T v ), airflow (A f ), heart rate phase (H c ) are expressed as the sum of stress, strain, and perturbation cardiac motion vectors, respectively, scaled by

[0257] In some embodiments, the different respiratory phases of the lungs include an early expiratory phase, a late expiratory phase, a maximum expiratory phase, an early inhalation phase, a late inhalation phase, and a maximum inhalation phase in a complete respiratory cycle of the patient (3324).

[0258] In some embodiments, the method further includes displaying a visual representation of the biophysical parameter. For example, in the example of FIG. 30, the biophysical parameter is p1 → and (p2 → +p3 → ) ratio. In some embodiments, displaying the visual representation includes displaying an image of the lungs, where the color of the locations in the image of the lungs corresponds to the biophysical parameter (e.g., the image of the lungs is displayed with a color map of the biophysical parameter). Displaying such a visual representation improves the accuracy of diagnosis, and therefore the x-ray imaging device itself. For example, it is clearer and easier to see that the patient's left lung is diseased in FIG. 30 than in a conventional x-ray, as shown in FIG. 29.

[0259] In some embodiments, the method further comprises generating (3326) a plurality of medical image cubes corresponding to different respiratory phases of the lungs from a plurality of x-ray measurements corresponding to different respiratory phases of the lungs. (see, for example, FIG. 21). In some embodiments, the multiple displacement fields of the lung tissue are extracted from the multiple medical image cubes corresponding to different respiratory stages of the lung by delineating the lung tissue from the remaining part of the first medical image cube by image segmentation, and determining, for each voxel in the first medical image cube, a displacement vector between the voxel in the first medical image cube and the second medical image cube using intensity-based structure mapping between the first medical image cube and the second medical image cube (3328). The multiple displacement fields of the lung tissue are extracted from the multiple medical image cubes corresponding to different respiratory stages of the lung by iteratively adjusting the displacement vectors of different voxels in the first medical image cube and corresponding voxels in the second medical image cube.

[0260] In some embodiments, the method includes selecting (3330) one or more of the plurality of medical image cubes as a reference medical image cube; determining a set of bio-parameters associated with each reference medical image cube; selecting a set of bio-parameters based on lung bio-measurements between two sets of bio-parameters associated with the two reference medical image cubes; and simulating the medical image cube between the two reference medical image cubes by applying the set of bio-parameters based on the lung bio-measurements to a bio-physics model.

[0261] The particular order of operations described in Figures 33A-33C is merely an example, and the order described is not meant to imply that the operations are the only order in which they can be performed. Those skilled in the art will appreciate that the operations described herein can be rearranged in various ways. Other processes described herein with respect to other methods described herein can also be applied in a manner similar to the method 3300 described above with reference to Figures 33A-33C. Such processes are described, for example, with reference to Figures 2, 6, 8, 11, 13, 15, 21, 23, 25, 27, 28A-28B, 31A-31B, 32A-32B, and 34A-34C. For the sake of brevity, detailed descriptions will be omitted.

[0262] 34A-34C are flow diagrams of a method 3400 for generating a 3D X-ray image cube movie. In some embodiments, some or all of the operations described below can be performed without human intervention (e.g., without intervention by a technician). In some embodiments, the method 3400 is performed by any of the devices described herein (e.g., the GREX imaging system 100 illustrated in FIG. 1). Some operations of the method 3400 are performed by a computer system including one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the operations of the method 3400. Some operations of the method 3400 may be combined or reordered in any manner.

[0263] In some embodiments, the patient's vital signs are measured (3402) as one or more time series sequences using one or more sensors, where the one or more sensors include a 3D spatial position localizer, a respiratory phase sensor, and a cardiac phase sensor (see, e.g., methods 3100 and 3200).

[0264] In some embodiments, the method further includes identifying a cardiac phase gate window using measurements from one or more cardiac phase sensors, predicting a respiratory phase using measurements from one or more respiratory phase sensors, determining a coincidence between the cardiac phase gate window and the predicted respiratory phase to generate an X-ray imaging pulse, and tagging an X-ray image corresponding to the X-ray imaging pulse with the respiratory phase, cardiac phase, and 3D spatial position localizer measurements (3406) (see, e.g., method 3200).

[0265] In some embodiments, the 3D spatial position localizer detects patient motion caused by breathing and heartbeat. The device is configured to measure 3408 the body movements of the subject in real time and output them as a time series (see, e.g., methods 3100 and 3200).

