Methods, systems, and / or computer readable media for characterizing tissue movement based on acquired PET data

Abstract

A computer-implemented method includes obtaining positron emission tomography (PET) data of a tissue of interest acquired over a plurality of motion cycles of the tissue of interest being moved. The computer-implemented method also includes generating a set of short PET frames from the PET data based on a predetermined duration that is less than a period of time of the plurality of motion cycles. The computer-implemented method also includes identifying the tissue of interest within each short PET frame in the set of short PET frames. The computer-implemented method also includes estimating motion of the identified tissue of interest within each of the short PET frames. The computer-implemented method also includes characterizing the motion of the tissue of interest within the plurality of motion cycles based on the estimated motion of the tissue of interest identified within each of the short PET frames.

Description

Technical Field

[0001] The following relates generally to positron emission tomography (PET), and more particularly to characterizing tissue motion based on acquired PET data. Background Art

[0002] Medical procedures target tissue of interest. For example, radiation therapy, which is a treatment procedure, applies radiation to a target tissue of interest, such as a tumor or lesion, to control or kill the target tissue of interest. During such a procedure, a therapeutic radiation beam is directed to the target tissue of interest. However, certain physiological processes regulated by the autonomic nervous system (ANS), such as breathing, heartbeat, etc., may cause the target tissue of interest to move. For example, breathing, which includes multiple inhalation and exhalation cycles, may cause the target tissue of interest to move in coordination with the circulatory motion of the lungs.

[0003] In order to track a therapeutic radiation beam to a moving target tissue of interest during a procedure, the motion of the target tissue of interest has been estimated from a pre-procedure planning scan, and the estimated motion has been used to plan modulation of the therapeutic radiation beam during treatment planning. In one example, four-dimensional (4D) cine computed tomography (CT) has been used to estimate the motion of the target tissue of interest. To this end, for example, during a PET / CT planning scan, three-dimensional (3D) volumetric CT image data is acquired within a respiratory cycle, and images are reconstructed for different phases of the respiratory cycle, collectively providing images of each phase of the respiratory cycle, and therefore the position of the tissue of interest in each phase.

[0004] The motion of target tissue of interest within a respiratory cycle has been estimated from 4D cine CT image data and used to plan radiation therapy protocols. However, 4D cine CT image data of a single respiratory cycle does not capture the variation in respiratory motion from respiratory cycle to respiratory cycle, which can be substantial. Therefore, the target tissue of interest may move during a treatment protocol in a manner that was not expected from the 4D cine CT image data, and the therapeutic radiation beam may not irradiate the target tissue of interest according to the treatment plan. In addition, CT imaging utilizes ionizing radiation, which may damage and kill cells of healthy normal tissue, and therefore, it is not practical to acquire CT image data for multiple respiratory cycles.

[0005] In view of at least the foregoing, there remains an unaddressed need for methods for characterizing the motion of tissue of interest. Summary of the invention

[0006] The various aspects described herein solve the above problems and other problems. The present disclosure introduces the concepts described in more detail in the specific embodiments. It should not be used to determine the essential characteristics of the claimed subject matter, nor should it be used to limit the scope of the claimed subject matter.

[0007] In one aspect, a computer-implemented method includes obtaining positron emission tomography (PET) data of a tissue of interest acquired over multiple motion cycles of the moving tissue of interest. The computer-implemented method also includes generating a set of short PET frames from the PET data based on a predetermined duration, the predetermined duration being less than a period of the multiple motion cycles. The computer-implemented method also includes identifying the tissue of interest in each short PET frame in the set of short PET frames. The computer-implemented method also includes estimating the motion of the identified tissue of interest in each short PET frame in the short PET frames. The computer-implemented method also includes characterizing the motion of the tissue of interest over the multiple motion cycles based on the estimated motion of the identified tissue of interest in each short PET frame in the short PET frames.

