System and method for determining radiation parameters

Abstract

A method includes positioning a patient in a first orientation relative to a radiation source. The method further includes measuring one or more locations of a patient's thorax using a 3D imaging technique. The method further includes, while measuring the one or more locations of the patient's thorax using the 3D imaging technique: generating a model of the patient's thorax using the one or more locations of the patient's thorax; updating the model of the patient's thorax as the patient breathes; and exposing the patient to a radiation dose using the radiation source, wherein the dose is determined according to the model of the patient's thorax.

Description

Technical Field

[0001] Some embodiments of the present invention relate to radiographic imaging and therapy, and more specifically to systems and methods for determining radiation parameters (e.g., dose) of a radiation source (e.g., an X-ray imager or a radiotherapy device). Background Technology

[0002] Computed tomography (CT) scans use computer processing to combine numerous X-ray measurements taken from different angles to generate cross-sectional (tomographic) images (virtual "slices") of specific areas of a scanned object, allowing medical professionals to see inside an object (such as a patient) without surgery. A crucial parameter of a CT scan is the dose (e.g., the number of photons) delivered per measurement. Typically, the dose from the X-ray tube is controlled by setting the amount of current flowing through the thermionic filament. If the dose is too low, too many photons will attenuate in the imaged object, resulting in a poor signal-to-noise ratio. If the dose is too high, the patient will be exposed to unnecessarily large amounts of potentially harmful radiation.

[0003] Therefore, for many CT scanners, a set of reconnaissance images is required to calibrate the dose. In the reconnaissance protocol, the CT scanner rotates the gantry to a vertical position (e.g., 0°). The patient is instructed to hold their breath, and the scan bed moves rapidly through the aperture. Next, the gantry stops in a lateral decubitus position (e.g., 90°), the patient holds their breath again, and the scan bed again moves rapidly through the aperture. In the imaging protocol, the gantry rotates while the scan bed moves through the aperture. As the gantry completes a 360° rotation and moves to the next adjacent scan bed position, the X-ray tube current changes (based on information obtained from the reconnaissance images). Summary of the Invention

[0004] A major drawback of the aforementioned reconnaissance scheme is that, since calibration images are acquired while the patient is holding their breath, images acquired during the imaging process must also be acquired while the patient is holding their breath. However, as discussed further in detail in this invention, a wealth of information can be generated by acquiring X-ray images at different respiratory stages. Therefore, there is a need for systems and methods for determining the radiation parameters (e.g., dose) of an X-ray imager while the patient is breathing. In some cases, these same principles can be applied to radiotherapy.

[0005] Therefore, embodiments of the present invention provide a method for determining radiation parameters of a radiation source (e.g., an X-ray imager or a radiotherapy source). The method includes positioning a patient in a first direction relative to the radiation source. The method further includes measuring one or more locations of the patient's chest cavity using 3D imaging technology. The method further includes, while measuring one or more locations of the patient's chest cavity using 3D imaging technology: generating a patient chest cavity model using the one or more locations of the patient's chest cavity; updating the patient chest cavity model as the patient breathes; and exposing the patient to a radiation dose using the radiation source, wherein the dose is determined based on the patient chest cavity model.

[0006] Furthermore, some embodiments provide a non-transitory computer-readable storage medium storing instructions that, when executed by a system comprising one or more processors, cause the one or more processors to perform a set of operations. The set of operations includes positioning a patient in a first direction relative to a radiation source. The set of operations further includes measuring one or more locations of the patient's chest cavity using 3D imaging technology. The set of operations further includes, while measuring one or more locations of the patient's chest cavity using 3D imaging technology: generating a model of the patient's chest cavity using the one or more locations of the patient's chest cavity; updating the model of the patient's chest cavity as the patient breathes; and exposing the patient to a radiation dose using the radiation source, wherein the dose is determined based on the patient's chest cavity model. Attached Figure Description

[0007] To more clearly describe the embodiments of this disclosure or the technical solutions in the prior art, the accompanying drawings necessary for the description of the embodiments or the prior art will be briefly explained. Obviously, the drawings in the following description are only some embodiments of this disclosure. Those skilled in the art can obtain other drawings based on the structures shown in these figures without inventive effort.

[0008] Figure 1 This is a schematic block diagram of a GREX imaging system including a hardware box, acquisition software, and post-processing software, according to some embodiments of the present disclosure.

[0009] Figure 2 This is a schematic flowchart of a GREX image acquisition process according to some embodiments of the present disclosure.

[0010] Figure 3 This is a schematic block diagram depicting a top view of a 3D spatial orientation locator of a GREX imaging system according to some embodiments of the present disclosure.

[0011] Figure 4 Examples of two breaths that share the same maximum inspiratory phase in time but have completely different tidal volumes, according to some embodiments of the present disclosure, are depicted.

[0012] Figure 5 Examples of tidal volume percentiles (two bottom subplots) for patients with regular breathing (top left subplot) and patients with irregular breathing (top right subplot) according to some embodiments of the present disclosure are depicted.

[0013] Figure 6 This is a schematic flowchart of a synchronization process according to some embodiments of the present disclosure.

[0014] Figure 7 The present disclosure depicts synchronized cardiac electrocardiogram (ECG) signals and lung respiratory signals according to some embodiments of the present disclosure.

[0015] Figure 8 This is a schematic flowchart of a drift and signal-noise removal process according to some embodiments of the present disclosure.

[0016] Figure 9 The illustration depicts exemplary breathing as tidal volume versus time according to some embodiments of this disclosure.

[0017] Figure 10 The diagram depicts airflow and tidal volume according to some embodiments of this disclosure. Figure 9 The same breathing pattern shown in the image.

[0018] Figure 11 This is a schematic flowchart of a respiratory phase prediction process according to some embodiments of the present disclosure.

[0019] Figure 12 The present disclosure describes an exemplary gating window over time that allows for slow changes in lung motion induced by the heart, based on the location selection of the T and P waves in the ECG signal according to some embodiments of the present disclosure.

[0020] Figure 13 This is a schematic flowchart of another GREX procedure that uses real-time identification of cardiac stage prediction rather than cardiac gating windows, according to some embodiments of this disclosure.

[0021] Figure 14 Examples of trigger images depicting, from left to right, at early exhalation, late exhalation, maximal exhalation, early inhalation, late inhalation, and maximal inhalation during the respiratory phase, according to some embodiments of the present disclosure.

[0022] Figure 15 This is a schematic flowchart illustrating the image acquisition triggering process according to some embodiments of the present disclosure.

[0023] Figure 16 This is a schematic block diagram of variables used in an exemplary GREX image reconstruction algorithm according to some embodiments of the present disclosure.

[0024] Figure 17This is a schematic block diagram of the operating components of a GREX imaging system during an imaging procedure according to some embodiments of the present disclosure.

[0025] Figure 18 This is an exemplary depiction of tissue depths traversed by 0° (A) and 90° (B) X-ray projections according to some embodiments of this disclosure.

[0026] Figure 19 This is an exemplary depiction of depth resolution based on a narrow projection angle range (1) and a wide projection angle range (2) according to some embodiments of the present disclosure.

[0027] Figure 20 This is an exemplary illustration of the β imaging angle orientation relative to the three main imaging planes (left) and the imaging isogonal point of the GREX imaging system in a top-down viewing direction (right) according to some embodiments of the present disclosure.

[0028] Figure 21A and 21B This is an exemplary block diagram illustrating how a GREX imaging system according to some embodiments of the present disclosure transfers a 2D x-ray projection to a static image cube and ultimately to an image cube image.

[0029] Figure 22 This is a graphical depiction of the process of creating the static image cube shown in FIG21 according to some embodiments of the present disclosure.

[0030] Figure 23 This is a schematic flowchart of an image filtering process according to some embodiments of the present disclosure.

[0031] Figure 24 An exemplary closed-loop lung tissue trajectory is depicted in some embodiments of the present disclosure, resulting from the interaction between the heart and lungs in a piece of tissue located in the left lung near the heart.

[0032] Figure 25 This is a schematic flowchart illustrating the operation of components of a biomechanical model according to some embodiments of the present disclosure.

[0033] Figure 26A It is a graphical depiction of the movement trajectory of lung tissue elements during the respiratory cycle according to some embodiments of the present disclosure.

[0034] Figure 26B It is a graphical depiction of displacement vectors between different breathing stages according to some embodiments of the present disclosure.

[0035] Figure 27 This is a schematic flowchart of a multi-resolution 3D optical flow algorithm according to some embodiments of the present disclosure.

[0036] Figure 28A This is a schematic flowchart illustrating the generation of images using biometric interpolation based on some embodiments of the present disclosure.

[0037] Figure 28B This is a block diagram illustrating the creation of an intermediate image cube using a biometric data matrix, based on some embodiments of this disclosure.

[0038] Figure 29 Examples of standard radiographs depicting healthy patients and patients with stage 1b left upper lung tumors (indicated by arrows).

[0039] Figure 30 Examples of GREX parameter diagrams depicting indicators of the condition of healthy patients and indicators of the health condition of sick patients according to some embodiments of the present disclosure are provided.

[0040] Figures 31A to 31B This is a flowchart of a method for imaging a patient's lungs according to some embodiments of the present disclosure.

[0041] Figures 32A to 32B This is a flowchart of a method for gating a radiation source according to some embodiments of the present disclosure.

[0042] Figures 33A to 33C This is a flowchart of a method for determining a biophysical model of a patient's lungs according to some embodiments of the present disclosure.

[0043] Figures 34A to 34C This is a flowchart of a method for generating 3D X-ray cubic images according to some embodiments of the present disclosure.

[0044] Figure 35 Depicts exemplary patient positioning and immobilization devices (PPFs) (e.g., swivel chairs) for supporting patients according to some embodiments.

[0045] Figure 36 A demonstrative biological event monitoring process (BEMP) card is depicted according to some embodiments.

[0046] Figures 37A-37B This is a flowchart illustrating a method for determining a certain radiation dose to expose a patient, according to certain embodiments.

[0047] Figures 38A-38C The process of generating a patient's thoracic cavity model according to certain embodiments is described. Detailed Implementation

[0048] Reference will now be made in detail to embodiments of this disclosure. Throughout the specification, identical or similar elements and elements having the same or similar functions are designated by the same reference numerals. The embodiments described herein with reference to the accompanying drawings are illustrative and explanatory, and are intended to provide a general understanding of this disclosure. The embodiments described should not be construed as limiting this disclosure.

[0049] In this specification, unless otherwise specified or limited, relative terms such as “center,” “longitudinal,” “lateral,” “front,” “rear,” “right,” “left,” “inner,” “outer,” “lower,” “upper,” “horizontal,” “vertical,” “above,” “below,” “upper,” “top,” “bottom,” and their derivatives (e.g., “horizontally,” “downward,” “upward,” etc.) shall be interpreted as referring to directions subsequently described or as shown in the accompanying drawings. These relative terms are for ease of description and do not require this disclosure to be constructed or operated in a particular orientation.

[0050] In this invention, unless otherwise specified or limited, the terms “installation,” “connection,” “linking,” “fixing,” etc., are used in a broad sense and can be, for example, a fixed connection, a detachable connection, or an integral connection; they can also be a mechanical or electrical connection; they can also be a direct connection or an indirect connection through an insertion structure; they can also be an internal connection between two elements, as can be understood by those skilled in the art according to the specific circumstances.

[0051] In this invention, unless otherwise specified or limited, a structure in which the first feature is "above" or "below" the second feature may include embodiments in which the first feature and the second feature are in direct contact, and may also include embodiments in which the first feature and the second feature are not in direct contact with each other but are in contact through an additional feature formed therebetween. Furthermore, "above," "on top," or "on the second feature" may include embodiments in which the first feature is exactly or obliquely positioned above, above, or "on top" the second feature, or simply means that the height of the first feature is greater than the height of the second feature; while "below," "below," or "at the bottom" of the second feature may include embodiments in which the first feature is exactly or obliquely positioned below, below, or "at the bottom" of the second feature, or simply means that the height of the first feature is less than the height of the second feature.

[0052] Figure 1 This is a schematic block diagram of a GREX imaging system 100 comprising 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) according to some embodiments of the present disclosure.

[0053] Hardware box 102 contains three measurement sensors (e.g., 3D spatial orientation locator 300, respiratory phase sensor 110, and cardiac phase sensor 112), which independently acquire the patient's biometric signals as time series, and the collected time series serve as input to acquisition software 104. Acquisition software 104 processes and filters the biometric time series measurements to generate an imaging trigger signal (e.g., reaching x-ray unit 108). The imaging trigger signal is targeted at a specific respiratory phase and optionally at the patient's cardiac phase. The respiratory and cardiac phases are each defined by their respective biometric time series measurements. Connector cables transmit the imaging trigger signal from acquisition software 104 to x-ray unit 108, which acquires radiographic images of the respiratory and cardiac phases at the targeted phases. Once acquired, a series of images defining the complete respiratory cycle for the respiratory and cardiac phases is input to post-processing software 106. The post-processing software constructs a biomechanical model of lung movement based on the images for the respiratory and cardiac phases. The biomechanical model is then used to generate other diagnostic results in the post-processing software. Although this application uses X-ray images as an example, it will be apparent to those skilled in the art that the methods disclosed herein can be applied effortlessly to other types of medical images. For example, the process of constructing biomechanical models is not limited to using X-ray images and can use other types of medical images (e.g., CT scans, MRI, etc.).

[0054] Figure 2 This is a schematic flowchart of a GREX image acquisition process 200 according to some embodiments of the present disclosure (e.g., by...). Figure 1 The acquisition software 104 performs the following: Specifically, the acquisition software 104 synchronizes and processes time series measurements from hardware box 102 to remove potential signal drift and noise. Next, the acquisition software 104 implements a stage prediction algorithm that predicts respiratory and cardiac stages based on synchronized, drift-free, and noise-free time series inputs. Based on the respiratory and cardiac stage prediction results, the acquisition software 104 uses a logic algorithm 118 to search for consistency of the targeted respiratory and cardiac stages. The consistency of the targeted respiratory and cardiac stages defines the conditions for triggering image generation and the acquisition of the resulting images by the x-ray unit 108. Once the GREX images are acquired, the post-processing software performs new lung disease diagnoses using GREX images previously unavailable to medical professionals. For example, the acquired GREX images are used to construct a biomechanical model that defines the thoracic geometry as varying with respiratory and cardiac stages, but does not explicitly include temporal parameters.

[0055] In this document, the GREX-based imaging system is divided into three sections. Section 1 describes an embodiment of the hardware box 102. Section 2 describes an embodiment of the acquisition software 104. Section 3 describes an embodiment of the post-processing software 106. Each section describes in more detail the configuration as follows: Figure 1 The components and functions of the hardware box 102, acquisition software 104, and post-processing software 106 shown are illustrated.

[0056] Chapter 1. Hardware Box 102

[0057] In some embodiments, hardware 102 has at least two functions. First, it acquires biometric signals defining the anatomical geometry of the thoracic cavity. Second, it communicates with a digital diagnostic X-ray unit (e.g., X-ray unit 108).

[0058] Biometric signal inputs defining the anatomical geometry of the thoracic cavity include: thoracic cavity dimensions (measured via a 3D spatial locator 300). Figure 3 The biometrics are measured in three phases: respiratory (measured via respiratory phase sensor 110) and cardiac (measured via ECG monitor). Within hardware box 102, biometric signals are sampled in real-time at 100Hz to create time series curves for each signal input. The output of hardware box 102, i.e., the time series curves, is then transmitted to acquisition software.

[0059] Figure 3 This is a schematic block diagram depicting a top view of a 3D spatial orientation locator 300 of a GREX imaging system 100 according to some embodiments of the present disclosure.

[0060] Chapter 1.1 - 3D Spatial Orientation Positioner 300

[0061] The 3D spatial orientation locator 300 measures real-time body movements of a patient caused by breathing and heartbeat, and outputs them as time series in a coordinate space (e.g., Cartesian, polar, hyperspace, etc.). Figure 3As shown, the 3D spatial positioning locator 300 includes three independent 3D detectors (e.g., detectors 302a-302c) fixed to a balanced circular track on the ceiling of a room. The three 3D detectors 302a of the system are separated from each other by 120° angular increments. The patient is positioned between the x-ray unit 108 and the x-ray detector panel 304, such that the patient is centered on the 3D spatial positioning locator 300 mounted on the ceiling. The system measures the patient's thoracic expansion (e.g., rise and fall) in a predefined coordinate space. Each 3D detector 302 has an unobstructed field of view for the patient's torso and for one of the x-ray unit 108 and detector panel 304. Note that in some embodiments more than three 3D detectors 302 may be used, while in other embodiments fewer than three 3D detectors 302 may be used. In some embodiments, the detectors are cameras (e.g., 3D imaging techniques using photogrammetry and / or structured light). In some embodiments, the detectors are LiDAR detectors (e.g., 3D imaging techniques using time-of-flight technology).