[0266] In some embodiments, the respiratory phase sensor is configured to measure 3410 one or more physiological indicators related to the patient's breathing, including tidal volume and its first time derivative. For example, tidal volume rate varies with time and airflow.

[0267] In some embodiments, the cardiac phase sensor is configured to measure 3412 a periodic, steady-state electrical signal generated by the patient's heart and having characteristics corresponding to cardiac phases.

[0268] In some embodiments, after being synchronized with the X-ray unit's clock, two separate filters are used to remove signal drift and noise from the patient's biosignals (3414).

[0269] In some embodiments, the patient's vital signs measured by one or more sensors are used to trigger the x-ray unit to acquire 3416 x-ray images of the patient at specific respiratory and cardiac phases.

[0270] In some embodiments, the x-ray unit includes a clock (3418). Patient vital signs measured by one or more sensors are synchronized with the x-ray unit's clock. Each value of the vital sign is recorded in association with the acquired x-ray image.

[0271] In some embodiments, the patient's vital signs are measured (3422) during a training frame (e.g., a training period) before taking an x-ray image of the patient, and the patient's vital signs measured during the training frame include a number of complete respiratory cycles of the patient (see, e.g., methods 3100 and 3200).

[0272] In some embodiments, multiple tidal volume percentiles for a complete respiratory cycle are identified (3424) using the patient's biosignals measured during the training frame, each tidal volume percentile corresponding to one of the different respiratory phases.

[0273] In some embodiments, the patient's vital signs measured during the training frame are used to build 3426 an optimized respiratory prediction model to predict the desired respiratory phase at which to trigger the x-ray unit to take an x-ray image of the patient.

[0274] In some embodiments, the optimized respiratory prediction model is based on an autoregressive integrated moving average (ARIMA) model (3428).

[0275] In some embodiments, the desired respiratory phase for taking x-ray images of the patient is configured 3430 to coincide with a cardiac gating window during which cardiac-induced lung motion is slowly changing.

[0276] In some embodiments, the cardiac gating window is selected 3432 based on the location of T and P waves in an electrocardiogram (ECG) signal, which slowly changes cardiac-induced pulmonary motion.

[0277] The method includes transforming (3434) the first plurality of sets of x-ray images of the lungs taken at different projection angles into a second plurality of sets of x-ray images of the lungs corresponding to different respiratory phases.

[0278] In some embodiments, converting the first plurality of sets of x-ray images of the lungs taken at different projection angles into a second plurality of sets of x-ray images of the lungs corresponding to different respiratory phases comprises converting the first plurality of sets of x-ray images of the lungs taken at different projection angles into a second plurality of sets of x-ray images of the lungs corresponding to different respiratory phases The method further includes taking a first plurality of sets of x-ray images of the lungs at different projection angles (3436), each set of the first plurality of sets of x-ray images corresponding to a different respiratory phase of the lungs at a particular projection angle. In some embodiments, converting the first plurality of sets of x-ray images of the lungs taken at different projection angles into a second plurality of sets of x-ray images of the lungs corresponding to different respiratory phases further includes reorganizing the first plurality of sets of x-ray images of the lungs into a second plurality of sets of x-ray images of the lungs by associated respiratory phases. Each set of the second plurality of sets of x-ray images corresponds to a different respiratory phase of the lungs.

[0279] In some embodiments, the x-ray images within any particular set are geometrically decomposed and temporally independent (3438).

[0280] In some embodiments, different respiratory phases of the lungs correspond to different tidal volume percentiles of lung movement (3440).

[0281] In some embodiments, the different respiratory phases of the lungs include an early expiratory phase, a late expiratory phase, a maximum expiratory phase, an early inhalation phase, a late inhalation phase, and a maximum inhalation phase in a complete respiratory cycle of the patient (3442).

[0282] In some embodiments, multiple x-ray images of the lungs taken at different projection angles all correspond to the same respiratory phase (3444).

[0283] In some embodiments, different respiratory phases of the lungs at a particular projection angle are collected 3446 from at least two respiratory cycles.

[0284] The method includes generating (3448) a still image cube from each of the second plurality of sets of x-ray images at each respiratory phase using a backprojection method.

[0285] The method includes combining (3450) still image cubes corresponding to different respiratory phases of the lungs into a 3D X-ray image cube movie by time interpolation.