[0008] The computer system includes a computer-readable storage medium including instructions for characterizing the motion of a moving tissue of interest in PET data; and a processor configured to execute the instructions. The instructions cause the processor to obtain PET data of the tissue of interest acquired within multiple motion cycles of the moving tissue of interest. The instructions also cause the processor to generate a set of short PET frames from the PET data based on a predetermined duration, the predetermined duration being less than a period of the multiple motion cycles. The instructions also cause the processor to identify the tissue of interest in each short PET frame in the set of short PET frames. The instructions also cause the processor to estimate the motion of the identified tissue of interest in each short PET frame in the short PET frames. The instructions also cause the processor to characterize the motion of the tissue of interest within the multiple motion cycles based on the estimated motion of the identified tissue of interest in each short PET frame in the short PET frames.

[0009] In another aspect, a computer-readable medium is encoded with computer-executable instructions. When executed by a processor, the computer-executable instructions cause the processor to obtain PET data of a tissue of interest acquired within multiple motion cycles of the moving tissue of interest. The computer-executable instructions also cause the processor to generate a set of short PET frames from the PET data based on a predetermined duration, the predetermined duration being less than a period of the multiple motion cycles. The computer-executable instructions also cause the processor to identify the tissue of interest within each short PET frame in the set of short PET frames. The computer-executable instructions also cause the processor to estimate the motion of the identified tissue of interest within each short PET frame in the short PET frames. The computer-executable instructions also cause the processor to characterize the motion of the tissue of interest within the multiple motion cycles based on the estimated motion of the identified tissue of interest within each short PET frame in the short PET frames.

[0010] Those skilled in the art will appreciate further aspects of the present application upon reading and understanding the accompanying description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The present application is illustrated by way of examples and not limited by the figures of the accompanying drawings, in which like references indicate similar elements.

[0012] Figure 1 An exemplary system according to embodiments herein is schematically illustrated.

[0013] Figure 2 A non-limiting example of a short frame PET processing module according to embodiments herein is schematically illustrated.

[0014] Figure 3 Included are non-limiting examples of short PET frames according to embodiments herein showing the location of tissue of interest during one phase.

[0015] Figure 4 Included are non-limiting examples of short PET frames according to embodiments herein showing the location of tissue of interest in another phase.

[0016] Figure 5 A non-limiting example including a short PET frame superimposed with tissue of interest on several short PET frames according to embodiments herein showing the extent of motion over multiple motion cycles.

[0017] Figure 6 A non-limiting example of a motion detector module according to embodiments herein is schematically illustrated.

[0018] Figure 7 Diagrammatically illustrated are non-limiting examples of motion positions detected for multiple motion cycles according to embodiments herein.

[0019] Figure 8 A non-limiting example of a motion characterization module of a non-limiting system according to embodiments herein is schematically illustrated.

[0020] Fig. 9 Diagrammatically illustrated are non-limiting examples of motion locations detected for multiple motion cycles with local minima and maxima labeled according to embodiments herein.

[0021] Fig.10 An identified cycle among a plurality of cycles from local minimum to local minimum according to embodiments herein is graphically illustrated.

[0022] Fig.11 An identified cycle among a plurality of cycles from local maximum to local maximum according to embodiments herein is graphically illustrated.

[0023] Fig.12 The diagram shows a system segmented into N segments according to an embodiment of the present invention. Fig.10 The identified cycles are shown in .

[0024] Fig.13 The motion position values ​​statistically as a function of phase according to embodiments herein are graphically shown.

[0025] Fig.14 Schematically illustrated is a non-limiting example of a cross-sectional side view of a multi-modality imaging system including a PET imaging system and a CT imaging system according to embodiments herein.

[0026] Fig.15 A non-limiting example of a PET imaging system according to embodiments herein is schematically illustrated.

[0027] Fig.16 A non-limiting example of a CT imaging system according to embodiments herein is schematically illustrated.

[0028] Fig.17 Non-limiting examples of therapeutic systems according to embodiments herein are schematically illustrated.

[0029] Fig.18 Schematically illustrated is a non-limiting example of a cross-sectional side view of a multi-modality imaging system including a PET imaging system and an MR imaging system according to embodiments herein.

[0030] Fig.19 A non-limiting example of amplitude-based gating according to embodiments herein is shown.

[0031] Fig. 20A non-limiting example of a flowchart of a computer-implemented method according to embodiments herein is shown.