[0062] Using a real-time depth map, each 3D detector 302 creates a surface representation of the patient. Simultaneous information from all three 3D detectors 302 is combined to form a volumetric skin surface orientation measurement that varies in real time based on the patient's respiratory and cardiac stages (e.g., using ray projection techniques). The 3D spatial orientation locator 300 uses the volumetric skin surface orientation measurement in at least two ways: (i) defining the patient's spatial boundaries and (ii) determining the patient's tissue location. Based on assumptions such as skin thickness, rib thickness, muscle thickness, and bone orientation derived from a standard medical internal radiation dose (MIRD) anatomy database, the 3D spatial orientation locator 300 roughly estimates the real-time spatial orientation of the lungs within the patient. The 3D spatial orientation locator 300 uses the MIRD data to calculate the spatial boundary conditions of the lungs, which are then made available to post-processing software 106. For example, the estimation of the lung's spatial boundary conditions by the 3D spatial orientation locator 300 produces an initial thoracic geometry, which the post-processing software 106 uses to simulate the cumulative tissue density along rays originating from the x-ray unit 108.

[0063] Chapter 1.2 - Respiratory Phase Sensor 110

[0064] The respiratory phase sensor 110 in hardware box 102 measures key physiological metrics related to respiration, namely tidal volume and its first time derivative (e.g., the rate of change of tidal volume over time or airflow). Two methods exist for measuring tidal volume: direct tidal volume measurement and indirect tidal volume measurement. Direct tidal volume measurement is performed using a mouth spirometer, which consists of a turbine within a tube that rotates at a rate proportional to the volume of air inhaled or exhaled by the patient. Indirect tidal volume measurement (or any other geometric measurement of the patient's chest cavity, as described herein) is performed using an abdominal binder, which measures changes in the patient's abdominal circumference during respiration (e.g., ...). Figure 1 (As shown). A larger abdominal circumference indicates inhalation, while a smaller abdominal circumference indicates exhalation. The abdominal binder does not directly measure tidal volume. To convert changes in abdominal circumference into a physiologically meaningful quantity, hardware box 102 correlates changes in abdominal circumference with an estimated lung volume determined by 3D spatial orientation locator 300. For example, thoracic expansion is proportional to abdominal expansion during respiration. When used together, the measurements from the abdominal binder and 3D spatial orientation locator 300 can be used to estimate the air content in the lungs.

[0065] As used herein, the term tidal volume refers to the difference between the current lung volume and a predefined baseline (e.g., the volume during the maximum expiratory phase of normal breathing without additional effort, the volume during the maximum inspiratory phase of normal breathing without additional effort, or any other suitable baseline volume). Based on the ideal gas law, the difference in air density between room air and body air causes the air in the lungs to increase by 11% compared to the tidal volume. To maintain mass, the body is 11% larger in volume than the amount of air inhaled. Therefore, the tidal volume of the lungs can thus be calculated using external measurements of the body calibrated to the body's air content. Furthermore, the 3D spatial orientation locator 300 provides an auxiliary check for the accuracy of air content and tidal volume measurements by identifying the patient's volume expansion during respiration. The body's volume expansion is compared to estimates of the amount of air in the trachea, lungs, and bronchi from X-ray images.

[0066] Chapter 1.3 - Cardiac Stage Sensor 112

[0067] like Figure 1As shown, cardiac phase is measured using an electrocardiogram (ECG) monitor or a blood volume pressure device (e.g., via cardiac phase sensor 112). For example, when using an ECG monitor, the clinician places leads on each of the patient's arms and a ground lead on the lower left side of the patient's abdomen (away from the diaphragm and abdominal binder). The human heart generates periodic and steady electrical signals with characteristics corresponding to cardiac phases. Steady signals are signals that are random processes, and their joint probability distribution does not change over time. The blood volume pressure device uses a light source and a light sensor to measure light attenuation in the patient's fingers. The circulating blood volume driven by the heartbeat causes variations in the amount of light attenuation in the patient's fingers. The amount of light attenuation is proportional to cardiac phase.

[0068] Typically, a digital diagnostic X-ray unit (e.g., X-ray unit 108) is powered on via an analog plunger attached to a plug port. The plug port is uniquely configured to accept plungers with specific pin configurations. In each pin configuration, there is an "acquisition pin" that receives a voltage signal to power on (e.g., gate) the X-ray unit. When an end user presses the plunger, the plunger sends a voltage pulse to the digital diagnostic X-ray unit, which activates an imaging beam passing through the patient's body.

[0069] like Figure 1 As shown, hardware box 102 uses a connector cable to transmit the same voltage pulse (e.g., a gating signal) from acquisition software to digital diagnostic x-ray unit 108. The voltage pulse signal turns on the digital diagnostic x-ray beam (e.g., a gating x-ray beam) when the voltage pulse signal exceeds a predefined voltage threshold, and turns off x-ray unit 108 when the voltage pulse signal on the pin becomes less than the predefined voltage threshold. In some embodiments, hardware box 102 generates a square wave signal with a pulse height greater than a predetermined voltage threshold and maintains said pulse height for the duration of image exposure. That is, a rectangular pulse with a voltage Y lasting X seconds is generated for x-ray unit 108 to capture an x-ray image of the patient. Subthreshold voltages less than Y will not trigger the scanner, such that the value of Y must be greater than the predefined voltage threshold to initiate an x-ray image. The pulse duration X is the amount of image exposure time, starting when the voltage Y exceeds the predefined voltage threshold and ending when the voltage drops below the predetermined voltage threshold. The voltage dropping below the predefined voltage threshold turns off x-ray unit 108. The pulse duration is defined by the manufacturer's specifications but is typically about a few milliseconds.

[0070] Chapter 2. Obtaining Software 104

[0071] The acquisition software 104 is designed to acquire measured spatial, cardiac, and pulmonary time series from the hardware box 102 and determine when to trigger the x-ray unit 108 to acquire x-ray images at specific respiratory and cardiac phases. The acquisition software 104 then accurately overlays (e.g., synchronizes) the measured cardiac and pulmonary phases, processes (e.g., filters) the acquired biometric time series, identifies appropriate imaging times, and generates an electronic trigger signal for the digital diagnostic x-ray unit 108. The electronic trigger signal (e.g., a gate signal) activates the x-ray unit 108, thereby acquiring a snapshot image of the thoracic geometry. Spatial, cardiac, and pulmonary values ​​associated with the snapshot are recorded to define the thoracic surface geometry at the time of image acquisition. The entire process is automated and independent of user input. Figure 1 and 2 As shown, the process implemented by the acquisition software 104 includes four sub-sections: Section 2.1 (synchronization module 114), Section 2.2 (signal processing module 116), Section 2.3 (logic algorithm 118), and Section 2.4 (trigger generation module 120).

[0072] Chapter 2.1 - Synchronization Module 114

[0073] The input of synchronization module 114 is synchronized with the clock of x-ray unit 108 via synchronization module 114, the input of which includes signals from 3D spatial orientation locator 300, respiratory phase sensor 110, and cardiac phase sensor 112. It should be noted that physiological biometric signals are acquired asynchronously, thus requiring synchronization. One source of this asynchronicity is that the respiratory cycle is slower than the cardiac cycle and is completely independent of it. As previously explained, the respiratory and cardiac cycles are measured using different sensors. Synchronization module 114 is configured to synchronize the respiratory and cardiac phase sensors with the acquired images. When images are captured, they display the anatomical geometry of the thoracic cavity at a given moment. These moments are recorded using a native timing system of x-ray unit 108, which does not necessarily need to be synchronized with the biometric time series of the respiratory and cardiac sensors.

[0074] It should be noted that the time mentioned alone does not distinguish between periods of irregular breathing and periods of regular breathing. In other words, if time is the only defining dimension of the breathing phase, then images taken during normal breathing and images taken during abnormal breathing (such as coughing) are computationally indistinguishable from each other. Figure 4Examples of two breaths 400 (e.g., 400a and 400b) from the same individual according to some embodiments of this disclosure are depicted, the two breaths being the same maximal inspiratory phase in time but having completely different tidal volumes. When superimposed on each other, the two breaths 400 are actually different despite having similar maximal inspiratory phases because they have different tidal volume values. In some embodiments, tidal volume refers to a volume value (e.g., in ml) measured relative to a reference volume. For example, a reference volume represents the minimum volume of the lungs during a patient's normal breathing (e.g., during maximal exhalation) (e.g., without additional effort or forced exhalation). In some embodiments, the reference volume is different for each patient. In some embodiments, the reference volume is represented as 0. In some embodiments, tidal volume is measured at a single point in time. Figure 4 The diagram shows the changes in tidal volume over a period of time.

[0075] To overcome the limitations of the time dimension, the GREX imaging system 100 defines respiratory phases using physiological values ​​acquired from various physiological sensors in the hardware box 102. These physiological values ​​are a more informative dimension of respiratory phases than time. The synchronization module 114 primarily allows for 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, discussed in detail in Section 2.2 below, during which the acquired tidal volume time series observations are used to calculate tidal volume percentiles. The acquisition software 104 uses tidal volume percentiles to define respiratory phases rather than peak-to-peak time intervals of periodic cosine waves. Due to the variation in tidal volume between breaths, the tidal volume percentiles of the acquisition software 104 are a more informative method for defining lung geometry than peak-to-peak periodic cosine curves.

[0076] Figure 5 Examples of tidal volume percentiles (two bottom subplots) for patients with regular breathing (top left subplot) and patients with irregular breathing (top right subplot) according to some embodiments of this disclosure are depicted. To quantitatively assess the tidal volume histogram for irregular breathing, the ratio between normal inspiratory tidal volume and extreme inspiratory tidal volume is used as a measure of respiratory phase. This ratio has a threshold defining the probability that the patient is breathing irregularly. Figure 5 As shown in the two bottom subplots, the vertical lines indicate the positions of the 85th, 90th, 95th, and 98th percentile tidal volumes in the tidal volume histogram. This is in contrast to irregular breathing patterns (…). Figure 5 Compared to the lower right sub-image, under regular breathing conditions ( Figure 5 (The lower left subplot) shows that the normal tidal volume percentiles (85th and 90th) are located closer to the extreme tidal volume percentiles (95th and 98th).

[0077] Figure 6 This is a schematic flowchart illustrating the synchronization process 600 between different sensors in hardware box 102 and diagnostic x-ray unit 108 according to some embodiments of this disclosure. It should be noted that hardware box 102 continuously acquires coordinates of respiratory phases defined by tidal volume percentiles, cardiac phases defined by ECG, and thoracic geometry defined by 3D spatial orientation locator 300. Before synchronization with x-ray unit 108, the sensor signals supporting these measurements need to be synchronized with each other. For this purpose, differences in measurement channels and cable resistivity between different sensors are corrected by matching trace lengths using impedance matching in the digital-to-analog converter. Because the clock of x-ray unit 108 (e.g., a timing system) is typically not synchronized with the clock of hardware box 102, connector cables are configured to connect to and interface with the clock of x-ray unit 108 via a data acquisition board. The clock of x-ray unit 108 is buffered and trace-matched for each channel in the analog-to-digital converter. The converted digital signal is passed to a field-programmable gate array, where synchronization of all signals is ensured. When the software 104 appropriately synchronizes the respiratory and cardiac phase sensor signals, the results should be similar to... Figure 7 The example shown.

[0078] To avoid unnecessary radiation exposure for the patient, the acquisition software 104 does not send any trigger signals to activate the x-ray unit 108 via the connector cables in the event that the different components of the GREX imaging system 100 are not properly synchronized. In some embodiments, a 30-second training window is used to verify synchronization between the clock of the hardware box 102 and the clock of the x-ray unit 108. Therefore, the 30-second training window should contain a sample value of 30 seconds. If the clock of each sensor in the hardware box 102 and the x-ray unit 108 does not show a sample value of exactly 30 seconds, synchronization has failed. For this reason, if this above-described checking procedure contains inconsistencies, the synchronization system will be restarted to correct the inconsistencies. It should be noted that the 30-second training window is for illustrative purposes, and those skilled in the art will understand that the length of the training window can vary as long as sufficient data is available to perform the synchronization process.

[0079] Chapter 2.2 - Signal Processing Module 116

[0080] After spatial orientation, respiratory phase, and cardiac phase signals are synchronized, software 104 processes the sensor signals to remove noise and ultimately predict the patient's accurate tidal volume. Noise in the measured lung and cardiac time series originates from sensor electronics, electrodes, and background electrical signals. A dedicated set of filters removes noise from the measured lung and cardiac time series, ensuring that the biometric time series maintains accuracy after being filtered by these filters.

[0081] In some embodiments, two different filters (e.g., wavelet filters) are used to remove signal drift and noise from biometric time series. Signal drift skews measurements acquired over time, causing inconsistencies between measurements acquired at the start and end of data acquisition. Signal noise is not physiological in nature and can cause serious problems when calculating patient airflow based on tidal volume measurements. Figure 8 This is a schematic flowchart of a drift and signal noise removal process 800 according to some embodiments of the present disclosure.

[0082] The software 104 requires a smooth tidal volume time series to calculate the first time derivative of tidal volume, such as airflow. If the tidal volume time series is not smooth, the first time derivative of tidal volume will not produce a smooth curve; instead, the curve will contain discontinuities that violate the biophysical reality of breathing. Figure 9 The illustration depicts exemplary breathing as tidal volume versus time according to some embodiments of this disclosure. Figure 10 The diagram depicts airflow and tidal volume according to some embodiments of this disclosure. Figure 9 The same respiration shown is a continuous closed loop for post-processing software 106 to use for biomechanical modeling (described in section 3.1).

[0083] The acquisition software 104 uses filtered and time-accurate time series curves to perform two different functions. The first function of the acquisition software 104 is to generate respiratory predictions using a short prediction range. Figure 11 This is a schematic flowchart of a respiratory phase prediction process 1100 according to some embodiments of the present disclosure. The short prediction range is the "look-ahead time" of the prediction algorithm. The prediction algorithm predicts the future moment when the desired tidal volume and airflow (e.g., respiratory phase) will occur. For the "desired" respiratory phase, a diagnostic X-ray unit 108 is triggered to obtain the desired thoracic geometry.

[0084] A short prediction range also reduces another source of inaccuracy in respiratory prediction, such as variations in respiratory amplitude and respiratory cycle between breaths. As the limit of the prediction range approaches zero, changes in lung geometry approach zero (e.g., lung geometry is considered nearly constant). In other words, lung geometry is unlikely to change significantly within a short prediction range. Therefore, a short prediction range reduces the impact of human respiratory variations on the predictive accuracy of respiratory motion models.

[0085] Time-accurate filtered tidal volume time series are used as inputs to the respiratory prediction algorithm. The respiratory prediction algorithm provides fast, real-time, and accurate predictions of respiratory phases. For example, the respiratory prediction algorithm is based on Autoregressive Integral Moving Average (ARIMA). ARIMA is suitable for respiratory prediction because the ARIMA model does not assume that the input values ​​are stationary and consists of polynomials. The polynomial coefficients of the ARIMA model are estimated during a 30-second training window acquired at the start of the imaging study. The number of polynomial coefficients of the ARIMA model, such as the model order, is examined by nonlinear optimization, which attempts to minimize the information criterion search function to reduce or eliminate overfitting. If the model order is optimal for the acquired training data, a tidal volume histogram is constructed (discussed in Section 2.1) and the probability density function is calculated using a log-likelihood objective function. The tidal volume distribution is used to examine irregular breathing, as discussed in Section 2.1. If irregular breathing is detected, the data is discarded and the training data is reacquired. If no irregular breathing is detected, the ARIMA model coefficients are estimated using the training data and the probability density function via maximum likelihood estimation. The 30-second training window can also be used for equipment checks before imaging. Figure 11 A flowchart for predicting respiratory phases is shown.

[0086] A second function of the acquisition software 104 is to identify cardiac phases, ensuring the heart is in the same phase in every desired thoracic cavity geometry. The duration of the prediction range is a key parameter for the acquisition software 104 in its efforts to accurately predict human respiration, as human respiration is a quasi-random function (because each breath has some unique aspects of itself). In some embodiments, the duration of the prediction range is longer than the sum of the time delay of the digital diagnostic x-ray unit 108 and the exposure time of the x-ray imaging. The sum of the time delay of the digital diagnostic x-ray unit 108 and the exposure time of the x-ray imaging is extremely short, approximately 10 milliseconds. Therefore, the duration of the prediction range is also very short (approximately 1 to 2 sensor measurement samples at operating frequencies of 100 to 1000 Hz).

[0087] When the acquisition software 104 searches for consistency between cardiac and respiratory phases, it is unlikely to align the phases represented by individual points in each time series. Therefore, imaging methods that search for single-point consistency take longer to complete because the acquisition software 104 must wait for low-probability consistency to occur. In contrast, the cardiac gating window expands the size of the consistency window, making the imaging study less time-consuming.

[0088] To further reduce computation time in the signal processing software, the software does not predict the cardiac phase. Instead, it targets specific gating windows where the heart does not cause rapid lung displacement. Figure 12This illustration depicts exemplary time-varying gating windows, selected based on the positions of the T and P waves in an ECG signal according to some embodiments of the present disclosure, that allow for slow changes in heart-induced lung motion. The dashed lines represent heart-induced lung motion. The rate of change of lung motion (e.g., velocity) is the slope of the dashed lines. When the slope of the dashed lines is small, the rate of change is also small, thus making the corresponding cardiac phase an ideal gating window. Figure 12 This shows a gating window (consistently) between the T and P waves, with a greater emphasis on the P wave. Within this gating window, lung movement caused by the heartbeat is minimal.