[0286] The particular order of operations described in Figures 34A-34C is merely an example, and the order described is not meant to imply that the operations are the only order in which they can be performed. Those skilled in the art will appreciate that the operations described herein can be rearranged in various ways. Other processes described herein with respect to other methods described herein can also be applied in a manner similar to the method 3400 described above with reference to Figures 34A-34C. Such processes are described, for example, with reference to Figures 2, 6, 8, 11, 13, 15, 21, 23, 25, 27, 28A-28B, 31A-31B, 32A-32B, and 33A-33C. For the sake of brevity, detailed descriptions will be omitted.

[0287] FIG. 35 is an example of a patient positioning fixture (PPF) 3501 (e.g., a rotating chair) that supports a patient (3502), according to some embodiments. In some embodiments, the PPF 3501 rotates (e.g., along rotation 3503) to position the patient (2502) at multiple angles (e.g., orientations) relative to a radiation source (3504) (e.g., an x-ray imaging system or a radiation therapy system). For example, the PPF moves to move the patient to a desired position (e.g., the patient does not have to move independently) so that the radiation device 3504 can take x-ray images of the patient at various angles. In some embodiments, the patient rotates (3502) through multiple angles (e.g., along rotation 3502) without rotating the PPF 3501. In some embodiments, the PPF 3501 and / or the patient 3205 automatically rotate and / or move (e.g., using a motor) to a desired position. In some embodiments, a technician rotates and / or moves the patient 3502. In some embodiments, the flat panel detector unit 3505 is positioned behind the patient relative to the radiation device 3504 .

[0288] In some embodiments, one or more cameras 3506-1 to 3506-m are used to detect objects within a predefined area (e.g., a room) surrounding the PPF 3501. For example, the camera 3506 captures images of objects as the PPF 3501 moves around (and rotates within) the predefined area to determine whether the objects collide with the PPF 3501. In some implementations, some effort is made to avoid collisions with the PPF 3501 (and the patient 3502).

[0289] In some embodiments, one or more 3D imaging sensors (eg, LIDAR sensors or structured light sensors) are used to geometrically monitor the breathing of the patient 3502 as in method 3100.

[0290] FIG. 36 is an example of a Biological Event Monitoring Process (BEMP) card 3600. The BEMP card 3600 includes a programmable analog filter, a programmable analog-to-digital converter (ADC) / digital signal processor (DSP) 3605, and a Zero Instruction Set Computer (ZISC) processor 3606. The BEMP card 3600 receives an analog input signal 3602 that includes a patient's biosignal. In some embodiments, the analog input 3602 includes an ECG signal. In some embodiments, the analog input is a 3-lead ECG signal or a 12-lead ECG signal. The ZISC processor 3606 detects predefined patterns in the analog input in real time. For example, the ZISC processor 3606 detects an R-wave in the ECG signal to predict when the next quiescent period of the patient's cardiac cycle will occur (e.g., within a TP interval). In some embodiments, the ZISC processor 3606 outputs a ZISC 4-bit weighted output that can be used to trigger a radiation source (e.g., an X-ray imaging system or a radiation therapy system). In some embodiments, the clock signal 3603 of the BEMP card 3600 is provided from an external source, such as a radiation source, so that the BEMP card 3600 can be synchronized to the radiation source.

[0291] The computational techniques described in the Post-Processing Software 106 chapter are computationally resource intensive. Many community hospitals and small clinics do not have the computer hardware required to create the animations, calculate the biomechanical models, and provide users with a fast and smooth experience. Because the functions of the Post-Processing Software 106 are performed in the cloud, users can access the powerful visualization tools of the Post-Processing Software 106 on their treatment consoles, office desktops, or work laptops.

[0292] Throughout this specification, the terms "embodiments," "some embodiments," "one embodiment," "another example," "example," "particular example," or "some examples" mean that a particular feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the disclosure. Thus, the use of phrases such as "in some embodiments," "in one embodiment," "in another embodiment," "in an example," "in a particular example," or "in some examples" herein does not necessarily refer to an embodiment or example of the disclosure. Furthermore, particular features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0293] Although the embodiments have been presented and described above, it should be understood by those skilled in the art that the above embodiments do not limit the present disclosure, and that changes, substitutions, and modifications can be made without departing from the principle, spirit, and scope of the present disclosure.