[0032] Fig.21 Another non-limiting example of a flowchart of a computer-implemented method according to embodiments herein is shown. DETAILED DESCRIPTION

[0033] The cyclic motion of certain tissues (e.g., lungs, heart, etc.) can cause tissue of interest (such as a tumor, lesion, lung tissue, heart tissue, etc.) to move with each cycle of motion of the particular tissue. For procedures that rely on the location of the tissue of interest, such as radiation therapy that applies a therapeutic radiation beam to the tissue of interest, such motion has been estimated for motion cycles from pre-procedure scans (e.g., via 4D cine CT); however, this estimate does not fully describe the variation in motion from cycle to cycle, which can result in less accurate and / or less complete quantification of the motion of the tissue of interest, and therefore less precise dose delivery during treatment. The computer-implemented methods, computing systems, and / or computer-readable media described herein provide methods to at least overcome such shortcomings.

[0034] In one example, the method utilizes short PET frames, which are frames of PET data within a duration less than a period of motion cycle. As used herein, a short PET frame is any image-based representation of the acquired PET data, including but not limited to an image, a point cloud representation, a back projection, a centroid analysis of a short duration sinogram, etc. The duration depends on the specific cycle, and not all cycles are of the same duration. For example, the literature indicates that the normal respiratory rate for an adult (i.e., eighteen years of age and older) person is twelve (12) to twenty (20) breaths per minute, which is three (3) to five (5) seconds of breathing. For such a person, a frame covering, for example, one tenth of a second (0.1s) to one second (1.0s) (such as about 0.5s) will provide multiple short PET frames within each respiratory cycle within multiple respiratory cycles. The short PET frames cover multiple phases of the respiratory cycle, and therefore cover multiple phases of the motion of the tissue of interest.

[0035] Tissue of interest is identified within each short PET frame, and the motion of the tissue of interest is tracked within the short PET frame, thereby providing a position value of the tissue of interest in each short PET frame, and thus providing each motion phase of all motion cycles. The method described in more detail below processes the position value to identify each cycle in a plurality of cycles, re-bins the position value of the motion cycle into a plurality of bins (e.g., 20, 24, 30, etc.) each corresponding to a different phase of the motion cycle, and performs statistical analysis on each bin to characterize the motion within the motion cycle. The motion characterization can then be used to plan treatment for the tissue of interest, for example, a radiotherapy treatment using a linear accelerator. The treatment plan can then be used during treatment to modulate the treatment beam based on the motion characterization. Examples of suitable modulation include adjusting the range of the beam for the motion phase to cover the expected range of motion, turning off the beam during certain motion phases, and / or modulating the beam in other ways.

[0036] In one example, the methods described herein facilitate pre-treatment planning using PET data acquired over multiple motion cycles, thereby allowing for more accurate and / or complete quantification of tissue motion of interest, and thus more precise dose delivery during treatment, without any additional radiation exposure / dose during acquisition, such as an additional 4D cine CT scan. The methods described herein additionally mitigate the use of magnetic resonance (MR) imaging, ultrasound (US) imaging, and / or X-ray imaging (which are based on different imaging characteristics relative to PET imaging) to determine the motion of the tissue of interest during treatment in order to modulate the beam to track the motion of the treatment tissue of interest. Embodiments of the present disclosure will now be described by way of example with reference to the accompanying drawings.

[0037] Figure 1 The non-limiting system 102 is schematically illustrated. The non-limiting system 102 includes a computing system 104, a multi-modality imaging system 106, a therapy system 108, a data repository 110, and a computer network 112. In one example, one or more of the computing system 104, the multi-modality imaging system 106, the therapy system 108, and the data repository 110 are in electronic communication with the computer network 112 and communicate through the computer network 112 with at least one other system (such as at least one of the computing system 104, the multi-modality imaging system 106, the therapy system 108, and the data repository 110) that is also in electronic communication with the computer network 112. Such communications may be performed through physical wired connections and / or wireless technologies.