[0089] The preceding section discussed how to identify an ideal cardiac gating window that minimizes the physical impact of the heart on the lungs while still maintaining an opportunity window to ensure that the targeted respiratory phase aligns with the desired cardiac phase. In some other embodiments, the GREX imaging system 100 predicts, rather than gating, the cardiac phase (e.g., the cardiac phase is periodic and stable) based on signal processing differences that distinguish between the cardiac and respiratory phases. Because the cardiac phase is periodic and stable, an unsupervised multilayer perceptron using a backpropagation method can be used to predict the next heartbeat based on pattern extraction rather than a time series prediction process.

[0090] Figure 13 This is a schematic flowchart of another GREX process 1300 for real-time identification using cardiac phase prediction instead of a cardiac gating window, according to some embodiments of this disclosure. In this case, twenty seconds of the training window (20 to 22 heartbeats) are used to train the algorithm, while the remaining ten seconds (10 to 11 heartbeats) are used to validate the multilayer perceptron node weights. The node weights are iteratively determined using gradient descent optimization until the model error in the training set is minimized. If the multilayer perceptron provides poor predictions of cardiac phase in the validation data, the trained model is applied to the 10-second validation data and the node weights are recalculated.

[0091] Chapter 2.3 - Logical Algorithms 118

[0092] Known medical algorithms and systems exist for identifying the T and P waves of an ECG within a cardiac cycle. Because the cardiac phase sensor 112 continuously measures the cardiac cycle, the time interval between the T wave and the subsequent P wave (which is equal to a constant fraction of the cardiac cycle and therefore proportional to the heart rate) is also known. The ECG characteristics within this time interval can be used by logic algorithm 118 to introduce a short time lag before initiating a gating window, such that the gating window can begin, for example, midway between the T and P waves, and close after logic algorithm 118 identifies the P wave.

[0093] Figure 14Examples of triggered X-ray image capture windows corresponding to different respiratory stages of a respiratory cycle, according to some embodiments of the present disclosure, are depicted from left to right: early expiration, late expiration, maximal expiration, early inspiration, late inspiration, and maximal inspiration of a respiratory cycle. In this example, acquisition software 104 identifies at least six respiratory stages representing a single respiratory cycle. During a training period, acquisition software 104 generates a sample distribution, and logic algorithm 118 calculates the tidal volume percentiles that will define the target respiratory stage for logic algorithm 118. Figure 14 The arrows shown indicate six cardiac gating windows corresponding to the targeted respiratory phase, and the logic algorithm 118 will generate an imaging trigger signal for the targeted respiratory phase. Once the acquisition software 104 acquires the respiratory phase, it creates an automated check to prevent redundant imaging of the same respiratory phase in the future.

[0094] Figure 15 This is a schematic flowchart of an image acquisition triggering process 1500 according to some embodiments of this disclosure. Cardiac stage sensor measurements are used to identify the cardiac gating window, as previously combined... Figure 12 As described. Measurements from the respiratory phase sensor 110 are used to predict respiratory phase, as previously described. Figure 11 As described. Logic algorithm 118 identifies the consistency between the cardiac stage gating window and the predicted respiratory stage. When consistency is found, the respiratory stage list is checked to determine if the respiratory stage has already been acquired. If the respiratory stage has been previously acquired, no imaging trigger pulse is generated. If the respiratory stage has not been previously acquired, an imaging trigger pulse is generated to take an anatomical snapshot of the patient. Respiratory stage, cardiac stage, and 3D spatial orientation locator 300 measurements are recorded and labeled with images. If all respiratory stages have been acquired, the respiratory stage list is updated to prevent redundant image capture.

[0095] Chapter 2.4 - Triggered Generation (Gating)

[0096] X-ray unit 108 has a port containing a series of electrical pins. One of those pins receives an electrical pulse that defines the timing and duration of radiation exposure. Based on the respiratory phase identified within a cardiac gating window by logic algorithm 118, a trigger generator generates a square wave trigger event as an electrical pulse. An optical fiber with a vendor-specific plug accessory carries the generated trigger signal to X-ray unit 108.

[0097] Chapter 3. Post-processing software

[0098] Imaging trigger events triggered by biometric notifications recognized by hardware box 102 (section 1) and acquisition software 104 (section 2) provide better quality input (and remove inferior input) to the image reconstruction algorithm. Specifically, the quality enhancement stems from the fact that image reconstruction and image post-processing techniques are enhanced by the process of acquiring biometrically targeted images during normal breathing. The biometrically targeted action during normal breathing allows for more accurate correlation of multiple images of the same patient's anatomical geometry acquired from different angles and at different times (e.g., different breaths), as the fundamental assumptions of basic radiological mathematics presuppose anatomical equivalents across various imaging angles of the probe. The enhanced images are used as observations, and the biometric signals serve as input to a complex biomechanical model of the thoracic geometry.

[0099] Chapter 3.1 - GREX Image Acquisition Based on Digital Tomography Synthesis

[0100] Multiple imaging angles are required to reconstruct the 3D volume. In the context of GREX imaging, each angle needs to be acquired for each respiratory stage. Acquisition software 104 (section 2) generates a trigger signal that allows the x-ray unit 108 to repeatedly image the thoracic cavity with a specific geometry. The same geometry imaged at different imaging angles and during different respiratory stages constitutes a set of 2D projected images, which are used to reconstruct the 3D volume. There are many known methods for reconstructing the 3D volume from multiple 2D projected images. One such exemplary method is the convolution-backprojection algorithm, which directly reconstructs the 3D density function using a set of 2D projections called the “FDK image reconstruction algorithm,” disclosed in Feldkamp, ​​LA, Davis, LC, Kress, JW, “Using a Practical Cone Beam Algorithm” (J Opt Soc Am 1, 612-619 (1984)).

[0101] Figure 16 This is a schematic block diagram of variables used in an image reconstruction algorithm according to some embodiments of the present disclosure. Once a single projection is acquired, the x-ray unit 108 moves by an angle β, and the detector plane moves to remain perpendicular to the x-ray unit 108. In some embodiments, the patient is moved, and the x-ray unit 108 remains in the same orientation between the acquired projections. Figure 17 schematically shown Figure 3Examples of how the x-ray unit 108 and detector panel 304 move to obtain multiple imaging angles are provided. For example, the x-ray unit 108 moves from azimuth 1700a to azimuth 1700b to azimuth 1700c. The detector panel 304 moves from azimuth 1702a to azimuth 1702b to azimuth 1702c. The detector plane rotates about an axis parallel to the detector plane, and the imaging plane rotates about its parallel axis z. The orientation of a pixel in the detector plane and the corresponding pixel in the imaging plane are separated by a distance s. Anatomical information (f(x, z / y)) in the image plane ((x, z) plane) at any depth y is calculated by equation (1).

[0102]

[0103] In Equation (1), N0 is the total number of projections, β is the angle of each projection, 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., the function R is the detector readout for a given angle, p coordinate, and ξ coordinate), h is the convolution filter, and W(p) is the weighting function. Essentially, Equation (1) represents a combination of convolution, backprojection, and weighting steps.

[0104] Information at a point in the midplane (y = 0) is calculated based on projection data along the intersection of the detector plane and the midplane. The projection along a line parallel to the midplane but not in itself (constant non-zero y) intersects the detector plane to define a plane. This plane is considered as another midplane arranged at an angle. If a complete set of projections is obtained (note that "complete" means obtaining all rotation angles around the normal), the density of the angled plane is reconstructed using the Radon transform. Obtaining a complete set of projections requires the source to rotate 360° around the imaging object along a circle in the angled plane; for example, in CT imaging, a complete 360° rotation around the imaging object occurs. Note that the bold term "imaging object" means—more precisely, the bold term explicitly expresses and defines—the fundamental assumption of the Radon transform (included in the R(β,p,ξ) term of equation (1)) that, if violated, would hinder the reconstruction of the representativeness of the underlying true anatomy being imaged: "imaging object" means assuming the Radon transform input to be a single, fixed, spatiotemporally immobile, and invariant static anatomical object at different angles. GREX imaging adheres to the fundamental assumptions of the transformation, by definition and in practice, that only the biometric targeting (effectively "preselection") of the Radon transform input is accurate (even if it occurs in different respirations, the same geometrically identical respiratory phase) of the respiratory phase, because the unique short prediction range and prospective biometric targeting of GREX imaging are geometrically and anatomically and physically (e.g., precisely defined) and also practically strictly defined by GREX.

[0105] It should be noted that 360° rotation is impractical for GREX imaging because the large amount of projection required to reconstruct the 3D volume of the torso increases clinical procedure time and raises the patient's radiation dose. In practice, GREX imaging can use projection angles up to 90° within the range of -45°≤β≤45° or 0°≤β<90°. Those skilled in the art should understand that experimental testing can identify a better range of projection angles, but the theoretical range does not exceed 90°. In some cases, -45°≤β≤45° may be superior to 0°≤β<90° because -45°≤β≤45° keeps the radiation dose during the imaging procedure at a reasonably feasible low level. Compared to the 0°≤β≤90° projection angle range, X-ray photons traverse less human tissue in the -45°≤β≤45° projection angle range and can therefore be lower-energy photons, thus depositing a smaller dose, such as in... Figure 18 As shown in A and 18B.

[0106] To produce high-quality 2D images without delivering excessive radiation doses, photon energy must be high enough to partially penetrate the patient's body but not so high that it completely penetrates the patient. Thicker patients require higher photon energies than thinner patients. The human body is much thicker when |β|>45° than when |β|<45°. Generally, photon energy decreases as β→0°. In some embodiments, the GREX imaging system 100 acquires a total of 30 projections across six respiratory phases at five different projection angles, but other numbers of projections are possible depending on the specific application of the GREX imaging technique. For example, in breast tomography, the symmetrical curvature of the breast means that the breast surface is substantially equidistant from the source at all projection angles, implying that tomography is well-suited for the breast. Furthermore, the breast does not move when placed in a support, a typical clinical tomography approach that can be similar to breath-hold imaging.

[0107] By addressing lung and cardiac motion challenges through GREX-based respiratory phase geometry and GREX-based high accuracy and rapid prospective targeting (sections 2.1 and 2.2, respectively), the GREX imaging system 100 can utilize biometric surface information collected by a 3D spatial locator (section 1.1) to handle varying trunk curvature. This biometric surface information also aids in image post-processing to account for attenuating tissue density in the imaging field, thereby quantifying previously ignored attenuation sources and ultimately achieving high-fidelity image reconstruction. In summary, GREX imaging technology enables digital tomography synthesis for non-breath-holding (“dynamic”) lung and cardiac imaging.

[0108] GREX-based 3D volume reconstruction methods can be patient-specific (personalized medicine, with a personalized number of discrete angles and pairs of arcs) or used as a “universal minimum procedure time and universal minimum delivery dose” (approximately 5 discrete angles, plus or minus 3 angles, depending on the statistical reconstruction method used and the number of previous GREX-based datasets available for this person).

[0109] like Figure 19 As shown, depth resolution is low when the rotation of the x-ray unit 108 is relative to a wider range of angles. Any movement, however small, will cause image artifacts in the reconstructed image, which often leads to false positive cancer detections. However, the unique hardware box 102 (section 1) of the GREX imaging system 100 acquires biometric signals that inform the acquisition software 104 (section 2) when to capture images to make the thoracic geometry clinically identical, thereby enabling intelligent thoracic digital tomography synthesis.

[0110] In some embodiments, GREX imaging allows the projection angle to vary during a respiratory phase, while multiple projection angles captured at different time points (defined by the quantitative definition of a “respiratory phase” in GREX imaging) still all correspond to the same respiratory phase, as they are all considered to capture a single thoracic geometry. Furthermore, GREX-based tomographic synthesis allows for the acquisition of depth information, since the final photon count across all pixels of the detector and the distribution of photon counts in space at the detector surface reflect a single thoracic geometry that has been probed from multiple angles.

[0111] It should be noted that the GREX imaging technique allows the x-ray unit 108 and detector panel 304 to be mounted on a non-motorized arm or support. The role of the manual arm in the exemplary GREX imaging procedure is described below and in Figure 20 The text shows:

[0112] 1. In the β1 orientation, all six necessary thoracic geometries (also known as “GREX quantitatively defined respiratory phases”) are imaged.

[0113] 2. Next, the clinician reorients the X-ray unit 108 and the detector panel 304 to image the patient in orientation β2.

[0114] 3. The orientation of the X-ray unit 108 and detector panel 304 relative to the patient is verified by a 3D spatial locator, which allows imaging at β2.

[0115] 4. Now, at β2: If only 4 of the 6 desired thoracic geometry images are acquired during the first breath, the patient can continue to breathe normally in subsequent breaths at orientation β until the remaining 2 thoracic geometry images are acquired.

[0116] 5. Now that all six breathing geometries have been acquired at β1 and β2, the X-ray arm can be repositioned to acquire each imaging angle sequentially from (β2→β3; β3→β4; β4→β5).

[0117] 6. At the end of the program, the X-ray arm moved only 4 times, as indicated by the → arrow in the workflow (β1→β2; β2→β3; β3→β4; β4→β5).

[0118] By moving the arm only four times during the procedure, GREX imaging minimizes procedure length, the amount of interaction between the clinician and the equipment during the procedure, and the wear and tear on the X-ray arm, because for all six breathing phases (e.g., performing 30 X-ray equipment reorientations), the clinician does not interact with the X-ray arm as much as when moving the arm from β1 to β5.

[0119] In some embodiments, the 3D spatial positioning locator allows three individual elements—the patient's posture and orientation, the orientation of the x-ray unit 108, and the spatial orientation of the detector—to be separately checked for prior orientation and interlocked for safety, and allows for the orientation / alignment of each element to be consistent along the axis and relative to the other elements. This prior orientation check and safety interlocking is caused by the geometric constraints of GREX imaging on the respiratory phase. Therefore, the GREX imaging system 100 (through a prospective respiratory phase prediction algorithm) is inherently suited to provide the user with software-based safety and quality assurance controls that (in the case of safety interlocking) prevent the triggering algorithm from initiating "beam opening" if any (or both) of the x-ray arm, detector, or patient posture is improperly positioned (or inconsistent) in space at a specific angle β.

[0120] In some embodiments, since the 3D spatial positioning locator records the coordinates of all devices during the procedure, the image reconstruction technique can benefit from the (posterior) quantization of each β angle and its associated uncertainty.

[0121] Figure 21A and 21B This is an exemplary block diagram illustrating how a GREX imaging system 100, according to some embodiments of the present disclosure, reconstructs a static image cube from 2D projection data taken from each imaging angular orientation. Specifically, Figure 21A and 21B This illustrates an exemplary GREX imaging case where the coronal and sagittal views themselves form the outer limits / boundaries (e.g., projection angles) of the imaging angular azimuth. For each of the six respiratory geometries, in a single imaging angular azimuth (β... i The x-ray projection of the i-th plane is obtained at point (i). Then, the sum of the projections of the i=1 to i=n planes informs the reconstruction of the depth information in (x, y, z) of the (v1, f1) geometry probed using the projections of i=1 to i=n (Equation 1), each projection having a focal plane at a unique (compared to other projections) depth.

[0122] 2D projection data were acquired six times at each projection angle (β) for early inspiration (EI), late inspiration (LI), maximal inspiration (MI), early expiration (EE), late expiration (LE), and maximal expiration (ME). For simplicity, the projection angle ranged from 0° to 90°. The x-ray unit 108 moved to the next projection angle only after all respiratory phases had been acquired at the previous projection angle. The 2D projections were ordered according to the respiratory phases in order to reconstruct a static image cube (e.g., from β, where the thoracic geometry was detected at (v1, f1)). i =1 to β i=n (projection angle) to represent the thoracic volume for each respiratory phase. Next, the static image cube is time-interpolated using the method discussed in Section 3.3 below.

[0123] The x-ray projection acquired at each angle β is obtained using the ARIMA model to identify the target respiratory phase (Section 2.2). After acquiring the target respiratory phase, the x-ray unit 108 moves to the next imaging angle orientation. Figure 22 An example is shown where only maximal inspiration and maximal expiration are acquired using acquisition software 104. In this example, maximal inspiration and maximal expiration are imaged at β1, and then the x-ray unit 108 moves to β2 so that the maximal inspiration and maximal expiration phases can be acquired at β2. When the target respiratory phase is acquired at all imaging azimuths, the images are sorted according to the corresponding respiratory phase. Sort the x-ray images according to the respiratory phase by grouping the projections according to six biometrically defined respiratory phases. Although respiratory phases are acquired during different respiratory periods, an accurate ARIMA model ensures that the tidal volume and airflow parameters are the same between the x-ray projections captured at different projection angles. It should be noted that GREX imaging biometrically defines “the same thoracic geometry” such that the lungs are in “the same thoracic geometry” at multiple time points. GREX imaging detects the same biometric respiratory phase at different angles because the ARIMA model (Section 2.2) is a fast prediction method. The short prediction range of the ARIMA model is used to minimize the prediction error of the thoracic geometry. The relevant equivalent of depth information at each individual angle (in general, used to generate realistic structural depth information) depends on the equivalent (consistent, within approximate tolerance) of the thoracic cavity geometry across different detection angles. Thus, the accuracy of respiratory phase prediction ensures successful image reconstruction.

[0124] The projection of the respiratory phase order is used to create static image cubes using the canonical FDK image reconstruction algorithm (or a similar cone-beam geometry image reconstruction algorithm) given in Equation (1) previously discussed. The image reconstruction algorithm uses the projection of the respiratory phase order and creates respiratory phase-ordered static image cubes. Each respiratory phase will have a separate image cube. The static image cubes are called static because they represent only the anatomical structures in one respiratory phase. The static cubes representing all the respiratory phases are combined along with temporal interpolation (described in Section 3.3) to create a 3D image cube image from the 3D static image cubes.