Claims

1. 1. A method of imaging the lungs of a patient, comprising: positioning a patient in a first orientation relative to an x-ray imaging device; obtaining a respiratory volume measurement of a patient; while the patient is positioned in the first orientation relative to the x-ray imaging device and while obtaining the respiratory volume measurements of the patient; determining a respiratory phase of the patient based on said respiratory volume measurements of the patient; gating the x-ray imaging device to generate x-ray projection images of the patient's lungs if the patient's respiratory phase is determined to coincide with a predefined respiratory phase.

2. the predetermined respiratory period is a first predetermined respiratory period of a plurality of predetermined respiratory periods; The method further comprises:

2. The method of claim 1, further comprising gating the x-ray imaging device to generate respective x-ray projection images of the patient's lungs if the patient's respiratory phase is determined to coincide with any of the plurality of predefined respiratory phases while obtaining the respiratory volume measurements of the patient.

3. 3. The method of claim 2, wherein x-ray projection images of the patient's lungs are acquired only if the patient's respiratory phase as determined by the patient's respiratory volume measurements corresponds to any one of the plurality of predefined respiratory phases.

4. 4. The method of claim 2 or 3, wherein the plurality of predefined respiratory phases comprises an early expiratory phase, a late expiratory phase, a maximum expiratory phase, an early inspiratory phase, a late inspiratory phase, and a maximum inspiratory phase in a complete respiratory cycle of the patient.

5. The X-ray projection image is a first X-ray projection image, The method further comprises: repositioning the patient to a second orientation relative to the x-ray imaging device; while the patient is positioned in the second orientation relative to the x-ray imaging device and while continuing to obtain the respiratory volume measurements of the patient; continuing to determine a respiratory phase of the patient based on said respiratory volume measurements of the patient; gating an x-ray imaging device to generate a second x-ray projection image of the patient's lungs when the patient's respiratory phase is determined to match the predetermined respiratory phase; and generating a still image cube corresponding to the predefined respiratory phase using the first and second X-ray projection images.

6. The method of claim 5 , wherein the still image cube corresponding to the predetermined respiratory phase is generated from less than ten X-ray projection images taken from different angles during the predetermined respiratory phase.

7. A method according to any preceding claim, wherein the respiratory volume measurement of a patient comprises a chest rise measurement of the patient.

8. The method according to any one of claims 1 to 6, wherein the patient's respiratory volume measurements are obtained using one or more of the following volumetric respiratory sensors: a 3D scanner, a spirometer, an abdominal belt.

9. 9. The method of claim 1, further comprising creating a point cloud on a surface of the patient's chest, the respiratory volume measurements of the patient being determined from the point cloud on the patient's chest surface.

10. The method of claim 9 , wherein the cloud of points on a surface of a patient's chest is acquired using 3D imaging techniques to measure one or more locations on the patient's chest.

11. identifying one or more anatomical landmarks on a surface of the patient's chest using the cloud of points on the surface of the patient's chest; The method of claim 10 , further comprising inferring the location of one or more internal anatomical landmarks within the patient's chest from the cloud of points on a surface of the patient's chest.

12. the respiratory phase of the patient is a future respiratory phase, Determining a respiratory phase of the patient based on the respiratory volume measurements of the patient includes: The method of any one of claims 1 to 11, comprising predicting the future respiratory phase from one or more current and / or past respiratory phases.

13. 1. A system comprising: An X-ray device; one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: positioning a patient in a first orientation relative to an x-ray imaging device; obtaining a respiratory volume measurement of a patient; while the patient is positioned in the first orientation relative to the x-ray imaging device and while obtaining the respiratory volume measurements of the patient; determining a respiratory phase of the patient based on said respiratory volume measurements of the patient; and gating the x-ray imaging device to generate x-ray projection images of the patient's lungs if the patient's respiratory phase is determined to match a predefined respiratory phase.

14. 1. A system comprising: An X-ray device; one or more processors; A system comprising: a memory for storing instructions, which, when executed by said one or more processors, cause said one or more processors to perform a method according to any one of claims 1 to 12.