[0038] In one example, computing system 104 includes a computer, workstation, server, etc. Computing system 104 includes input devices 114 such as a keyboard, mouse, touch screen, microphone, etc., output devices 116 including human-readable devices such as display monitors, and input / output (I / O) 118 for transmitting and / or receiving signals and / or data. Computing system 104 also includes a processor 120 (such as a microprocessing unit (MPU), a central processing unit (CPU), a graphics processing unit (GPU), etc.) and a computer-readable storage medium 122, which includes non-transitory media (e.g., storage units or devices) and does not include transient media (i.e., signals, carrier waves, etc.). Computer-readable storage medium 122 is encoded with computer-executable instructions and / or data.

[0039] The processor 120 is configured to execute at least one of the computer executable instructions, employ and / or generate data, etc. In one example, the computer executable instructions include a short frame PET generation module 124, a motion detection module 126, and a motion characterization module 128. The short frame PET generation module 124 includes instructions for generating a short PET frame based on a predetermined duration that is less than a period of a motion cycle, such as less than a period of a shortest motion cycle. The motion detection module 126 includes instructions for locating a tissue of interest in the short PET frame and determining a tissue position value of interest based on the short PET frame. The motion characterization module 128 includes instructions for characterizing the detected motion within a motion phase within a captured motion cycle based on the tissue position value of interest.

[0040] The acquired PET data may be obtained by the computing system 104 through the computer network 112, for example, via the multimodal imaging system 106, the data repository 110, the radiation therapy system 108, and / or another device. The characterized motion may be stored in the computer-readable storage medium 122, the multimodal imaging system 106, the data repository 110, the therapy system 108, and / or the other device. In another example, at least one of the short frame PET generation module 124, the motion detection module 126, and / or the motion characterization module 128 is located in a different system, such as the multimodal imaging system 106, the therapy system 108, and / or the other system, and the short PET frames and / or position values ​​are obtained by the system having the motion characterization module 128 and used to characterize the motion thus detected.

[0041] In one example, the motion characterization, short PET frame, and / or tissue location value of interest are stored in the computer-readable storage medium 122, the data repository 110, the treatment system 108, the multi-modality imaging system 106, and / or another device, e.g., via a data structure, a file, etc. The motion characterization, short PET frame, and / or tissue location value of interest can be transmitted, e.g., in a header, metadata, Digital Imaging and Communications in Medicine (DICOM) fields, etc., or in a separate file via DICOM and / or other protocols.

[0042] The multi-modality imaging system 106 is configured with N imaging systems, including a first imaging system 130, ..., and an Nth imaging system 132, where N is an integer equal to or greater than 1. In one example, the multi-modality imaging system 106 is configured for at least two imaging modalities, including positron emission tomography (PET) imaging and computed tomography (CT) imaging or magnetic resonance (MR) imaging.

[0043] In one example, one of the first (or Nth) imaging system 130 (or 132) is configured for PET (or CT) imaging, and the other of the first imaging system 130 or the Nth imaging system 132 is configured for CT (or PET) imaging. Generally speaking, a PET imaging system generates functional data, and a CT imaging system generates structural data. Examples of PET and CT imaging systems are described in further detail below.

[0044] In one example, the multi-modality imaging system 106 generates acquired PET data that is utilized by the short frame PET generation module 124 and the motion detection module 126 to produce tissue of interest location values ​​that are used by the motion characterization module 128 to characterize the detected motion. In such an example, the acquired PET data may be stored in the multi-modality imaging system 106, the computer-readable storage medium 122, the data repository 110, the treatment system 108, and / or another device.

[0045] The treatment system 108 includes, for example, an external beam radiation therapy (EBRT) system. In one example, EBRT includes a radiation therapy system, such as a linear accelerator (linac). Generally speaking, a radiation therapy system uses ionizing radiation to irradiate a tissue of interest, such as a tumor or lesion, to control or kill cells of the tissue of interest. In another example, an EBRT system includes a proton radiation therapy system, such as a cyclotron. Generally speaking, a proton radiation therapy system uses a proton beam to irradiate a tissue of interest, such as a tumor or lesion, to control or kill cells of the tissue of interest. In another example, an EBRT system includes yet another therapy system. An example of the treatment system 108 is described in further detail below.