[0125] The GREX imaging system 100 maintains radiation dose at a reasonably achievable minimum through statistical image reconstruction. Each acquired image increases the overall dose of the imaging process (a clinically undesirable outcome) but provides additional information for image reconstruction (a clinically desirable outcome). Traditional forms of image reconstruction based on Fourier transform or filtered backprojection tend to exhibit image artifacts because they cannot handle missing information (e.g., missing projection angle β). For example, if projection is performed in 10° increments instead of 5°, half the amount of information would be available to create a static image cube, but the former would only deliver half the dose compared to the latter. Statistical iterative image reconstruction addresses the missing information caused by incomplete image datasets.

[0126] Many existing statistical image reconstruction algorithms exist, which the GREX imaging system 100 can use to perform image reconstruction tasks (e.g., constructing static image cubes). However, the GREX imaging system 100 improves upon conventional statistical image reconstruction algorithms by implementing a unique feedback step and adhering to boundary conditions based on the law of conservation of mass.

[0127] The fundamental principles of physics can be applied to statistical image reconstruction in GREX because GREX images are biometrically defined by biophysical quantities governed by physical laws. By biometrically labeling each image and the resulting image cube, and by acquiring a continuous stream of biometric data during the procedure and even when the patient is not being imaged, the respiratory dynamics of mass exchange (inspiration and expiration) and volume changes can be understood (making it possible to resolve the consistent, invariant lung tissue mass throughout the scan). The law of conservation of mass can be applied because the static image cube can initially be composed of moving organs such as the lungs. This is attributed to the speed and accuracy of prospective prediction / triggering algorithms that accurately label and acquire the same thoracic geometry at different times (sections 2.2 and 2.3). In other words, the tissue mass within the static image cube should not change from one static image cube to the next (e.g., it does not change due to the law of conservation of mass). According to the ideal gas law, the ratio of room temperature air to lung air is 1.11. Given the tidal volume of the image cube from sensor data, combined with a 1.11 ratio at room temperature and a mass / volume air deviation curve, the mass of inhaled air can be determined (calculated in absolute and relative terms as a ratio between image cubes of two different breathing phases).

[0128] GREX imaging is highly useful based on mass-conserving boundary conditions because the presence of air, for example, can artificially darken voxels, thereby adversely affecting the ability of statistical image registration algorithms to accurately determine object density. By correcting for air volume differences in the acquired projection and ensuring that isolation remains constant throughout the scan (e.g., lung tissue mass), GREX imaging produces more accurate image reconstructions to generate static image cubes.

[0129] Consider two GREX projections obtained from different imaging angles but with the same biometrically defined respiratory phase. The air volume in the lung is the same, but the way air displaces tissue may differ between the two projections, causing the nodules in the second projection to be darker. The second projection is affected and erroneous compared to the first projection. This error results in a piece of tissue (“nodule”) visible in the first projection being invisible in the second, ultimately dulling the intensity of the “nodule” in the resulting image cube (or causing it to be mistaken for background). The mass conservation law boundary conditions of GREX imaging are implemented as a feedback step, which checks the lung mass conservation between the aforementioned image cube confirming the erroneously darkened nodule and image cubes from later respiratory phases that (correctly and anatomically represent the brightness of the “nodule”). The feedback step of GREX imaging corrects the level of the second projection of the erroneous image cube by updating the expected geometry during reconstruction based on a simulation using the first projection as the highest criterion. In this way, GREX statistical image reconstruction will produce a more accurate static image cube.

[0130] In addition to pre-existing statistical image reconstruction algorithms, GREX's post-processing software 106 also incorporates edge-limiting filters (discussed in Chapter 3.2), spatial boundary conditions (discussed in Chapter 3.2), and smooth transitions between respiratory phases (discussed in Chapter 3.3) into digital diagnostic X-ray images to improve anatomical imaging.

[0131] Chapter 3.2 - Image Filters

[0132] The quality of digital diagnostic X-ray images depends on the X-ray unit 108 setup and the anatomical site of study. Each patient and anatomical site has a different electron density through which X-rays pass to generate an image. For example, imaging the femur requires higher X-ray energy than imaging the thoracic cavity, because the lungs are primarily composed of air, while the femur is composed of bone. Given that higher-energy X-rays penetrate the body to a greater extent than lower-energy X-rays, the amount of X-rays exiting the body and reaching the flat panel detector differs for high-energy and low-energy X-rays imaging the same anatomical geometry. Too many X-rays exiting the body can cause overexposure of the flat panel detector, similar to overexposure in optical radiography. If the X-ray unit 108 setup is not optimal for the anatomical site of study, image quality will be significantly degraded. In clinical practice, commercial vendors have designed imaging protocols for their digital diagnostic X-ray units that roughly estimate the optimal X-ray unit setup for selected anatomical sites. However, these rough estimates of optimal tube settings are not tailored to address the potentially significant anatomical differences between different patient locations (e.g., the stomach of an overweight man versus the stomach of an average-weight man). In fact, existing imaging protocol settings from vendor groups, as rough estimates, rarely produce optimal quality images.

[0133] If the optimal X-ray unit settings or other imaging parameters are unknown before imaging (as is currently the case in the medical field), a strategy of deploying digital image filters can improve image quality for suboptimal X-ray unit settings. The improved image enhances the visibility of anatomical features that are not readily visible to the human eye. For example, in the coronal plane, every rib may not be visible in a digital diagnostic radiograph. Post-processing software 106 filters the coronal plane image using edge enhancement filters (such as Laplacian filters), thus displaying the boundaries of all ribs in the resulting image even when the rib boundaries in the original image are too subtle to be detected by the human eye (e.g., a radiologist's eye). Post-processing software 106 overlays the filtered image with the original image, which highlights the enhanced (e.g., post-filtered) and previously invisible rib edges on the original image. Image filters available for user application include Laplacian filters, Hanning filters, Butterworth filters, Parzen filters, Wiener filters, Metz filters, Ramp filters, nonlinear spatial mean filters, and hybrid filters.

[0134] In some embodiments, post-processing software 106 uses skin surface measurements from the 3D spatial orientation locator 300 to calculate optimal imaging parameters for images captured at each breathing phase. As a patient breathes, the body's electron density changes with increased air intake and chest circumference. Increasing the patient's diameter, decreasing the distance between the patient and the x-ray unit 108, and decreasing the distance between the patient and the detector panel 304 introduce additional image noise into the resulting x-ray images. The 3D spatial orientation locator 300 tracks the patient's skin surface orientation relative to the x-ray unit 108 and the detector panel 304 for each image. This skin surface orientation tracking provides unique measurements for digital diagnostic x-ray studies.

[0135] The field of digital diagnostic radiology currently relies on scaling dose index readings measured in the ionization chamber to approximate the patient's body mass index. Radiographers currently perform only two measurements: the first for a solid water cylinder phantom of equivalent tissue density, 16 cm in diameter, and the second for a phantom of the same material, 32 cm in diameter. X-ray unit 108 has a vendor-defined, “one-size-fits-all (all patients)” built-in protocol specific to a particular anatomical location. For example, regardless of the patient's chest cavity diameter, the vendor provides only a single protocol with built-in imaging settings for the technician to choose from. In other words, a male with a large chest receives the same imaging settings as a male with a small chest.

[0136] The 3D spatial orientation locator 300 of the GREX imaging system 100 generates real-time and personalized measurements of the patient's chest cavity diameter. These measurements inform the technician to select personalized X-ray unit settings for the patient. The chest cavity diameter changes as the patient breathes. This changing chest cavity diameter prevents the technician from optimally setting imaging parameters to match the patient's chest cavity diameter. Furthermore, the real-time and personalized measurements of the patient's chest cavity diameter can be used to remove image noise in post-processing and to simulate X-ray images obtained with optimal imaging parameters.

[0137] Figure 23This is a schematic flowchart of an image filtering process 2300 according to some embodiments of the present disclosure, which computes a noise-free lung image and simulates an image obtained with optimal X-ray unit settings. After acquiring the image as described in Section 2, the trachea is identified using the line profile of a line segment that runs laterally (from left to right) through the neck region. The neck consists of muscles, bones, and arteries, but the trachea stands out more than all other tissues because it contains only air, which has a significantly lower density than the tissues. The line segment will show where the air is located, and will identify small regions containing pixels designated as air. Image noise in the X-ray image is computed by subdividing the entire image into smaller blocks. Gaussian noise is estimated independently for each block, and the block with the minimum noise level is used for texture mapping. The texture mapping technique uses a gradient covariance matrix to estimate the initial texture level in each block. The block with the minimum initial noise level is re-estimated for noise level through an iterative process that continues until the noise estimate of the block converges through additional iterations of the gradient covariance matrix. It is assumed that the weakly textured block is located in air, where the air is located away from the patient, such as the upper corner of the X-ray image. The noise level estimation of the block provides a baseline noise level for the entire image. Then, the baseline noise level identified by the iterative gradient covariance matrix is ​​subtracted from the entire image to obtain a noise-free air density estimate in the trachea.

[0138] Post-processing software 106 overlays a human skeleton model (scaled individually for each patient) onto a surface orientation estimate provided by a 3D spatial orientation locator 300 to estimate the initial location of the lungs. The individually scaled patient skeleton is rigorously registered to the X-ray image using visible landmarks on the skin surface (e.g., clavicle, rotator cuff, scapula, vertebrae, etc.). After rigorous registration to the X-ray image, the skeleton provides the orientation of the ribcage. The ribcage itself provides boundary conditions for the pixel values ​​of the lung edges and noise-free air density estimates near the trachea. The lung edges (via the ribcage location) and the pixel values ​​of the noise-free air density estimates near the trachea are automatically identified as seed locations using a region growing algorithm (the region growing algorithm starts from these locations and then grows radially outwards). Region growing is a region-based segmentation method. This segmentation method first identifies a set of initial seed points within the image, then examines the neighboring pixels of the initial seed points and determines whether the neighboring pixels should be added to the region. This process is repeated in the same manner as general data clustering algorithms. In other words, the region growing algorithm uses the initial placement of seed pixels to expand outwards using a statistical process of consuming "similar" pixels. The region growing algorithm will continue (consuming similar pixels) until the identified pixels are statistically dissimilar to the consumed cluster.

[0139] In practice within the GREX imaging system, the region growing algorithm "stops" (e.g., detects pixel dissimilarity) at important anatomical landmark interfaces (e.g., the lung defined by dense pixels of the ribcage). Pixels not identified by the region growing algorithm as belonging to lung tissue are masked (the masked image is defined as an image that enhances the structure once subtracted from the original image) to form two separate images. These two separate resulting images are (i) the segmented lung and (ii) the remaining body tissue. To provide radiologists with accurate and noise-free segmented lung volumes (e.g., lung volumes not visually obscured by non-lung tissue) that provide improved diagnostic visibility, tissues associated with body occlusion (e.g., non-lung, and therefore not visually informative) are removed from the lung images. For example, pixels belonging to the intercostal muscles within each simulated imaging ray projection are completely subtracted from the segmented lung images. In addition to the aforementioned subtraction of body occlusion from the lung images to produce improved lung tissue visualization, body occlusion can also be used to provide a second examination of the patient's surface orientation calculated by the 3D spatial orientation locator 300. For example, post-processing software 106 calculates the number of pixels identified as body occlusion by the region growing algorithm, and then calculates the body diameter at various locations along the height of the torso. This body diameter calculation should be highly consistent with the estimate of the patient's body diameter by the 3D spatial orientation locator 300. If not, it may indicate that the 3D spatial orientation locator 300 needs to be recalibrated to improve its accuracy.

[0140] If clinical users require a more accurate view of the body occlusion (e.g., for clinical or educational reasons), the optimal X-ray unit settings are used to simulate the body occlusion image, thereby removing noise patterns and potential sources of artifacts from the body occlusion. The body occlusion and segmented lungs can then be recombined to form an artifact-free X-ray image with global enhancements tailored to clinical applications (e.g., structural configuration).

[0141] Chapter 3.3 - Biomechanical Modeling

[0142] A biomechanical model used in post-processing software 106 is created based on the first principles of physics, namely the law of conservation of mass and the ideal gas law. The goal of the biomechanical model in post-processing software 106 is to identify biophysical quantities that enhance clinicians' ability to diagnose diseases. These relevant biophysical quantities include, but are not limited to, stress and strain of lung tissue elements.

[0143] A mechanical system under heavy load will generate stress. In the case of the lungs, the elements of the mechanical system are represented by lung tissue. The lung tissue visible and distinguishable in medical imaging consists of parenchyma (containing alveolar sacs, alveolar walls, bronchi, and blood vessels). Parenchyma is directly responsible for lung function. Suitable tissue elements for biomechanical modeling should be small enough to be internally homogeneous, but statistically stable in response to respiratory stimuli. Typical voxel sizes in lung medical imaging range from 1 mm. 3 Up to 3mm 3 This corresponds to 125 to 375 alveoli. The voxels are considered to be nearly homogeneous in density and contain enough alveoli to provide a stable response to respiratory stimuli. The alveoli are arranged in a hexagonal array, inflated by the expansion normal stress from the shared alveolar wall. The sum of all expansion normal stresses within the lung tissue element provides an estimate of the pressure experienced by the alveoli and induced by respiratory stimuli. The expansion stresses are balanced by the elastic stresses on the alveolar walls, and a balance exists between the two stresses when the airflow through the tissue element is zero. The spatial translation of the lung tissue element's orientation in any direction due to varying tidal volumes can be modeled by the material response of the tissue element to the normal stresses on the corresponding surfaces of the lung tissue element. In other words, the biomechanical model contains vectorized terms describing the response of the lung tissue element to increased tidal volumes, where these vectorized terms relate to normal stresses.

[0144] Strain is defined as the response of a mechanical system to force. From the perspective of material elements (e.g., tissue), stress is a deformation force, while strain is a restoring force. The stress vector on the surface of a lung tissue element contains two components: (i) normal stress (related to the outward or inward directional movement of the lung tissue element, causing expansion or contraction, respectively), and (ii) shear stress perpendicular to the normal stress and caused by pressure imbalance induced by airflow. It should be noted that, by definition, the perpendicular shear stress component does not promote changes in lung volume. A graphical illustration of the relationship between normal stress / shear stress and tidal volume / airflow is available in […]. Figure 10 The "Airflow and Tidal Volume" diagram depicted in the figure shows that exhalation occurs at the lowest tidal volume value where airflow is zero (the leftmost point on the curve). During inhalation, airflow is positive, and tidal volume increases (the upper part of the curve). As airflow decreases, tidal volume reaches its maximum value. As tidal volume approaches its maximum value (the rightmost point on the curve), airflow decreases sharply, and the lungs gradually begin to expel air. As more air is expelled from the lungs, tidal volume begins to decrease. When airflow is negative, tidal volume decreases and continues to decrease until tidal volume reaches residual volume and airflow is zero (e.g., exhalation). In summary, the movement of lung tissue elements is defined by tidal volume and airflow respiratory stimuli. The movement of lung tissue elements is caused by normal and shear stresses acting on the elements. Changes in the shape of lung tissue elements (e.g., compression, elongation) are not modeled by stress; in fact, changes in the shape of the elements are modeled by strain.

[0145] The model creates a new diagnostic perspective in the medical field because medical analyses using the model are controlled by quantitative force analysis. The balance of stress and strain forces on each voxel in the lung fundamentally assesses lung health because functional behaviors of the lung (e.g., lung tissue movement) can now be visualized and analyzed from a clinical perspective.

[0146] The first step in building the model is identifying which tissues are part of the lungs and which are not. To accomplish this, GREX imaging technology acquires multiple snapshots of the thoracic geometry in various configurations (six different respiratory phases). The air density of the trachea is used as the initial starting point for a region growing algorithm, which segments the lungs based on this algorithm. The GREX imaging system 100 uses two different deformable image registrations to construct the biomechanical model: one for the lung tissue and the other for non-lung tissues (e.g., chest wall, ribs, liver, heart, trachea, esophagus, etc.). The results of the region growing algorithm distinguish between lung and non-lung tissues prior to the deformable image registration step.

[0147] The rationale behind using two different deformable registrations (one for the lungs and one for the non-lungs) in the GREX imaging system is that the motion and material properties of the lungs differ from those of the non-lungs. If only one deformable image is registered for both the lungs and the non-lungs as a whole, the registration will assign far more weight to the non-lung tissue compared to the lung tissue. This would result in the lung tissue being assigned an unrealistically low weight and therefore an unrealistically limited degree of motion. GREX imaging is designed to visually elucidate the nuances of lung motion, including the nuances of motion across the computationally complex lung surface.

[0148] An optical flow algorithm is a deformable image registration algorithm that can track tissue movement between images. Figure 27 This is a schematic flowchart 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, which identifies structures in two different images based on the brightness or contrast of each structure, or both. For this purpose, given that non-lung tissue contains characteristically brighter (e.g., at least 10 times denser) anatomical structures (e.g., ribs, pectoral muscles, sternum, etc.) compared to lung tissue, using a single 3D multi-resolution optical flow algorithm for both non-lung and lung regions would result in the lower-density lung tissue being artificially de-prioritized in the algorithm. The algorithm's computational resources would be preferentially allocated to the brighter contrast structures (e.g., ribs, etc.) present in non-lung tissue. This result, where computational resource allocation preferentially ignores lung tissue dynamics, hinders the purpose of GREX imaging to intuitively elucidate the nuances of lung tissue movement.