15. 1. A non-transitory computer-readable storage medium storing instructions that, when executed by a system including an x-ray device and one or more processors, cause the one or more processors to: positioning a patient in a first orientation relative to an x-ray imaging device; obtaining a respiratory volume measurement of a patient; while the patient is positioned in the first orientation relative to the x-ray imaging device and while obtaining the respiratory volume measurements of the patient; determining a respiratory phase of the patient based on said respiratory volume measurements of the patient; and gating the x-ray imaging device to generate x-ray projection images of the patient's lungs if the patient's respiratory phase is determined to match a predefined respiratory phase.

16. A non-transitory computer readable storage medium storing instructions which, when executed by a system comprising an X-ray device and one or more processors, cause the one or more processors to perform the method of any one of claims 1 to 12.

17. positioning a patient in a first orientation relative to a radiation source; obtaining respiration measurements from a patient; obtaining cardiac function measurements of the patient; while the patient is positioned in the first orientation relative to the radiation source and while obtaining the respiration measurements of the patient; determining a respiratory phase of the patient from said respiratory measurements of the patient; determining a cardiac phase of the patient from said cardiac function measures of the patient; Gating the radiation source determining that the patient's respiratory phase corresponds to a predetermined respiratory phase; and based on a determination that the cardiac phase of the patient corresponds to a predetermined window of the cardiac cycle, exposing the patient to radiation.

18. the radiation source being an X-ray imaging device; 20. The method of claim 17, wherein gating the radiation source to expose the patient to radiation comprises gating the x-ray imaging device to generate x-ray projection images of the patient's lungs.

19. The radiation source is a radiotherapy source, 20. The method of claim 17, wherein gating the radiation source to expose the patient to radiation comprises gating the radiation therapy source to deliver a therapeutic dose to a lung region of the patient.

20. prior to gating said radiation source to expose a patient to radiation; obtaining cardiac function measurements of the patient from a plurality of cardiac cycles of the patient; determining an average interval between the predetermined cardiac phase and a beginning of the predetermined window of a cardiac cycle using the cardiac function measures of the patient obtained from the plurality of cardiac cycles; Determining that the cardiac phase of the patient coincides with the predetermined frame of the cardiac cycle includes: determining the predetermined frame of the cardiac cycle by: Detecting a predetermined cardiac phase in real time; 20. The method according to claim 17, further comprising: waiting a length of time corresponding to an average interval between the predetermined cardiac phase and a start point of the predetermined window of a cardiac cycle.

21. The measurements obtained from the patient's multiple cardiac cycles include waveform measurements of the multiple cardiac cycles, The method comprises:

21. The method of claim 20, comprising validating the waveform measurements of the plurality of cardiac cycles as being statistically stable.

22. The method according to any one of claims 17 to 21, wherein the predetermined window of the cardiac cycle is a sedation window of the cardiac cycle.

23. 1. A system comprising: A radiation source; one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: positioning a patient in a first orientation relative to a radiation source; obtaining respiration measurements from a patient; obtaining cardiac function measurements of the patient; while the patient is positioned in the first orientation relative to the radiation source and while obtaining the respiration measurements of the patient; determining a respiratory phase of the patient from said respiratory measurements of the patient; determining a cardiac phase of the patient from said cardiac function measures of the patient; Gating the radiation source determining that the patient's respiratory phase corresponds to a predetermined respiratory phase; and based on a determination that the patient's cardiac phase corresponds to a predetermined window of the cardiac cycle, exposing the patient to radiation.

24. 1. A system comprising: A radiation source; one or more processors; A system comprising: a memory for storing instructions, which, when executed by said one or more processors, cause said one or more processors to perform a method according to any one of claims 17 to 22.

25. 1. A non-transitory computer-readable storage medium storing instructions that, when executed by a system including an x-ray device and one or more processors, cause the one or more processors to: positioning a patient in a first orientation relative to a radiation source; obtaining respiration measurements from a patient; obtaining cardiac function measurements of the patient; while the patient is positioned in the first orientation relative to the radiation source and while obtaining the respiration measurements of the patient; determining a respiratory phase of the patient from said respiratory measurements of the patient; determining a cardiac phase of the patient based on the cardiac function measurements of the patient; Gating the radiation source determining that the patient's respiratory phase corresponds to a predetermined respiratory phase; and based on a determination that the patient's cardiac phase corresponds to a predetermined window of the cardiac cycle, exposing the patient to radiation.

26. A non-transitory computer readable storage medium storing instructions which, when executed by a system comprising an X-ray device and one or more processors, cause the one or more processors to perform the method of any one of claims 17 to 22.