[0046] In one example, the treatment system 108 generates and / or executes a treatment plan. The treatment plan is generated based at least on the motion characterization of the tissue of interest generated by the motion characterization module 128 of the computing system 104. In addition, the treatment plan includes modulation information for modulating the treatment beam to track the treatment beam to the moving tissue of interest. The treatment plan can be stored in the treatment system 108, the data repository 110, the multi-modality imaging system 106, the computer-readable storage medium 122, and / or another device.

[0047] The system 102 also includes a data repository 110. In one example, the data repository 110 includes a radiology information system (RIS), a hospital information system (HIS), an electronic medical record (EMR), a picture archiving and communication system (PACS), a server, a database, a cloud-based service, and / or other storage media. In one example, a data repository in the data repository 110 is remote from all computer-readable storage media 122, the multi-modality imaging system 106, and the treatment system 108. In another example, a data repository in the data repository 110 is local to at least one of the computer-readable storage media 122, the multi-modality imaging system 106, and the treatment system 108.

[0048] Figure 2 A non-limiting example of a short frame PET generation module 124 is schematically shown. The short frame PET generation module 124 receives as input acquired PET data of tissue of interest that is moving. The PET data includes at least event data from the tissue of interest that moved during the PET scan during which the PET data was acquired.

[0049] In one example, the PET data is generated by the first imaging system 130 or the Nth imaging system 132 of the multimodal imaging system 106, whichever is configured for PET imaging. In another example, the PET data is generated by one or more other imaging systems configured for PET imaging. The PET data may be obtained (e.g., received and / or retrieved) from the multimodal imaging system 106, one or more other imaging systems configured for PET imaging, the data repository 110, the treatment system 108, and / or other systems.

[0050] The short frame PET generation module 124 includes a coincidence event detector 202. The coincidence event detector 202 is configured to identify coincidence events in the PET data. For example, the coincidence event detector 202 identifies a pair of events as coincidence events, where the events are detected along a line of response (LOR) within a predetermined time of each other to indicate coincidence. The pair of detected events determined to be coincidence events are recorded as coincidence events. Events that cannot be paired are discarded.

[0051] The short frame PET generation module 124 includes a time interval library 204. The time interval library 204 includes at least one time duration for defining a time window within which a short PET frame is generated. For example, in the case where the motion cycle is a breathing cycle, the at least one time duration may be about 0.5 seconds. Generally, the time duration is based on the motion cycle of interest and is less than the period of the motion cycle of interest. In the case of generating short PET frames for consecutive time windows, a first PET frame is generated using PET data acquired during the first 0.5 seconds of the scan, a next PET frame is generated using PET data acquired during the next 0.5 seconds of the scan, ..., and a final PET frame is generated using PET data acquired during the last 0.5 seconds of the scan.

[0052] In another example, one or more of the time windows may overlap. For example, a first PET frame may be generated using PET data acquired from 0.0 to 0.5 seconds of a scan, a next PET frame may be generated using PET data acquired from 0.4 to 0.9 seconds of a scan, ... In another example, one or more of the time windows may include time gaps between PET data that are not used to generate a PET frame. For example, a first PET frame may be generated using PET data acquired from 0.0 to 0.5 seconds of a scan, a next PET frame may be generated using PET image data acquired from 0.6 to 1.0 seconds of a scan, ... Other overlaps and / or time gaps are contemplated herein, including, for example, overlaps and / or time gaps that vary based on phase.

[0053] In one example, the time interval is a default value. In one example, the default value is a constant (e.g., about 0.5 seconds). In another example, the time interval changes, for example, where the cyclic motion changes. In another example, the time interval library 204 includes multiple time intervals. Such intervals can be based on tissues of interest, cyclic motion sources, imaging centers, radiologists assigned to read PET data, etc. In another example, the operator can, for example, change or specify the time interval from a list of optional time intervals. In another example, the short frame PET generation module 124 automatically selects the time interval from a list of optional time intervals based on, for example, tissues of interest, cyclic motion sources, etc.

[0054] The short frame PET generation module 124 also includes a frame processor 206. The frame processor 206 is configured to generate short PET frames with coincident pairs in the bins for all bins in the data buffer and / or other memory. Thus, each short PET frame will represent a phase within a motion cycle, and a set of short PET frames will represent phases over multiple different motion cycles. The short frame PET generation module 124 can employ known iterative and / or other techniques to generate short PET frames. In one example, the frame processor 206 is configured to apply attenuation and / or scatter correction. In another example, the frame processor 206 does not apply attenuation or scatter correction.