[0149] Since prioritizing resource allocation for high-contrast structures is inherent in 3D multi-resolution optical flow algorithms, the task of deformable image registration within the thoracic cavity is divided into two separate ancillary tasks (e.g., two more homogeneous internal regions): (i) image registration of the lungs, and image registration of the non-lungs. In some embodiments, to resolve the two separate task regions from the overall thoracic cavity, it is necessary to identify the lung surface (e.g., the boundary between the lungs and non-lungs) prior to 3D multi-resolution optical flow deformable image registration. It is essentially possible to identify the lung surface using a region growing algorithm that begins in an air-filled (e.g., visually dark) region within the lung, grows outward toward the lung surface boundary, and achieves high pixel contrast at the lung surface boundary. Performing the region growing algorithm is the first step.

[0150] The lungs are not attached to the chest wall. Therefore, lung movement is relatively independent of thoracic cavity movement. In other words, instead of the predictable push-pull response at the lung surface boundaries, other types of tissue dynamics are at play. For example, a horizontally adjacent thoracic voxel moves vertically downwards, thus the lung voxel moves horizontally into the space previously occupied by the thoracic voxel.

[0151] To accurately model complex dynamics, the GREX imaging system 100 quantifies the shear forces experienced by the surfactant layer at the lung surface boundary. Two separate segmentations for lung and non-lung tissue provide the basis for force estimation. The force estimation procedure is performed (for each reconstructed respiratory stage image) by subtracting segmented lung pixels from the X-ray image (e.g., removing their assignment values). The previously segmented lung is masked from the original image, which provides an image containing all remaining tissue. The segmented thoracic geometry of each individual image must be registered to each thoracic geometry in the other images to know the orientation of each lung tissue element across all images. A multi-resolution optical flow algorithm performs image registration by computing a displacement vector field that shows the displacement of each pixel between two images taken at different respiratory stages. In effect, knowing the displacement vector field allows for accurate spatial calculations of all lung tissue elements within the thoracic geometry. The difference between the segmented lung registration displacement vector field and the non-lung tissue displacement vector field provides the magnitude and direction of the shear forces existing between the lung and chest wall.

[0152] Based on observations of over 150 unique patients, 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 the displacement vector in coordinate space for each measured tidal volume and airflow value. When the displacement vector is calculated for all respiratory phases, the result is a closed-loop trajectory (e.g., ...). Figure 10As shown below, the observations are used as a biomechanical model. As described in more detail below, the biomechanical model parameters—which may include parameters representing normal stress associated with tidal volume, normal stress associated with airflow, and shear stress associated with airflow—are solved using QR decomposition for each lung tissue element. The parameters are specific to each lung tissue element (e.g., each lung tissue element has a unique solution) and collectively describe the lung tissue element's response to respiratory stimuli. The biomechanical model parameters are vectors globally scaled by the measured tidal volume and airflow (e.g., tidal volume and airflow are scalar values). Relationships between the biomechanical model parameters, such as the angle between two (vectorized) parameters, can aid in the diagnosis of potential morbidities in the lungs. Based on the unique biomechanical model vector parameters of the lung tissue elements (e.g., each tissue has different vector parameters), the displacements of the lung tissue elements are scaled to the thoracic geometry, which is defined by tidal volume and airflow measurements. The biomechanical model can be computed for each patient or shared by multiple patients.

[0153] In some embodiments, the biomechanical model roughly estimates the motion of lung tissue as a function of several factors, including the tidal volume of the lung (T). v ), airflow (A f ) and cardiac phase (H c These values ​​are global values; for example, the cardiac phase is the same for all tissue elements in the chest cavity. Global values ​​are treated as scalar numbers and measured using the hardware discussed in Chapter 1. Note that tidal volume, airflow, and cardiac phase are all time-varying measurements. Vectors are used. and The unique stress and strain values ​​for each structural element are expressed mathematically using the following equations:

[0154]

[0155] in A vector describes the normal stress caused by moisture. Describe the normal stress caused by airflow. Describe the shear stress caused by airflow, and Describes tissue motion caused by perturbed cardiac motion. In general, it describes the tissue displacement at any point in the closed-loop trajectory. It is expressed as the sum of stress, strain, and perturbation cardiac motion vectors scaled proportionally by tidal volume, airflow, and cardiac phase, respectively.

[0156] Figure 24 This illustration depicts an exemplary closed-loop lung tissue motion trajectory, partially caused by the interaction between the heart and lungs, in a section of the left lung near the heart, according to some embodiments of this disclosure. It should be noted that... Figure 24The wave-like behavior described in the text originates from the interaction between the heart and lungs. Figure 24 This is a schematic block diagram illustrating how the orientation of a lung tissue segment moving in a closed-loop lung tissue movement trajectory can be determined based on the above-described biomechanical model, according to some embodiments of the present disclosure. Figure 24 This demonstrates how to add together the 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.

[0157] The main advantage of using physiologically based biomechanical models to interpolate images between acquired respiratory phases is the ability to use quantitative physical quantities to examine the accuracy of the biomechanical model's output. According to the ideal gas law, at room temperature, the ratio of lung volume change to tidal volume is 1.11. In other words, the ratio of room air density to lung air density is 1.11. Therefore, the volume integral of the divergence of the normal stress vector should be 1.11 (e.g., Where V is the total body volume). The ideal gas law provides a "soundness check," thereby generating applicable quality assurance information about the 3D spatial orientation locator 300, the respiratory phase sensor 110, and each interpolated image.

[0158] An example of how biomechanical modeling in GREX imaging can enhance diagnosis is early-stage lung tumors that are invisible to the radiologist's eye during imaging examinations. The radiologist cannot see the tumor because it is too small for the imaging sensitivity. Although the radiologist cannot see the tumor, its presence still affects the balance of forces within the lung because the tumor's electron density is greater than that of healthy lung tissue. The higher electron density of the tumor implies that it possesses different material and mechanical properties (e.g., different characteristic stress and strain parameters), which influence the movement of the tumor and the surrounding local area (e.g., healthy tissue near the tumor site). The effect of a tumor on local healthy lung tissue can be roughly analogous to the effect of mass on the spacetime continuum under general relativity: when a massive object is present, spacetime curves around the object, causing light to behave differently near the object compared to when there is no mass. The same analogy applies to the lung, causing the tumor to distort the trajectory of adjacent healthy tissue, making it move in a different way compared to the trajectory of a healthy lung. The displacement vector mapping of the biomechanical model makes changes in lung tissue composition and biomechanical properties readily apparent to medical practitioners: when a tumor is pre-defined, the displacement vector field exhibits unnatural degrees of vector curling and / or other altered properties. The post-processing software 106 for GREX imaging technology, discussed in Section 3.4, visually displays such diagnostically important information—previously unseen by the end user—through the creation of parametric maps. Parametric maps of GREX imaging technology are an example of the new diagnostic perspectives that the GREX platform brings to medicine.

[0159] Figure 25 This is a schematic flowchart of operation 2500, which is a component of a biomechanical model according to some embodiments of the present disclosure.

[0160] The biomechanical modeling process provides a quantitative means for biometric interpolation between two images (2D or 3D) acquired at different respiratory stages. Equation (2) shows that the solution of the biomechanical model is the displacement between the two respiratory stages. As described above, the displacement between the two breathing stages is found by performing deformable image registration to interindex the two breathing stages. It typically includes the following three steps:

[0161] Step 1: Perform region-based segmentation to delineate the structural boundaries between lungs and non-lungs.

[0162] Step 2: Use 3D multi-resolution optical flow to perform intensity-based structure mapping to match “identical” structures in two separate image cubes.

[0163] Step 3: In some embodiments, the displacement between the two breathing phases is iteratively refined. The initial estimate is used until it is optimized.

[0164] Figure 26A This is a graphical depiction of the movement trajectories of lung tissue elements during the respiratory cycle, according to some embodiments of this disclosure. For example... Figure 26A As shown, each imaging angle contains 6 images, corresponding to the six respiratory stages of a complete respiratory cycle, from left to right: early inspiration (EI), late inspiration (LI), maximal inspiration (MI), early expiration (EE), late expiration (LE), and maximal expiration (ME), as follows. Figure 14 The image depicts the displacement vector of a particle, defined by the finite strain theory, where the orientations of particles in undeformed and deformed configurations are joined. Using a specific voxel within the EI image cube as a reference, six images illustrate the deformation of the reference voxel from its original position, size, and shape during the respiratory cycle. As the lungs fill with more and more air, the reference voxel begins to “bubble.” In other words, as the voxel “bubbles,” the lung problem corresponding to the reference voxel deforms. Displacement between two respiratory phases. The degree of voxel bubbling, or the corresponding deformation of lung tissue, is quantified. Deformable image registration assumes that a reference voxel in the EI image cube moves to its new orientation in the LI image cube, while simultaneously deforming due to increased air intake into the lung tissue. As the respiratory cycle moves forward, the reference voxel maintains its trajectory, as shown in the MI, EE, LE, and ME image cubes, respectively. In other words, for each voxel in the reference image cube, a set of displacement vectors is computed across all six image cubes.

[0165] Figure 26B Five deformable registrations (2→1, 3→1, 4→1, 5→1, 6→1) exist between the EI image cube and five other image cubes. These six image cubes represent the thoracic anatomy of the patient at six corresponding predefined respiratory stages. For each voxel, there exists a vector at the image cube corresponding to each of the six respiratory stages. Assuming the vector corresponding to the cube in the EI image is zero, then the six displacement vectors of a given voxel can be expressed as:

[0166] Or expressed as

[0167] The parameter "n" is a time-related parameter corresponding to the corresponding respiratory stage.

[0168] Similarly, for a specific respiratory phase "n", this includes the tidal volume of the lungs (T). v ), airflow (A f ) and cardiac phase (H c The biometric data matrix can be expressed as:

[0169] Or represented as [B] n ].

[0170] For each voxel in the image cube, the parameter matrix The biomechanical model can use six displacement vectors The corresponding biostatistical data matrix described above Solve. The four vectors describe the organization properties of the voxels, which control the displacement vectors and deformations of the voxels. For each voxel in the reference image cube, we have:

[0171]

[0172] As noted above, there are many deformable image registration algorithms capable of performing image registration for 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 obtained at different observed tidal volumes. The displacement between the two respiratory stages is calculated. supply Figure 24 The observation points in the tissue trajectory shown. Once the parameters of the biomechanical model are... Solving by, for example, least squares regression, changes in tidal volume (T) v ), airflow (A f ) and cardiac phase (H c This generates the entire closed-loop trajectory. Applying the closed-loop trajectory to all tissue elements in the thoracic cavity yields a new image cube that can be considered as biometrically interpolated. In summary, Table 1 shows each component of the GREX biomechanical model and how those components were found.

[0173]

[0174] Post-processing software 106 fills in all potential respiratory stages through biometric interpolation between the acquired image cubes, thereby creating a complete thoracic image. Generally, at least 30 simulated images are required for a smooth transition between frames. Figure 28A This is a schematic flowchart 2800 illustrating the creation of images using biometric interpolation based on some embodiments of this disclosure. Figure 28B As shown, the biometric data matrix corresponding to the EI image cube is assumed to be:

[0175] T v =20ml, (A) f ) = 20 ml / s, and H c =0.10.

[0176] For each voxel in the intermediate simulated image cube at a specific moment in the respiratory cycle, such as EI+Δt, EI+2Δt, EI+3Δt, etc., a parameter matrix can be used. The calculation is performed using a biomechanical model and a corresponding biometric data matrix at a specific time point.

[0177] Chapter 3.4 - Parameter Diagram

[0178] One clinical benefit of GREX imaging technology is its unique parametric maps. Using 2D maps, 2D colorwashes, 3D maps, and 3D vector field mapping, GREX imaging technology presents end-users with previously unavailable information about a patient's thoracic health.

[0179] Figure 29Examples of standard radiographs depicting healthy patients (left) and standard radiographs of patients with stage 1b left upper lung tumors (as indicated by the arrows). Both figures show standard radiographs currently used in the field of radiography. Although Figure 29 The lesion on the right side of the left lung is an early-stage lung tumor, but it is not easily visible on standard X-rays because standard X-rays are difficult to interpret and only show anatomical information. In contrast, Figure 30 The accompanying GREX parametric plots according to some embodiments of this disclosure illustrate health indicators for the same two patients. The parametric plot on the right clearly indicates a lesion in the patient's left upper lung, while a standard radiograph is blurry. Changing the window level and image contrast ultimately reveals the poorly ventilated area in the left upper lung. However, the chance of detecting such early-stage lung cancer tumors is low if the end user does not take these steps. In other words, the parametric plots generated by GREX can significantly reduce the variation in the risk of disease underreporting by the end user.

[0180] First consideration Figure 30 In A 2D color image depicting the normal stress related to moisture content. Based on By definition, end users will expect that, compared to the apex of the lung, the area near the diaphragm... The magnitude will be larger (for example, when tidal volume increases, the diaphragm has a larger tissue displacement than the apex of the lung). In general, The value varies steadily throughout a healthy lung. If we calculate the value of the entire lung... The gradient will result in a smooth function. Figure 30 The example described in the text shows that, for these two patients, although The parameters are distributed in a similar manner, but the tissue displacement in healthy patients is twice that in diseased patients.

[0181] Next, consider the sum of normal and shear stresses associated with airflow. The 2D brush color. Generally, higher parameter values ​​appear near the area of ​​the bronchial tree that carries air into the lungs at a faster rate (the middle of the lung). However, in diseased lungs, the presence of a tumor can substantially alter the elastic behavior of the lung, making the tumor visually distinguishable from the distribution of healthy lung tissue. Figure 30 In the example shown, the presence of a lung tumor is clearly visible in the left lung due to the significant difference in magnitude. When a disease affects a region of the lung, other lung tissue regions "clean up the mess" by exchanging gas with healthy areas rather than diseased areas. However, as... Figure 29As shown in the left-hand image, the healthy lung is more elastic than the diseased lung. In other words, airflow resistance is significantly increased in the location of the tumor, thus highlighting the presence of the tumor and enabling quantitative analysis of its impact on the patient's ability to adequately exchange gases during respiration. The ratio between parameters associated with tidal volume and parameters associated with airflow significantly reveals a pattern in the left upper lung that is markedly different from that in healthy patients. Fundamentally, with Figure 24 Compared to the example tissue trajectories shown, a higher percentage is interpreted as tissue moving in a more rounded pattern. The amount attributed to moisture is located beneath the 2D brush color. and airflow The histogram of the observed total motion components also shows a clear indicator of disease in the left lung. A healthy lung has... The distribution is bimodal, but the overall distribution pattern differs between the two lungs when disease is present in one lung. GREX imaging techniques can involve Bayesian processes to better classify disease etiologies by combining the distribution of parametric histograms from patient interviews (information voluntarily provided by the patient prior to examination) with biomechanical models. In summary, lung diseases (e.g., the presence of tumors) can be classified across multiple new GREX parameters. Figure 30 Consistent indicators (as shown) greatly aid in early detection and disease diagnosis.

[0182] Chapter 3.5 - Diagnostic Indicators

[0183] Lung cancer, chronic obstructive pulmonary disease (COPD), lower respiratory tract infections, and tuberculosis all have disease indicators visible in current digital diagnostic X-ray imaging. However, these indicators are not always visible in the early stages of the disease. Figure 29 and 30 As shown, GREX parameters have the potential to be used as diagnostic indicators in the early stages of disease. Lung cancer may go undetected if medical teams rely solely on standard X-rays because the tumor is not large enough to be clearly visible on standard X-rays. GREX imaging uses biometric signals combined with multiple biometrically targeted X-rays to utilize previously unavailable information about how the lungs move to diagnose the disease. The GREX procedure directly addresses the problem of early-stage lung cancer, which often has poor specificity in digital diagnostic X-ray images. It overcomes the limitations of existing digital diagnostic X-ray-based diagnostic sensitivity by using patient-specific and disease-indicating respiratory information provided biometrically in imaging. Clinicians can observe diagnostic depth as early as disease onset through lung tissue motion visualization and parametric mapping, and continue to monitor patients as the disease progresses. Machine learning and finite element analysis techniques will be used to discover subtle patterns or baseline changes in lung function, assisting end-users in detecting the disease at its onset, thus providing patients with higher survival rates and more treatment options.

[0184] Another example of GREX's impact on the diagnostics field is the classification of lung disease etiologies. In biopsies, lung diseases caused by asbestos and those caused by smoking present differently. Asbestos is a natural silicate mineral composed of long, thin, fibrous crystals made up of millions of microscopic fibers. Inhaled asbestos fibers penetrate the alveoli and eventually form a dense network that impairs alveolar function, thereby reducing lung function. This dense network aggregates cancerous tissue and is known as mesothelioma. Standard radiographs can only identify mesothelioma by the accumulation of plaques, which appear as dense consolidation (blurring) in lung images. Before the end user can visually identify the presence of asbestos, GREX can detect subtle signs of asbestos through parametric maps. As an asbestos network forms, the elasticity of the lung locally decreases. Small localized reductions in elasticity can be seen in parametric maps (2D brush color, parameter ratio, and histogram). For example, in tissue that has lost its elasticity, the trajectory of the tissue during breathing will be more rounded than an ellipse. This means that… and The ratio between the values ​​will be higher than in healthy tissue. Consulting the color chart will clearly show the lung areas displaying patterns indicating disease.