27. 1. A method for determining a biophysical model of a patient's lungs from a plurality of x-ray measurements corresponding to different respiratory phases of the lungs, comprising: extracting a plurality of displacement fields of lung tissue from a plurality of x-ray measurements corresponding to different respiratory phases of the lungs, each displacement field representing a motion of said lung tissue from a first respiratory phase to a second respiratory phase, each respiratory phase having a corresponding set of biometric parameters; and calculating one or more biophysical parameters of a biophysical model of the lung using a plurality of displacement fields of the lung tissue between different respiratory phases of the lung and a corresponding set of bio-parameters.

28. 28. The method of claim 27, wherein the one or more biophysical parameters define a physical relationship between bioparameters associated with different respiratory phases of the lungs and a plurality of displacement fields of lung tissue.

29. a plurality of displacement fields of the bio-parameters and the lung tissue associated with different respiratory phases of the lungs; The physical relationship is defined by the following equation: ##EQU00017## The above ｐ１ → The vector represents the normal stress caused by the tidal volume, ｐ２ → is the normal stress caused by the air flow, ｐ３ → is the shear stress caused by the airflow, ｐ４ → is the tissue motion due to cardiac motion, and the tissue displacement at any point in the closed loop trajectory (U → -U ０ → 29. The method of claim 28, wherein Tv, Af and Hc are expressed as the sum of stress, strain and perturbation cardiac motion vectors scaled by tidal volume (Tv), airflow (Af) and cardiac phase (Hc), respectively.

30. generating a plurality of medical image cubes corresponding to different respiratory phases of the lungs from the plurality of x-ray measurements corresponding to different respiratory phases of the lungs; A plurality of displacement fields of the lung tissue are extracted from the plurality of medical image cubes corresponding to different respiratory phases of the lungs, Delineating lung tissue from a remainder of the first medical image cube by image segmentation; for each voxel in the first medical image cube, determining a displacement vector between the voxel in the first medical image cube and the second medical image cube using an intensity based structure mapping between the first medical image cube and a second medical image cube; The method according to any one of claims 27 to 29, wherein the displacement vectors of different voxels in the first medical image cube and corresponding voxels in the second medical image cube are extracted by iteratively adjusting the displacement vectors of different voxels in the first medical image cube and corresponding voxels in the second medical image cube.

31. 31. The method of any one of claims 27 to 30, wherein the set of biological parameters associated with each respiratory phase comprises tidal volume and airflow of the lungs during each respiratory phase, and cardiac phase corresponding to each respiratory phase of the lungs.

32. generating a plurality of medical image cubes corresponding to different respiratory phases of the lungs from said plurality of x-ray measurements corresponding to different respiratory phases of the lungs; selecting one or more of the plurality of medical image cubes as a reference medical image cube; determining a set of physiological parameters associated with each reference medical image cube; selecting a set of biometric parameters based on lung biometric measurements between two sets of biometric parameters associated with two reference medical image cubes; The method according to any one of claims 27 to 31, further comprising: simulating a medical image cube between the two reference medical image cubes by applying a set of bio-parameters based on lung bio-measurements to a bio-physical model.

33. 33. The method of any one of claims 27 to 32, wherein the different respiratory phases of the lungs include an early expiratory phase, a late expiratory phase, a maximum expiratory phase, an early inspiratory phase, a late inspiratory phase and a maximum inspiratory phase in a complete respiratory cycle of the patient.

34. 34. The method of claim 27, further comprising measuring a patient's vital signs as one or more time-series sequences using one or more sensors, the one or more sensors including one or more 3D spatial position localizers, respiratory phase sensors, and cardiac phase sensors.

35. 35. The method of claim 34, wherein the 3D spatial position localizer is configured to measure and output as a time series, in real time, movements of the patient's body caused by breathing and heartbeat.

36. 36. The method of claim 34 or 35, wherein the respiratory phase sensor is configured to measure one or more physiological indices related to the patient's respiration, including tidal volume and its first time derivative.

37. The method of any one of claims 34 to 36, wherein the cardiac phase sensor is configured to measure a periodic steady-state electrical signal generated by the patient's heart.

38. 38. The method according to any one of claims 34 to 37, further comprising using a patient's vital signs measured by the one or more sensors to trigger an X-ray unit to acquire X-ray images of the patient at specific respiratory and cardiac phases.