[0055] Each short PET frame in the set of short PET frames represents a phase of a corresponding motion cycle in the plurality of motion cycles, and a combination or superposition of the short PET frames represents motion within the plurality of motion cycles. Figure 3 A non-limiting example of one of the short PET frames is provided showing the location 302 of tissue of interest 304 during one phase, including the motion of the tissue during the phase. Figure 4 A non-limiting example of another of the short PET frames is provided, showing the position 402 of the tissue of interest 304 in another phase, including the motion of the tissue during the phase. Figure 5 A non-limiting example of an overlay of such frames of tissue of interest 304 across all frames is provided, illustrating the range of motion 502. Figure 5 , positions 302 and 402 represent the end points of the motion cycle.

[0056] return Figure 2 In another example, the frame processor 206 uses the algorithm described in patent US11,179,128B2 (serial number US16 / 732,250) filed on December 31, 2019, entitled "Methods and Systems for Motion Detection in Positron Emission Tomography" to reconstruct short PET frames, which is incorporated herein by reference in its entirety. In US11,179,128B2, a series of real-time PET frames are generated during a defined duration while the emission data is being acquired. In a variation, the frame processor 206 uses the algorithm of US11,179,128B2 to generate a set of short PET frames from fully acquired PET data after a PET scan rather than during acquisition.

[0057] The short PET frame sets may be stored in the computing system 104 , the multi-modality imaging system 106 , the data repository 110 , the treatment system 108 , and / or other devices.

[0058] Figure 6A non-limiting example of a motion detection module 126 is schematically shown. The motion detection module 126 obtains as input a set of short PET frames generated by the frame processor 206 of the short frame PET generation module 124 and / or other processors. The short PET frame set may be obtained (e.g., received and / or retrieved) from the multi-modality imaging system 106, the data repository 110, the treatment system 108, and / or other devices.

[0059] The motion detection module 126 includes a tissue of interest identifier 602. In one example, the tissue of interest includes a tumor or a lesion, and the tissue of interest identifier 602 identifies the tissue of interest in all or a subset (less than all) of the set of short PET frames. The tissue of interest identifier 602 utilizes known and / or other segmentation methods (including manual, semi-automatic, and / or automatic segmentation methods) to identify the tissue of interest. Several suitable non-limiting examples of such methods are discussed below.

[0060] Non-limiting methods include K-means clustering. With this method, K cluster centers are selected randomly or based on a heuristic method, each pixel in the image is assigned to the cluster that minimizes the distance between the pixel and the cluster center, and then the cluster center is recalculated by averaging all pixels in the cluster, and the assignment and recalculation steps are repeated until a stopping criterion is reached (e.g., convergence of no pixel changes cluster). K can be selected manually, randomly, and / or by a heuristic method, the distance is the squared difference or absolute difference between the pixel and the cluster center, and the difference can be based on pixel color, intensity, texture, and position, or a weighted combination thereof.

[0061] Another non-limiting method includes a histogram-based method, wherein a histogram is calculated from pixels in the image, and the peaks and valleys in the histogram are used to locate the clusters in the image via color or intensity. Another non-limiting method includes edge detection, such as searching, zero crossing and / or other edge detection techniques for finding edges. Another non-limiting method includes thresholding, which uses a threshold value to convert a grayscale image into a binary image. Another non-limiting method includes an artificial intelligence method (e.g., a pulse coupled neural network, U-Net, etc.). Another non-limiting method includes a region growing method. Other methods are also contemplated herein.

[0062] The motion detection module 126 also includes a motion estimator 604. The motion estimator 604 determines the absolute position of the identified tissue of interest in two dimensions and / or three dimensions relative to a reference frame in all or a subset (less than all) of the short PET frames in which the tissue of interest was identified. The motion estimator 604 determines the position using known and / or other methods, including manual, semi-automatic, and / or automatic methods. Suitable non-limiting examples of such methods are discussed below.