[0185] Inhaled particles (non-fibrous) will appear in a completely different way than fibrous asbestos particles. Inhaled particles deposit in the lungs, reducing lung function by "blocking" the airways, forming scar tissue, and creating tumors rather than a network. GREX will show the onset of particle deposition by detecting tiny, localized changes in lung motion dynamics that are inconsistent with healthy characteristics. Cigarettes contain tar and radon, which adhere to the alveoli and radically alter lung function. GREX tracks reduced lung function and provides end-users with a unique tool to better demonstrate the destructive extent of smoking habits to patients. COPD is another disease that can be elucidated more effectively through parametric mapping than current clinical methods. Currently, COPD is detected through spirometry and standard radiography. These methods are not particularly sensitive and are undetectable in the initial stages of the disease. Detecting the disease as early as possible gives patients more opportunities to utilize preventative medicine and change unhealthy habits before it is too late.

[0186] Chapter 3.6 - Visual Presentation

[0187] The image rendering graphical user interface (GUI) focuses on providing a clear and intuitive canvas for displaying 2D and 3D image results. All functions, such as rotating the view, pausing the image, and using rulers, are housed in a toolbar represented by tabs at the top of the screen. Selecting a function changes the tooltip to indicate the function being used. The GUI is designed to be lightweight, ensuring proper operation even on older computer systems. Finally, the image rendering GUI will work in conjunction with annotation / configuration, potential disease indicators, and cloud computing platforms.

[0188] Healthcare end-users prefer to highlight and place annotations directly on medical images, rather than attaching additional documentation to the images. Medical end-users strongly demand the ability to include image annotations (“eye-catching” arrows and treatment notes) and anatomical features (virtual lines drawn around anatomical structures) in imaging studies. Medical end-users are typically members of large, multidisciplinary nursing teams that collaborate in treating patients. The following example illustrates the current workflow and technical capabilities of clinical nursing teams:

[0189] Once imaging studies are conducted on lung cancer patients, the images are read by radiologists, radiation oncologists prescribe radiation therapy, and used as the basis for surgical treatment by surgeons. Specifically, radiologists outline the area around the tumor and transmit images of the configuration to radiation oncologists.

[0190] Next, the radiation oncologist writes instructions for radiation therapy and / or tumor resection surgery based on the images.

[0191] Using radiographic contours and radiation oncology tumor resection instructions, the surgeon removes the tumor.

[0192] Post-processing software 106 provides end users with tools for direct annotation and morphology on moving images, simplifying healthcare workflows. This user-friendly capability enhances healthcare workflows and reduces the probability of medical errors. The interactive nature of our user interface means that treatment instructions and / or medical questions are now clearly visible to all users, and relevant clinical notes are correctly displayed in the corresponding areas of interest.

[0193] Figures 31A to 31B This is a flowchart illustrating a method 3100 for imaging a patient's lungs. In some embodiments, any or all of the operations described below can be performed without human intervention (e.g., without the intervention of a technician). In some embodiments, method 3100 is performed using any of the devices described herein (e.g., Figure 1 The GREX imaging system 100 shown is used for execution. Some operations of 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 method 3100. Some operations in method 3100 may be combined, and / or the order of some operations may be changed.

[0194] The method includes positioning (3102) the patient relative to an x-ray imaging device (e.g., a GREX imaging system). Figure 1The first direction. In some embodiments, method 3100 is performed at a conventional medical imaging system modified to perform some of the operations described below. In some embodiments, positioning the patient in the first direction includes moving (e.g., rotating) the patient to the first direction (e.g., as referenced). Figure 35 (As discussed above), while the x-ray imaging system (e.g., x-ray unit and detector panel) remains in a fixed orientation. For example, in some embodiments, the patient sits or stands on a patient positioning and immobilization device (PPF), such as reference... Figure 35 The described PFF 3501. In some embodiments, positioning the patient in a first direction relative to the x-ray imaging apparatus includes rotating the patient positioning fixation device. For example, in some embodiments, at the beginning of method 3100, the patient positioning fixation device is rotated such that the patient's sagittal plane is positioned at a predefined angle relative to the optical axis of the x-ray imaging apparatus (e.g., the axis along which the x-ray imaging apparatus emits x-rays). In some embodiments, the predefined angle is selected from the group consisting of -45 degrees, -22.5 degrees, 0 degrees, 22.5 degrees, and 45 degrees.

[0195] In some embodiments, positioning the patient in a first direction involves moving (e.g., rotating) the x-ray imaging system, while the patient remains in a fixed orientation (e.g., as referenced). Figure 17 (As discussed).

[0196] In some embodiments, when the patient is positioned in a first direction relative to the x-ray imaging device, the patient positioning fixation device maintains the patient in a fixed orientation (e.g., stabilizes the patient's orientation) so that a three-dimensional image of the patient's lungs can be reconstructed based on the "stationary object" assumption (e.g., as described throughout this disclosure).

[0197] The method includes obtaining (3104) a volumetric measurement of the patient's respiration (e.g., when the patient is breathing normally; the patient does not need to maintain their width). In some embodiments, the volumetric measurement of the patient's respiration is a measurement of the patient's lung volume (e.g., instantaneous lung volume) (e.g., a direct measurement) or a derivative of the patient's lung volume (e.g., flow rate). In some embodiments, as explained in more detail below, the volumetric measurement of the patient's respiration is a measurement that can be converted into the patient's tidal volume (e.g., by measuring chest rise and fall). As used herein, the term tidal volume refers to the difference between the current lung volume and a predefined baseline (e.g., the volume during maximum expiration of normal breathing without additional effort, the volume during maximum inspiratory breathing without additional effort, or any other suitable baseline volume).

[0198] In some embodiments, the volume measurement of a patient's respiration is a geometric (spatial or directional) measurement of the patient's respiration.

[0199] In some embodiments, the volumetric measurement of patient respiration includes (3106) measurements of the patient's chest rise (and / or chest descent). In some embodiments, one or more volumetric respiratory phase sensors (e.g., respiratory phase sensor 110) from the group consisting of a three-dimensional (3D) scanner, a spirometer, and an abdominal binder are used (3108). Figure 1 The method is used to obtain a volume measurement of the patient's respiration. In some embodiments, the method further includes creating (3110) a point cloud of the patient's thoracic cavity surface. The volume measurement of the patient's respiration is determined based on the point cloud of the patient's thoracic cavity surface.

[0200] In some embodiments, the 3D point cloud is used to determine volumetric measurements of patient respiration without generating a mesh reconstruction of the patient's chest cavity (e.g., the raw output of the 3D point cloud is used to generate volumetric measurements of patient respiration instead of first generating a mesh). In some embodiments, (3112) 3D imaging techniques are used to measure one or more locations of the patient's chest cavity to obtain a point cloud of the patient's chest cavity surface. For example, 3D imaging techniques include laser scanning techniques such as light detection and ranging (LIDAR). Such laser scanning techniques are advantageous because some lasers can accurately measure the orientation of the patient's chest cavity even when the patient is wearing modesty garments (e.g., the patient is wearing LiDAR-permeable clothing during method 3100).

[0201] In some embodiments, method 3100 further includes identifying (3114) one or more anatomical landmarks (e.g., externally visible landmarks such as the clavicle, trachea, superior transverse bone, sternum, xiphoid process, spine, or vertebrae) on the surface of the patient's chest cavity using a point cloud of the patient's chest cavity surface. The method also includes inferring the location of one or more internal anatomical landmarks within the patient's chest cavity (e.g., the location of the patient's lungs) based on the point cloud of the patient's chest cavity surface. In some embodiments, the method includes generating a reconstruction of the patient (e.g., generating a computer model of 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 of tissue density (e.g., a 3D model). In some embodiments, the reconstruction includes or is used to determine 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 to be delivered by an X-ray imaging device (e.g., for each image).

[0202] The method includes determining (3116) the patient's respiratory stage based on the patient's respiratory volume measurement (e.g., in real time when the patient is breathing normally) when the patient is positioned in a first direction relative to the x-ray imaging device and upon obtaining a volume measurement of the patient's breathing. In some embodiments, the patient's respiratory stage is defined by the volume of the lungs. Therefore, in some embodiments, determining the respiratory stage of the lungs includes determining the volume of the lungs.

[0203] In some embodiments, operation 3102, etc., is performed as part of the imaging period of method 3100. Method 3100 also includes undergoing 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, volumetric measurements of the patient's breathing are obtained at regular intervals over multiple cycles of the patient's breathing (e.g., 15, 20, or 50 cycles of the patient's breathing, each cycle corresponding to one breath). The volumetric measurements from the training period are then used to correlate specific volumetric measurements with specific respiratory phases. For example, a volumetric measurement corresponding to a tidal volume of 400 ml may be correlated with a maximum inspiratory volume, a volumetric measurement corresponding to a tidal volume of 0 ml may be correlated with a maximum expiratory volume, and so on. Additionally, statistical data about the patient's breathing (e.g., histograms) may be obtained during the training period and used to verify that the breathing obtained during imaging is "normal" breathing (e.g., not excessively deep breathing or other abnormal breathing patterns).

[0204] During the imaging period, in some embodiments, the determined respiratory stage of the patient is (3118) a future (e.g., predicted) respiratory stage. That is, in some embodiments, determining the patient's respiratory stage based on a volume measurement of the patient's respiration involves predicting the future respiratory stage based on one or more current and / or past respiratory stages. For example, the prediction is based on a time series of respiratory stages. In some embodiments, predicting the future respiratory stage based on one or more current and / or past respiratory stages involves generating an autoregressive integral moving average (ARIMA) model. In some embodiments, the prediction uses data from a training period.

[0205] The method includes gating (3120) an x-ray imaging device to generate an x-ray projection (sometimes referred to as a projected image) of the patient's lungs based on determining that the patient's respiratory phase matches a predefined respiratory phase (e.g., the lung volume matches a predefined lung volume). In some embodiments, the x-ray projection is an image taken at a specific angle (e.g., determined by the patient's orientation relative to the x-ray imaging device). In some embodiments, the x-ray projection is obtained using a single x-ray exposure. In some embodiments, the method includes stopping the gating of the x-ray imaging device (e.g., stopping the patient's exposure to x-ray radiation) based on determining that the patient's respiratory phase does not match a predefined respiratory phase.

[0206] In some embodiments, the method includes determining whether the current breathing is irregular based on a volume measurement of the patient's breath. The method further includes, if the current breathing is determined to be irregular, stopping (e.g., maintaining) the gating of the x-ray imaging device (e.g., continuing to wait for an appropriate breathing phase to be obtained via x-ray projection).

[0207] In some embodiments, the predefined breathing phase is (3122) the first predefined breathing phase among a plurality of predefined breathing phases. In some embodiments, the method further includes, while obtaining a volume measurement of the patient's breath, gating an x-ray imaging device to generate a corresponding x-ray projection of the patient's lungs based on determining that the patient's breathing phase matches any one of the plurality of predefined breathing phases. In some embodiments, the x-ray projection (e.g., x-ray measurement) of the patient's lungs is obtained (3124) only when the patient's breathing phase determined by the volume measurement of the patient's breath matches one of the plurality of predefined breathing phases, thereby reducing the patient's total x-ray exposure.

[0208] In some embodiments, the plurality of predefined respiratory phases include (3126) the early expiratory phase, late expiratory phase, maximal expiratory phase, early inspiratory phase, late inspiratory phase, and maximal inspiratory phase of a patient’s complete respiratory cycle (e.g., as referenced). Figure 14 (As shown and described). In some embodiments, an x-ray projection is obtained for each of a plurality of predefined respiratory phases, while the patient is positioned in a first direction relative to the x-ray imaging device.

[0209] In some embodiments, the x-ray projection is (3128) a first x-ray projection, and the method further includes repositioning the patient in a second direction relative to the x-ray imaging apparatus (e.g., by rotating the patient or rotating the x-ray imaging system). In some embodiments, the method includes, while the patient is positioned in the second direction relative to the x-ray imaging apparatus, and while continuing to acquire volumetric measurements of the patient's respiration, continuing to determine the patient's respiratory phase based on the volumetric measurements of the patient's respiration. In some embodiments, the method includes, according to the determined respiratory phase of the patient, gate the x-ray imaging apparatus to generate a second x-ray projection of the patient's lungs to match a predefined respiratory phase.

[0210] In some embodiments, the method further includes generating a static image cube corresponding to a predefined respiratory phase using a first x-ray projection and a second x-ray projection (e.g., as described with reference to FIG. 21). In some embodiments, the static image cube is a three-dimensional reconstruction of the patient's lung volume. In some embodiments, x-ray projections are obtained for each of a plurality of predefined respiratory phases, while the patient is positioned in each of a plurality of directions (including the first and second directions) relative to the x-ray imaging device. In some embodiments, the plurality of directions includes at least five directions (e.g., -45 degrees, -22.5 degrees, 0 degrees, -22.5 degrees, and 45 degrees). In some embodiments, the plurality of directions includes more than five directions (e.g., 6, 7, 8, or more directions). In some embodiments, when the patient is positioned in each of the plurality of directions (including the first and second directions) relative to the x-ray imaging device, no x-ray projection is obtained except for each of the plurality of predefined respiratory phases. Thus, when x-ray projections are obtained for six different respiratory phases in five directions, a total of thirty x-ray projections are obtained (e.g., as described above, the x-ray imaging device is gated to obtain only these images).

[0211] As described elsewhere in this document, these thirty x-ray projections can be used to reconstruct a biomechanical model of how the lungs move in 3D. In some embodiments, fewer than ten x-ray projections obtained from various angles at a predefined breathing phase are used (3130) to generate a static image cube corresponding to the predefined breathing phase (e.g., as described with reference to Figure 21).

[0212] Those skilled in the art will recognize that method 3100 can be applied to other types of movement besides lung movement caused by breathing. For example, in some embodiments, a method includes positioning a patient in a first direction relative to an x-ray imaging device. The method also includes obtaining 3D measurements of a portion of the patient's body (e.g., 3D measurements of the location of that portion of the patient's body). The method further includes, when the patient is positioned in the first direction relative to the x-ray imaging device, and when obtaining the 3D measurements of the portion of the patient's body: determining, based on the 3D measurements of said portion of the patient's body, a triggering criterion for triggering exposure to radiation by the x-ray imaging device; and, based on determining that the triggering criterion is met, gated the x-ray imaging device to generate an x-ray image of the patient. In some embodiments, the x-ray image is an image of a portion of the patient's body (e.g., the patient's legs, abdomen, skull, etc.). In some embodiments, the triggering criterion includes criteria that are met when the 3D measurements of the portion of the patient's body indicate that the portion of the patient's body is in a predefined location (e.g., relative to the imaging device). In some embodiments, the method includes, based on determining that the triggering criterion is not met, stopping the gated x-ray imaging device (e.g., stopping the patient's exposure to x-ray radiation). Furthermore, in some embodiments, method 3100 is applicable to other types of imaging that are not strictly based on X-rays, such as positron emission tomography (PET) imaging or MRI imaging.

[0213] Additionally, it should be understood that method 3100 can be applied to radiotherapy and radiographic imaging. For example, in some embodiments, a method includes positioning a patient in a first direction relative to a radiotherapy source. The method also includes obtaining 3D measurements of a portion of the patient's body (e.g., 3D measurements of the location of that portion of the patient's body). The method further includes, when the patient is positioned relative to the radiotherapy source in the first direction, and upon obtaining the 3D measurements of the portion of the patient's body: determining, based on the 3D measurements of the portion of the patient's body, a triggering criterion for triggering exposure to radiation from the radiotherapy source; and, based on determining that the triggering criterion is met, gated the radiotherapy source to expose the patient to radiation (e.g., expose the portion of the patient's body to radiation). In some embodiments, the triggering criterion includes criteria that are met when the 3D measurements of the portion of the patient's body indicate that the portion of the patient's body is in a predefined location (e.g., relative to an imaging device). In some embodiments, the method includes, based on determining that the triggering criterion is not met, stopping the patient's exposure to radiation.

[0214] It should be understood that what has already been described Figures 31A to 31BThe specific order of operations described herein is merely an example and is not intended to indicate that the described order is the only possible order in which the operations can be performed. Those skilled in the art will recognize the various ways in which the operations described herein can be reordered. Furthermore, it should be noted that details of other processes described herein relative to other methods described herein also apply in a similar manner to the above descriptions of… Figures 31A to 31B Method 3100 is described. For example, see reference 3100. Figure 2 , Figure 6 , Figure 8 , Figure 11 , Figure 13 , Figure 15 Figure 21 Figure 23 , Figure 25 , Figure 27 , Figures 28A to 28B , Figures 32A to 32B , Figures 33A to 33C and Figures 34A to 34C Describe this type of process. For the sake of brevity, these details will not be repeated here.

[0215] Figures 32A to 32B This is a flowchart illustrating method 3200 for gating a radiation source when the patient's respiratory and cardiac phases are consistent. In some embodiments, any or all of the operations described below can be performed without human intervention (e.g., without the intervention of a technician). In some embodiments, method 3200 is performed via or using any of the devices described herein (e.g., Figure 1 The GREX imaging system 100 shown is used for execution. Some operations of 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 method 3200. Optionally, some operations in method 3200 may be combined, and / or the order of some operations may be changed.