39. the X-ray unit includes a clock; the patient's vital signs measured by the one or more sensors are synchronized with a clock of the X-ray unit; 39. The method of claim 38, further comprising recording the respective values ​​of said vital signs in association with said acquired X-ray images.

40. 40. The method of claim 39, wherein the patient's vital signs measured during a training window are used to develop an optimized respiratory prediction model for predicting a desired respiratory phase during which an X-ray unit will be activated to take an X-ray image of the patient.

41. 1. A system for determining a biophysical model of a patient's lungs from a plurality of x-ray measurements corresponding to different respiratory phases of the lungs, comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: extracting a plurality of displacement fields of lung tissue from a plurality of x-ray measurements corresponding to different respiratory phases of the lungs, each displacement field representing a motion of said lung tissue from a first respiratory phase to a second respiratory phase, each respiratory phase having a corresponding set of biometric parameters; and calculating one or more biophysical parameters of a biophysical model of the lung using a plurality of displacement fields of the lung tissue between different respiratory phases of the lung and a corresponding set of bio-parameters.

42. 1. A system for determining a biophysical model of a patient's lungs from a plurality of x-ray measurements corresponding to different respiratory phases of the lungs, comprising: one or more processors; A system comprising: a memory for storing instructions, which, when executed by said one or more processors, cause said one or more processors to perform a method according to any one of claims 27 to 40.

43. 1. A non-transitory computer-readable storage medium storing instructions that, when executed by a system having one or more processors, cause the one or more processors to: extracting a plurality of displacement fields of lung tissue from a plurality of x-ray measurements corresponding to different respiratory phases of the lungs, each displacement field representing a motion of said lung tissue from a first respiratory phase to a second respiratory phase, each respiratory phase having a corresponding set of biometric parameters; and calculating one or more biophysical parameters of a biophysical model of the lung using a plurality of displacement fields of the lung tissue between different respiratory phases of the lung and a corresponding set of bio-parameters.

44. A non-transitory computer-readable storage medium storing instructions that, when executed by a system having one or more processors, cause the one or more processors to perform the method of any one of claims 27 to 40.

45. 1. A method for generating a 3D X-ray image cube animation from 2D X-ray images of a patient, comprising: transforming the first plurality of sets of x-ray images of the lungs taken at different projection angles into a second plurality of sets of x-ray images of the lungs corresponding to different respiratory phases; generating a still image cube from each of the second plurality of sets of x-ray images at each respiratory phase using a backprojection method; and combining still image cubes corresponding to different respiratory phases of the lungs into a 3D X-ray image cube movie by time interpolation.

46. Transforming a first plurality of sets of x-ray images of the lungs taken at different projection angles into a second plurality of sets of x-ray images of the lungs corresponding to different respiratory phases, comprising: taking a first plurality of sets of x-ray images of the lungs at different projection angles, each set of the first plurality of x-ray images corresponding to a different respiratory phase of the lungs at a particular projection angle; 46. ​​The method of claim 45, further comprising reorganizing the first plurality of sets of x-ray images of the lungs into a second plurality of sets of x-ray images of the lungs by associated respiratory phases, each set of the second plurality of sets of x-ray images corresponding to a respective respiratory phase of the lungs.

47. 47. A method according to claim 45 or 46, wherein the X-ray images within any particular set are geometrically resolved and temporally independent.

48. 48. The method according to any one of claims 45 to 47, wherein different respiratory phases of the lung correspond to different tidal volume percentiles of lung movement.

49. 49. The method of any one of claims 45 to 48, wherein the different respiratory phases of the lungs include an early expiratory phase, a late expiratory phase, a maximum expiratory phase, an early inspiratory phase, a late inspiratory phase and a maximum inspiratory phase in a complete respiratory cycle of the patient.

50. A method according to any one of claims 45 to 49, wherein a plurality of X-ray images of the lungs taken at different projection angles all correspond to the same respiratory phase.

51. 51. The method of any one of claims 45 to 50, comprising measuring a patient's vital signs as one or more time series sequences using one or more sensors, the one or more sensors including one or more 3D spatial position localizers, respiratory phase sensors, and cardiac phase sensors.