[0216] The method includes positioning (3202) a patient in a first direction relative to a radiation source. In some embodiments, the radiation source is (3204) an x-ray imaging device. In some embodiments, the radiation source is (3206) a radiotherapy source. For example, such as Figure 3 and Figure 35 As shown, the patient (e.g., patient 3502) is positioned in a first orientation relative to the radiation source 3504 (e.g., x-ray unit 108). In some embodiments, as referenced in operation 3102 ( Figure 31A As described, patient positioning includes moving (e.g., rotating) the patient, while in some embodiments, patient positioning includes moving (e.g., rotating) the x-ray equipment (e.g., x-ray source and detector).

[0217] The method includes obtaining (3208) measurements of patient respiration (e.g., using a respiratory phase sensor). In some embodiments, the patient respiration measurements are those described above in reference operation 3104 ( Figure 31A The patient's respiration is a volumetric measurement as described in the description. In some embodiments, the patient's respiration measurement is a non-volumetric measurement (e.g., a time-based measurement of the patient's respiration).

[0218] The method includes obtaining (3210) measurements of a patient's cardiac function. In some embodiments, one or more sensors (3304) are used to measure the patient's cardiac function. 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 multiple measurements of the patient's cardiac function that provide a time series of electrical signals controlling the movement of the patient's heart.

[0219] Returning to the imaging phase, the method includes determining (3214) the patient's respiratory phase based on the patient's respiratory measurements (3212) and determining (3216) the patient's cardiac phase (e.g., in real time) based on measurements of the patient's cardiac function (3216) when the patient is positioned in a first direction relative to the radiation source and when measurements of the patient's respiration are obtained (3212). For example, in some embodiments, a zero instruction set computer (ZISC) processor is used to determine the patient's cardiac phase, as referenced in [reference]. Figure 36 As described, the ZISC processor is capable of recognizing the presence of predefined markers (e.g., S wave or T wave) in a single cardiac cycle or a portion of a cardiac cycle.

[0220] In some embodiments, the method further includes obtaining (3218) measurements of the patient's cardiac function from multiple cardiac cycles before gating the radiation source to expose the patient to radiation (operation 3222 below). In some embodiments, the method includes determining an average interval between a predefined cardiac phase and the start of a predefined window of the cardiac cycle using the measurements of the patient's cardiac function from multiple cardiac cycles. For example, the predefined window of the cardiac cycle represents the interval between the top of the R wave and the start of the gating window. For example, in some embodiments, operation 3202, etc., is performed as part of the imaging phase of method 3200. Method 3200 also includes undergoing a training phase prior to the imaging phase, during which information about the patient's cardiac function is obtained. For example, during the training phase, ECG measurements of the patient's cardiac function are obtained at regular intervals in multiple cardiac cycles (e.g., 15, 20, 50 cardiac cycles, one of which corresponds to a complete period of cardiac motion, e.g., from one T wave to the next T wave). The ECG measurements from the training phase are then used to predict resting periods of cardiac motion during the imaging phase, as described below.

[0221] In some embodiments, measurements from multiple cardiac cycles of a patient are (3220) waveform measurements of the multiple cardiac cycles (e.g., ECG measurements), and the method includes statistically stable verification of the waveform measurements of the multiple cardiac cycles.

[0222] The method further includes gating (3222) a radiation source to expose the patient to radiation based on determining that the patient's respiratory phase matches a predefined respiratory phase and determining that the patient's cardiac phase matches a predefined window of cardiac cycle. In some embodiments, when the radiation source is gated, the patient's lungs are exposed to radiation. In some embodiments, the predefined cardiac window corresponds to a quiescent period of cardiac activity (e.g., a period during a cardiac cycle with minimal cardiac activity, as referenced). Figure 12 (As described). In some embodiments, the radiation source is gated within the same cardiac cycle as a defined cardiac phase. In this way, by gating the patient's lung exposure based on the consistency of the respiratory phase and a predefined cardiac phase window, a precise region of the lung (e.g., a precise region of lung tissue) is exposed to radiation without disturbance or motion caused by cardiac movement.

[0223] In some embodiments, the radiation source is an X-ray imaging device. A gated radiation source for exposing a patient to radiation includes (3224) a gated X-ray imaging device to produce an X-ray projection of the patient's lungs. In some embodiments, as referenced... Figures 31A to 31B As described in method 3100, x-ray projections can be obtained in this manner for multiple directions and multiple respiratory stages of the patient relative to an x-ray imaging source. These x-ray projections can then be used to generate images of lung motion and / or biophysical models of the lungs (e.g., by relating the motion of lung tissue 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 disturbance or motion caused by cardiac movement.

[0224] In some embodiments, the radiation source is a radiotherapy source. Gated radiation sources for exposing a patient to radiation include (3226) gated radiotherapy sources for irradiating an area of ​​the patient's lungs with a therapeutic dose. In these cases, it is important to deliver as much radiation dose as possible to diseased tissue (e.g., cancerous tissue) and as little as possible to healthy tissue. Method 3200 improves the radiotherapy apparatus by delivering a more accurate dose to diseased tissue while minimizing radiation delivered to healthy tissue.

[0225] In some embodiments, determining a predefined window that matches a patient's cardiac phase to their heart rate cycle includes real-time detection (e.g., using...) Figure 36The BEMP card shown predefines a cardiac phase and waits for a certain period of time to predict (3228) a predefined window of the cardiac cycle, the duration corresponding to the average interval between the predefined cardiac phase and the start of the predefined window of the cardiac cycle. For example, in some embodiments, the peak value of the T wave is detected, and the average interval from the T wave to the ideal gating window (where the radiation source is gated) is waited.

[0226] It should be understood that what has already been described Figures 32A to 32B The specific order of operations described herein is merely an example and is not intended to indicate that the described order is the only possible order in which the operations can be performed. Those skilled in the art will recognize the various ways in which the operations described herein can be reordered. Furthermore, it should be noted that details of other processes described herein relative to other methods described herein also apply in a similar manner to the above descriptions of… Figures 32A to 32B Method 3100 is described. For example, see reference 3100. Figure 2 , Figure 6 , Figure 8 , Figure 11 , Figure 13 , Figure 15 Figure 21 Figure 23 , Figure 25 , Figure 27 , Figures 28A to 28B , Figures 31A to 31B , Figures 33A to 33C and Figures 34A to 34C Describe this type of process. For the sake of brevity, these details will not be repeated here.

[0227] Figures 33A to 33C This is a flowchart of method 3300 according to some embodiments, the method for generating a mechanical property model of the lung by fitting data from registered images. In some embodiments, any or all of the operations described below can be performed without human intervention (e.g., without the intervention of a technician). In some embodiments, method 3300 is performed by or using any of the devices described herein (e.g., Figure 1 The GREX imaging system 100 shown is used for execution. Some operations of 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 method 3300. Optionally, some operations in method 3300 may be combined, and / or the order of some operations may be changed.

[0228] The method includes extracting (3302) multiple displacement fields of lung tissue from multiple x-ray measurements (e.g., x-ray images, also referred to as x-ray projections) corresponding to different respiratory stages of the lung. Each displacement field represents the movement of lung tissue from a first respiratory stage to a second respiratory stage, and each respiratory stage has a corresponding set of biometric parameters. In some embodiments, the x-ray measurements are x-ray projections (also referred to as x-ray projection images) obtained according to method 3100 and / or method 3200. In some embodiments, extracting displacement fields from lung tissue includes identifying a portion of lung tissue in one or more first x-ray projections from the first respiratory stage, the portion corresponding to the same portion of lung tissue in one or more second x-ray projections from the second respiratory stage, and determining the displacement of said portion of lung tissue from the first respiratory stage to the second respiratory stage (e.g., with reference to...). Figure 28B (As described above). In some embodiments, a deformable image registration algorithm is used to perform the recognition, as discussed above.

[0229] In some embodiments, the plurality of x-ray measurements include a plurality of x-ray images, comprising x-ray images obtained for a first respiratory phase in each of a plurality of directions 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 cases, at least one of the x-ray images corresponding to the first respiratory phase is obtained during a patient respiratory cycle different from the different x-ray images corresponding to the first respiratory phase (e.g., images obtained during the same phase but in different breaths). In some embodiments, the method includes grouping the plurality of x-ray images by respiratory phase.

[0230] In some embodiments, the method includes extracting multiple vector fields, of which a displacement field is one example.

[0231] In some embodiments, one or more sensors are used (3304) to measure a patient's biometric signals as one or more sequences of a time series, said one or more sensors comprising a 3D spatial orientation locator (e.g., 3D spatial locator 300, Figure 3This includes one or more of a respiratory phase sensor and a cardiac phase sensor. In some embodiments, the 3D spatial orientation locator is configured (3306) to measure real-time body movements of the patient caused by breathing and heartbeat and output them as a time series. In some embodiments, the respiratory phase sensor is configured (3308) to measure one or more physiological measurements related to the patient's breathing, including tidal volume and its first time derivative. In some embodiments, the cardiac phase sensor is configured (3310) to measure periodic and stationary electrical signals (e.g., ECG signals) generated by the patient's heart. For example, the cardiac phase sensor measures periodic and stationary electrical signals having characteristic features corresponding to the phases of the heartbeat.

[0232] In some embodiments, a patient’s biometric signal measured by one or more sensors is used to trigger (3312) the x-ray unit to obtain x-ray images of the patient during specific respiratory and cardiac phases (e.g., as described with reference to method 3200).

[0233] In some embodiments, the x-ray unit includes a clock (3314), and the patient's biometric signals, measured by one or more sensors, are synchronized with the clock of the x-ray unit. In some embodiments, corresponding values ​​of the biometric signals are recorded as associated with acquired x-ray images.

[0234] In some embodiments, the patient’s biometric signals measured during the training window are used (3316) to construct an optimized respiratory prediction model for predicting the desired respiratory phase when the X-ray unit is triggered to capture X-ray images of the patient.

[0235] The method includes calculating (3318) one or more biophysical parameters of a biophysical model of the lung using multiple displacement fields of lung tissue between different respiratory stages of the lung and corresponding sets of biometric parameters. In some embodiments, calculating one or more biophysical parameters includes calculating one or more derivatives of the displacement fields (e.g., curl, gradient, etc.). In some embodiments, the biophysical parameters are biomechanical parameters (e.g., stress, strain, elastic modulus, elastic limit, etc.). In some embodiments, the one or more biophysical parameters define (3320) the physical relationship between biometric parameters associated with different respiratory stages of the lung and multiple displacement fields of lung tissue. In some embodiments, the set of biometric parameters associated with a corresponding respiratory stage includes (3322) tidal volume and airflow of the lung in the corresponding respiratory stage and the cardiac stage corresponding to the corresponding respiratory stage of the lung. In some embodiments, the physical relationship between biometric parameters associated with different respiratory stages of the lung and multiple displacement fields of lung tissue is defined as follows:

[0236]

[0237] vector Describe the normal stress caused by moisture. Describe the normal stress caused by airflow. Describe the shear stress caused by airflow, and Describes tissue movement induced by cardiac motion, specifically the displacement of tissue at any point along a closed-loop trajectory. Expressed as tidal volume (T) v ), airflow (A f ) and cardiac phase (H c The sum of stress, strain, and perturbation vectors of the heart, scaled proportionally.

[0238] In some embodiments, the different respiratory phases of the lungs include (3324) the patient’s complete respiratory cycle of early expiration, late expiration, maximal expiration, early inspiration, late inspiration, and maximal inspiration.

[0239] In some embodiments, the method further includes displaying a visual representation of biophysical parameters. For example, Figure 30 The biophysical parameters provided are and Examples of ratios. In some embodiments, the visual representation includes displaying an image of the lungs, wherein the color of a location within the lung image corresponds to a biophysical parameter (e.g., displaying an image of the lungs using a color plot of biophysical parameters). Displaying such a visual representation improves the X-ray imaging equipment itself by increasing diagnostic accuracy. For example, compared to... Figure 29 The conventional X-ray image shown is in Figure 30 It is easier to see lesions in the patient's left lung in this way.

[0240] In some embodiments, the method further includes generating (3326) multiple medical image cubes corresponding to different respiratory stages of the lung based on multiple x-ray measurements (e.g., as described with reference to FIG. 21). In some embodiments, multiple displacement fields of the lung tissue are also extracted (3328) from the multiple medical image cubes corresponding to different respiratory stages of the lung by delineating the lung tissue from the remainder of the first medical image cube using image segmentation, and for a corresponding voxel in the first medical image cube, a displacement vector between the voxels in the first medical image cube and the second medical image cube is determined using an intensity-based structural mapping between the first medical image cube and the second medical image cube. The multiple displacement fields of the lung tissue are also extracted from the multiple medical image cubes corresponding to different respiratory stages of the lung by iteratively refining the displacement vectors of different voxels in the first medical image cube and their corresponding portions in the second medical image cube.

[0241] In some embodiments, the method further includes: selecting one or more of a plurality of medical image cubes (3330) as reference medical image cubes, determining a set of biometric 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, and simulating the medical image cube between the two reference medical image cubes by applying the set of biometric parameters based on lung biometric measurements to a biophysical model.

[0242] It should be understood that what has already been described Figures 33A to 33C The specific order of operations described herein is merely an example and is not intended to indicate that the described order is the only possible order in which the operations can be performed. Those skilled in the art will recognize the various ways in which the operations described herein can be reordered. Furthermore, it should be noted that details of other processes described herein relative to other methods described herein also apply in a similar manner to the above descriptions of… Figures 32A to 32B Method 3100 is described. For example, see reference 3100. Figure 2 , Figure 6 , Figure 8 , Figure 11 , Figure 13 , Figure 15 Figure 21 Figure 23 , Figure 25 , Figure 27 , Figures 28A to 28B , Figures 31A to 31B , Figures 32A to 32B and Figures 34A to 34C Describe this type of process. For the sake of brevity, these details will not be repeated here.

[0243] Figures 34A to 34C This is a flowchart illustrating a method 3400 for generating a 3D x-ray cubic image from a patient's 2D x-ray images. In some embodiments, any or all of the operations described below can be performed without human intervention (e.g., without the intervention of a technician). In some embodiments, method 3300 is performed using any of the devices described herein (e.g., Figure 1 The GREX imaging system 100 shown is used for execution. Some operations of 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 method 3300. Optionally, some operations in method 3400 may be combined, and / or the order of some operations may be changed.

[0244] In some embodiments, one or more sensors are used (3402) to measure a patient’s biometric signals as one or more sequences of time series, said one or more sensors including one or more of a 3D spatial orientation locator, a respiratory phase sensor and a cardiac phase sensor (e.g., as described above with reference to methods 3100 and 3200).

[0245] In some embodiments, the method further includes: identifying (3406) a cardiac phase gating window using one or more cardiac phase sensor measurements, predicting a respiratory phase using one or more respiratory phase sensor measurements, identifying the consistency between the cardiac phase gating window and the predicted respiratory phase for generating an x-ray imaging pulse, and labeling an x-ray image corresponding to the x-ray imaging pulse using respiratory phase, cardiac phase, and 3D spatial orientation locator measurements (e.g., as described above with reference to method 3200).

[0246] In some embodiments, the 3D spatial orientation locator is configured (3408) to measure real-time body movements of the patient caused by breathing and heartbeat and output them as time series (e.g., as described above with reference to methods 3100 and 3200).

[0247] In some embodiments, the respiratory phase sensor (3410) is configured to measure one or more physiological metrics related to a patient's breathing, including tidal volume and its first time derivative. For example, the rate of tidal volume varies with time or airflow.

[0248] In some embodiments, the cardiac phase sensor (3412) is configured to measure periodic and steady electrical signals generated by the patient’s heart, having characteristic features corresponding to cardiac phases.

[0249] In some embodiments, after synchronization with the clock of the x-ray unit, two different filters are used (3414) to remove signal drift and noise from the patient's biometric signal.

[0250] In some embodiments, the patient’s biometric signals, measured by one or more sensors, are used (3416) to trigger the x-ray unit to acquire x-ray images of the patient during specific respiratory and cardiac phases.

[0251] In some embodiments, the x-ray unit includes (3418) a clock. The patient's biometric signals, measured by one or more sensors, are synchronized with the clock of the x-ray unit, and the corresponding values ​​of the biometric signals are recorded as associated with the acquired x-ray images.

[0252] In some embodiments, the patient’s biometric signal is measured (3422) during a training window (e.g., a training period) prior to capturing any X-ray images of the patient, and the patient’s biometric signal measured during the training window includes multiple complete respiratory cycles of the patient (e.g., as described above with reference to methods 3100 and 3200).

[0253] In some embodiments, biometric signals of the patient measured during the training window are used to identify (3424) multiple tidal volume percentiles over a complete respiratory cycle, each tidal volume percentile corresponding to a respiratory phase in a different respiratory phase.

[0254] In some embodiments, the patient’s biometric signals measured during the training window are used (3426) to construct an optimized respiratory prediction model for predicting the desired respiratory phase when the X-ray unit is triggered to capture X-ray images of the patient.

[0255] In some embodiments, the optimized respiratory prediction model (3428) is based on an autoregressive integral moving average (ARIMA) model.

[0256] In some embodiments, the desired breathing phase for capturing X-ray images of the patient is configured (3430) to coincide with a cardiac gating window during which cardiac-induced lung movements change slowly.

[0257] In some embodiments, a cardiac gating window (3432) is selected based on the position of the T wave and P wave in the electrocardiogram (ECG) signal, so that the cardiac-induced lung motion changes slowly.

[0258] The method includes converting (3434) a first set of multiple X-ray images of the lungs captured at different projection angles into a second set of multiple X-ray images of the lungs corresponding to different respiratory stages.

[0259] In some embodiments, converting a first set of multiple X-ray images of the lungs captured at different projection angles into a second set of multiple X-ray images of the lungs corresponding to different respiratory stages further includes (3436) capturing the first set of multiple X-ray images of the lungs at different projection angles. Each set of the first set of X-ray images corresponds to a different respiratory stage of the lungs at a specific projection angle. In some embodiments, converting the first set of multiple X-ray images of the lungs captured at different projection angles into a second set of multiple X-ray images of the lungs corresponding to different respiratory stages further includes reorganizing the first set of multiple X-ray images of the lungs into a second set of multiple X-ray images of the lungs based on the associated respiratory stages of the first set of X-ray images of the lungs. Each set of the second set of X-ray images corresponds to a corresponding respiratory stage of the lungs.

[0260] In some embodiments, x-ray images within any particular group are (3438) geometrically resolved and are temporally independent.

[0261] In some embodiments, different respiratory stages of the lung correspond to different tidal volume percentiles of lung movement (3440).

[0262] In some embodiments, the different respiratory phases of the lungs include (3442) the patient’s complete respiratory cycle of early expiration, late expiration, maximal expiration, early inspiration, late inspiration, and maximal inspiration.

[0263] In some embodiments, multiple X-ray images of the lungs captured at different projection angles all correspond to the same respiratory phase (3444).

[0264] In some embodiments, different respiratory phases of the lungs are acquired from at least two respiratory cycles at a specific projection angle (3446).

[0265] The method includes generating (3448) static image cubes based on each of the second multiple sets of X-ray images during the corresponding breathing phase using back projection.

[0266] The method involves combining (3450) static image cubes corresponding to different respiratory stages of the lungs into a 3D x-ray image cube image by time interpolation.

[0267] It should be understood that what has already been described Figures 34A to 34C The specific order of operations described herein is merely an example and is not intended to indicate that the described order is the only possible order in which the operations can be performed. Those skilled in the art will recognize the various ways in which the operations described herein can be reordered. Furthermore, it should be noted that details of other processes described herein relative to other methods described herein also apply in a similar manner to the above descriptions of… Figures 34A to 34C Method 3100 is described. For example, see reference 3100. Figure 2 , Figure 6 , Figure 8 , Figure 11 , Figure 13 , Figure 15 Figure 21 Figure 23 , Figure 25 , Figure 27 , Figures 28A to 28B , Figures 31A to 31B , Figures 32A to 32B and Figures 33A to 33C Describe this type of process. For the sake of brevity, these details will not be repeated here.

[0268] Figure 35An exemplary patient positioning and immobilization device (PPF) 3501 (e.g., a swivel chair) for supporting a patient 3502 is depicted according to some embodiments. In some embodiments, the PPF 3501 rotates (e.g., along rotation 3503) to position the patient 3502 at multiple angles (e.g., orientations) relative to a radiation source 3504 (e.g., an x-ray imaging system or a radiotherapy system). For example, the PPF moves in a manner that moves the patient to the desired position (e.g., the patient does not need to move independently), enabling the radiation device 3504 to capture x-ray images of the patient at various angles. In some embodiments, the patient 3502 rotates (e.g., along rotation 3502) to achieve the multiple angles without rotating the PPF 3501. In some embodiments, the PPF 3501 and / or the patient 3505 automatically rotate and / or move (e.g., using a motor) to the desired position. In some embodiments, a technician rotates and / or moves the patient 3502. In some embodiments, a flat panel detector unit 3505 is positioned behind the patient relative to the radiation device 3504.

[0269] 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 PPF 3501. For example, camera 3506 captures whether an object will collide with PPF 3501 as it moves (and rotates within) the predefined area. In some embodiments, a collision avoidance method is provided for PPF 3501 (and patient 3502).

[0270] In some embodiments, one or more 3D imaging sensors (e.g., LIDAR sensors or structured light sensors) are used for geometric monitoring of the patient's breathing 3502, as described in reference method 3100.

[0271] Figure 36A demonstrative Bioevent Monitoring Process (BEMP) card 3600 is depicted. 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 the patient's biometric signals. 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 R waves in the ECG signal to predict when the next resting period of the patient's cardiac cycle will occur (e.g., during a TP interval). In some embodiments, the output of the ZISC processor 3606 can be used to trigger a ZISC 4-bit weighted output of a radiation source (e.g., an X-ray imaging system or a radiotherapy system). In some embodiments, a clock signal 3603 for the BEMP card 3600 is provided from an external source (e.g., a radiation source) so that the BEMP card 3600 can be synchronized with the radiation source.

[0272] The computational techniques discussed throughout Chapter 106 of Post-Processing Software are computationally resource-intensive. Many community hospitals and small clinics lack access to the computer hardware necessary to create images, compute biomechanical models, and deliver results quickly and smoothly to end users. Post-Processing Software 106's functionality is executed in the cloud, enabling end users to access its powerful visualization tools on processing consoles, office desktops, or work laptops.

[0273] Figures 37A-37B This is a flowchart of a method 3700 for determining a certain radiation dose to expose a patient, according to certain embodiments. In some embodiments, any or all of the operations described below can be performed without human intervention (e.g., without the intervention of a technician). In some embodiments, method 3700 is performed by or using any of the devices described in this invention (e.g., Figure 1 The GREX imaging system 100 shown is illustrated. Some operations of method 3700 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 method 3700. Certain operations in method 3700 may be optionally combined and / or the order of certain operations may be optionally changed.

[0274] Note that in some embodiments, method 3700 is performed during the imaging protocol or phase (rather than during the calibration protocol or phase). Furthermore, while specific embodiments of patient thoracic cavity modeling and irradiation are described below, it should be understood that, according to other embodiments, method 3700 can be used to model and irradiate a first part of the patient's body rather than the patient's thoracic cavity.

[0275] Method 3700 includes (3702) positioning a patient in a first direction relative to a radiation source. In some embodiments, the radiation source is an X-ray imaging source. Positioning a patient relative to a radiation source is described in detail herein, for example, with reference to... Figure 1 The GREX imaging system is shown. For the sake of brevity, these details will not be repeated here.

[0276] Method 3700 further includes (3704) measuring one or more locations in the patient's chest cavity (e.g., one or more locations on the skin surface of the patient's chest cavity) using 3D imaging technology. As described above, in some embodiments, the 3D imaging technology is used to measure a first part of the patient's body (not the patient's chest cavity, e.g., the patient's abdomen, skull, legs, feet). In some embodiments, (3706) the 3D imaging technology uses (e.g., based on) visible light or ultraviolet light (e.g., non-X-ray imaging technology). In some embodiments, the 3D imaging technology uses sound (e.g., sound wave or ultrasound 3D imaging technology). In some embodiments, the 3D imaging detector used by the 3D imaging technology is different from the 3D imaging detector used for X-ray imaging. In some embodiments, the 3D imaging technology is not a computed tomography (CT) technology. In some embodiments, the light frequency used by the 3D imaging technology does not penetrate the human body surface. In some embodiments, the radiation dose has a first light frequency, and the second light frequency used by the 3D imaging technology is different from the first light frequency (e.g., at least one order of magnitude different). In some embodiments, the 3D imaging technology is a multispectral imaging technology. In some embodiments, the 3D imaging technique does not rely on radiation transmission through the patient's chest cavity (e.g., the 3D imaging technique is performed using a light source and a detector (e.g., a camera) that images the patient's surface illuminated by the light source). In some embodiments, the light frequency does penetrate clothing worn by the patient in the method of the invention (e.g., penetrating ordinary clothing). In some embodiments, the 3D imaging technique acquires an image of the patient's chest cavity surface and uses the image of the patient's chest cavity surface to measure one or more locations within the patient's chest cavity. In some embodiments, the 3D imaging technique includes (3708) light detection and ranging (LIDAR).

[0277] In some embodiments, measuring one or more locations of the patient's pleural cavity includes (3710) creating a point cloud of the patient's pleural cavity surface (e.g., referencing...). Figure 38A The above).

[0278] In some embodiments, when measuring one or more locations of a patient’s chest cavity using 3D imaging technology (3712), method 3700 includes performing the operations described with reference to references 3714-3730.

[0279] The method includes (3714) generating a patient chest model (e.g., the surface shape of the patient chest, or more generally, a model of a first part of the patient's body) using one or more locations of the patient's chest cavity. In some embodiments, generating the patient chest model includes (3716) identifying one or more anatomical landmarks on the surface of the patient's chest using data from 3D imaging technology and (3718) creating a patient chest model (e.g., the spatial distribution of the density of the patient's chest) based on the one or more anatomical landmarks, wherein the patient chest model is an internal anatomical model of the patient's chest cavity. In some embodiments, updating the patient chest model as the patient breathes includes updating the location of one or more anatomical landmarks and updating the model of the internal anatomy based on the updated location of the one or more anatomical landmarks. In some embodiments, the internal anatomical model of the patient's chest cavity includes a plurality of model points corresponding to regions within the patient's chest cavity, each of the plurality of model points being associated with an X-ray cross section (e.g., the corresponding electron density of body tissue associated with the model point determined based on data using a Medical Internal Radiation Dose (MIRD) anatomical database).

[0280] The method includes (3720) updating the patient chest cavity model as the patient breathes (e.g., repeatedly performing some or all of the operations described above to generate the patient chest cavity model, such that the patient chest cavity model is a real-time or near-real-time model of the patient's chest cavity as the patient breathes).

[0281] The method includes (3722) exposing a patient to a radiation dose using a radiation source. In some embodiments, the radiation dose is applied to the patient's chest cavity (or more generally to a first part of the patient's body). The dose is determined based on a patient chest cavity model (e.g., one or more exposure parameters, such as dose magnitude and / or duration, are based on the patient chest cavity model at a current respiratory stage corresponding to a certain duration of exposure to the radiation dose). In some embodiments, the method includes determining the dose based on the patient chest cavity model (e.g., automatically, without human intervention). In some embodiments, (3724) the radiation dose is determined without acquiring calibration X-ray images. In some embodiments, the radiation dose is determined based on an X-ray cross-section at a model point along the radiation path. In some embodiments, the dose is determined based on an X-ray cross-section (e.g., electron density) of a vector passing through the patient chest cavity model, wherein the vector is along the direction of radiation propagation of the radiation source (e.g., the vector is through the patient's beam view, connecting the radiation source to an X-ray imaging detector).

[0282] In some embodiments, (3726) the radiation source is a radiotherapy source, and the dose is a therapeutic dose. In some embodiments, the radiation source is an X-ray imaging source, and the dose is a diagnostic dose (e.g., a dose sufficient to provide a predefined signal-to-noise ratio in the resulting image).

[0283] In some embodiments, (3728) when exposed to a certain radiation dose (e.g., the patient does not hold their breath), the patient breathes normally.

[0284] In some embodiments, method 3700 includes, while measuring one or more locations of the patient's chest cavity using 3D imaging technology and (3730) continuing to update the patient's chest cavity model during the patient's breathing, adjusting the radiation dose for each of the multiple exposures at different times based on the updated model of the patient's chest cavity (e.g., by adjusting the current in the linear accelerator that generates the radiation dose). For this purpose, in some embodiments, the radiation dose is a first radiation dose among a plurality of radiation doses received by the patient. In some embodiments, the patient is exposed to the first radiation dose during a first breathing phase, and the first radiation dose is determined based on the patient's chest cavity model during the first breathing phase. In some embodiments, the method includes exposing the patient to a second radiation dose during a second breathing phase different from the first breathing phase, wherein the second radiation dose is determined based on the updated model during the second breathing phase. In some cases, the second radiation dose is acquired when the patient is located in a first direction relative to the radiation source, and the second radiation dose is further based on the first direction (e.g., based on a vector through the patient's chest cavity model). In some cases, the second radiation dose is acquired when the patient is located in a second direction relative to the radiation source, and the second radiation dose is further determined based on the second direction (e.g., based on a vector through the patient's chest cavity model).

[0285] Note that method 3700 can be used to determine the radiation parameters of any radiation exposure described in this invention. For example, method 3700 can be combined with any other method described in this invention to determine the dose and / or duration of radiation exposure. Thus, as described with reference to method 3300, a biophysical model of a patient's lungs can be generated using an appropriate dose.

[0286] Figures 38A-38C The process of generating a patient's thoracic cavity model according to certain embodiments is described. Figure 38A The results of measuring the patient's 3800 pleural cavity location using 3D imaging techniques (e.g., LiDAR) are illustrated. For example, in some embodiments, the location measurement of the patient's pleural cavity (e.g., skin surface location) can be used to create a point cloud 3802 of the patient's pleural cavity surface. Figure 38B As shown, one or more anatomical landmarks (such as the umbilicus of patient 3800 and / or the clavicle portion of patient 3800) can be identified from the point cloud. In some embodiments, such as Figure 38C As shown, the patient's thoracic cavity model (e.g., a 3D or internal model) is based on one or more anatomical landmarks. For example, in some embodiments, creating a patient's thoracic cavity model includes scaling (e.g., "manipulating") known skeletal and / or soft tissue structures based on the location of anatomical landmarks. For example, creating a patient's thoracic cavity model includes generating model points with X-ray cross-sections corresponding to the following bones: clavicle, superior transverse bone, sternum, xiphoid process, spine, humerus, etc.

[0287] Throughout this specification, references to "embodiment," "some embodiments," "one embodiment," "another example," "example," "specific example," or "some examples" indicate that a particular feature, structure, material, or characteristic described in connection with the said embodiment or example is included in at least one embodiment or example of this disclosure. Therefore, the appearance of phrases such as "in some embodiments," "in one embodiment," "in an embodiment," "in another example," "in an example," "in a specific example," or "in some examples" in various places throughout the specification does not necessarily refer to the same embodiment or example of this disclosure. Furthermore, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0288] Although illustrative embodiments have been shown and described, those skilled in the art will understand that the above embodiments should not be construed as limiting this disclosure, and variations, substitutions, and modifications may be made in the embodiments without departing from the spirit, principles, and scope of this disclosure.

Claims

1. A method for X-ray imaging, comprising: Position the patient in the first direction relative to the X-ray imaging source; 3D imaging, which differs from X-ray imaging, measures one or more locations in a patient's chest cavity using visible or ultraviolet light. When using 3D imaging technology to measure one or more locations in a patient's chest cavity: A point cloud of the patient's chest cavity surface was created using data from 3D imaging technology; Use point cloud data of the patient's chest surface to identify one or more anatomical landmarks on the patient's chest surface; An internal anatomical model of the patient's thoracic cavity is created based on one or more anatomical landmarks. The internal anatomical model represents the spatial distribution of density in the patient's thoracic cavity. The internal anatomical model of the patient's thoracic cavity includes multiple model points corresponding to the internal regions of the patient's thoracic cavity, and each of the multiple model points is associated with an X-ray cross section. The internal anatomical model of the patient's thoracic cavity is updated as the patient breathes. as well as Acquiring an X-ray image of the patient's chest cavity includes exposing the patient to a radiation dose using the X-ray imaging source, wherein the dose is calculated based on an X-ray cross-section of a vector passing through a model of the patient's chest cavity.

2. The method according to claim 1, wherein, The 3D imaging technology includes photodetection and photometric ranging.

3. The method according to claim 1, wherein, The radiation dose is determined without acquiring a calibration X-ray image.

4. The method according to any one of claims 1-3, wherein, The patient's breathing was normal when exposed to a certain dose of radiation.

5. The method according to any one of claims 1-4, wherein, Based on the patient's chest cavity model, the X-ray imaging source is used to expose the patient to a certain radiation dose, including adjusting the radiation dose during multiple exposures at different times.

6. An X-ray imaging system, comprising: One or more processors; as well as A memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform a set of operations, including: Position the patient in the first direction relative to the X-ray imaging source; 3D imaging, which differs from X-ray imaging, measures one or more locations in a patient's chest cavity using visible or ultraviolet light. When using 3D imaging technology to measure one or more locations in a patient's chest cavity: A point cloud of the patient's chest cavity surface was created using data from 3D imaging technology; Use point cloud data of the patient's chest surface to identify one or more anatomical landmarks on the patient's chest surface; An internal anatomical model of the patient's thoracic cavity is created based on one or more anatomical landmarks. The internal anatomical model represents the spatial distribution of density in the patient's thoracic cavity. The internal anatomical model of the patient's thoracic cavity includes multiple model points corresponding to the internal regions of the patient's thoracic cavity, and each of the multiple model points is associated with an X-ray cross section. The patient's internal anatomical model is updated with the patient's breathing; and Acquiring an X-ray image of the patient's chest cavity includes exposing the patient to a radiation dose using the X-ray imaging source, wherein the dose is calculated based on an X-ray cross-section of a vector passing through a model of the patient's chest cavity.

7. A system comprising: One or more processors; as well as A memory for storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of any one of claims 2-5.

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