52. identifying cardiac phase gate windows using measurements from one or more cardiac phase sensors; predicting a respiratory phase using measurements from one or more respiratory phase sensors; Identifying a cardiac phase gating window and a predicted respiratory phase coincidence that generates an x-ray imaging pulse; 52. The method of claim 51, further comprising tagging an x-ray image corresponding to said x-ray imaging pulse with respiratory period, cardiac phase, and 3D spatial position localizer measurements.

53. 53. A method according to claim 51 or 52, wherein the 3D spatial position localiser is adapted to measure in real time and output as a time series the movements of the patient's body caused by breathing and heartbeat.

54. 54. The method according to claim 51, wherein the respiratory phase sensor is configured to measure one or more physiological indices related to the patient's respiration, including tidal volume and its first time derivative. The method according to any one of claims 1 to 5.

55. 55. A method according to any one of claims 51 to 54, wherein the cardiac phase sensor is configured to measure a periodic, steady-state electrical signal produced by the patient's heart and having characteristics corresponding to cardiac phases.

56. A method according to any one of claims 51 to 55, further comprising using two separate filters to remove signal drift and noise from the patient's bio-signals after being synchronized with the X-ray unit's clock.

57. 57. The method according to any one of claims 51 to 56, further comprising using the patient's vital signs measured by the one or more sensors to trigger an X-ray unit to acquire X-ray images of the patient at specific respiratory and cardiac phases.

58. the X-ray unit includes a clock; the patient's vital signs measured by the one or more sensors are synchronized with a clock of the X-ray unit; 58. The method of claim 57, further comprising recording the respective values ​​of said vital signs in association with said acquired X-ray images.

59. 59. A method according to any one of claims 51 to 58, wherein the patient's vital signs are measured during a training frame prior to taking an x-ray image of the patient, and the patient's vital signs measured during the training frame include a number of complete respiratory cycles of the patient.

60. 60. The method of claim 59, wherein a plurality of tidal volume percentiles in a complete respiratory cycle are identified using the patient's biosignals measured during a training frame, each tidal volume percentile corresponding to one of the different respiratory phases.

61. 61. The method of claim 59 or 60, wherein the patient's vital signs measured during the training frame are used to build an optimized respiratory prediction model for predicting the desired respiratory phase for triggering an X-ray unit to take an X-ray image of the patient.

62. 62. The method of claim 61, wherein the optimized respiratory prediction model is based on an autoregressive integrated moving average (ARIMA) model.

63. 63. The method of claim 61 or 62, wherein the desired respiratory phase for taking an x-ray image of the patient is configured to coincide with a cardiac gating window during which cardiac-induced pulmonary motion is slowly changing.

64. 64. The method of claim 63, wherein the cardiac gating window is selected based on the location of T-waves and P-waves in an electrocardiogram (ECG) signal, whereby cardiac-induced pulmonary motion changes slowly.

65. The method according to any one of claims 45 to 64, wherein different respiratory phases of the lungs at a particular projection angle are collected from at least two respiratory cycles.

66. 1. A system for determining a biophysical model of a patient's lungs from a plurality of x-ray measurements corresponding to different respiratory phases of the lungs, comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: A first plurality of sets of x-ray images of the lungs taken at different projection angles corresponding to different respiratory phases are converting the x-ray images of the lungs into a second plurality of sets; generating a still image cube from each of the second plurality of sets of x-ray images at each respiratory phase using a backprojection method; and combining still image cubes corresponding to different respiratory phases of the lungs into a 3D X-ray image cube movie by time interpolation.

67. 1. A system for determining a biophysical model of a patient's lungs from a plurality of x-ray measurements corresponding to different respiratory phases of the lungs, comprising: one or more processors; A system comprising: a memory storing instructions, which, when executed by said one or more processors, cause said one or more processors to perform a method according to any one of claims 45 to 65.

68. 1. A non-transitory computer-readable storage medium storing instructions that, when executed by a system having one or more processors, cause the one or more processors to: transforming the first plurality of sets of x-ray images of the lungs taken at different projection angles into a second plurality of sets of x-ray images of the lungs corresponding to different respiratory phases; generating a still image cube from each of the second plurality of sets of x-ray images at each respiratory phase using a backprojection method; combining still image cubes corresponding to different respiratory phases of the lungs into a 3D X-ray image cube movie by time interpolation.

69. A non-transitory computer readable storage medium storing instructions which, when executed by a system having one or more processors, cause the one or more processors to perform the method of any one of claims 45 to 65.

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