[0063] Non-limiting methods include centroid analysis. Generally speaking, the centroid is a weighted average of pixels and can be calculated, for example, by summing the pixels, multiplying each pixel by its value, and dividing the result by the sum of the values. The centroid provides the location of the center of each short PET frame in the short PET frame. The centroid of the short PET frame can then be tracked through the short PET frame to track the movement of the tissue of interest. Another non-limiting method includes registering the frames to determine the displacement of the identified tissue of interest from frame to frame. Other methods are contemplated herein.

[0064] The location of the tissue of interest in each short PET frame may be stored with the frame in a vector, a data structure (e.g., in a header, metadata, a DICOM field, etc.), etc. Turning briefly to Figure 7 , a graphical representation 702 of an example of a set of positions is shown. The graphical representation 702 includes a first axis 704 representing the position of the tissue of interest and a second axis 706 representing time. The graphical representation 702 shown includes a graph 708 of the tissue position values ​​of interest over time. An example of the change in motion for each cycle is shown in the graphical representation 702, for example, by peaks and valleys.

[0065] return Figure 6 The set of locations of tissues of interest (whether embedded with and / or separate from corresponding short PET frames) may be stored in the computing system 104, the data repository 110, the multi-modality imaging system 106, the treatment system 108, and / or other devices.

[0066] Figure 8 A non-limiting example of a motion characterization module 128 is schematically shown. The motion characterization module 128 obtains as input a set of position values ​​of the tissue of interest determined by the motion detection module 126. In one example, the motion characterization module 128 is configured to characterize the motion of the tissue of interest over all motion cycles based on the set of positions of the tissue of interest. The set of positions may be obtained (received and / or retrieved) from the computing system 104, the data repository 110, the multimodal imaging system 106, the treatment system 108, and / or other devices.

[0067] The motion characterization module 128 includes a motion cycle identifier 802. The motion cycle identifier 802 identifies each cycle of motion of the tissue of interest based on a set of locations of the tissue of interest. In one example, the motion cycle identifier 802 identifies each cycle based on local minima and maxima in the set of locations of the tissue of interest. Generally speaking, the minima and maxima identify inflection points, or points in the set of locations of the tissue of interest where the motion of the tissue of interest changes direction.

[0068] Briefly turn to Fig. 9, a graphical representation 902 of an exemplary set of tissue location values ​​of interest including local minima and maxima is shown. Fig. 9 Basically similar to Figure 7 , but with graphical symbols added to identify local minima and maxima. For example, Fig. 9 Graph 708 including tissue location values ​​of interest ( Figure 7 ) and additionally includes a graphical symbol “*” identifying a local minimum (e.g., at 908 and 910) and another graphical symbol “0” identifying a local maximum (e.g., at 912 and 914).

[0069] return Figure 8 , the motion cycle identifier 802 identifies cycles from minimum to minimum or from maximum to maximum, for example, identifying the start of each cycle based on a local minimum or maximum. Fig.10 , showing that from Fig. 9 The local minimum 908 passes through Fig. 9 The local maximum value of 912 to Fig. 9 A graphical representation 1002 of an example of a cycle of local minima 910 of . Turning briefly to Fig.11 , showing that from Fig. 9 The local maximum value 912 passes through Fig. 9 The local minimum value of 908 to Fig. 9 A graphical representation 1102 of an example of a cycle of local maxima 914 .

[0070] In the above, motion may be derived from a full reconstruction, a point cloud representation of the TOF data, a simple back-projection of the data, or a centroid analysis of short duration sinusoids and / or otherwise.

[0071] return Figure 8 , the motion characterization module 128 also includes a segmenter 804. The segmenter 804 is configured to segment each loop into M segments, where M is an integer greater than one. Examples of M include 24, 15, 100, less, more, etc. In some instances, M is a static value. In another instance, M is adjustable. The value of M can be a predetermined default value. In another instance, M is user defined. Simply turn to Fig.12 , Fig.10 The loop 1002 in is segmented into M segments 1202 1 ,1202 2 ,……,1202 M , wherein the length of each segment corresponds to the displacement value, for example, one millimeter (1 mm), etc. In one example, the M segments are of equal size. In another example, at least one segment of the M segments has a different size relative to another segment of the segments.

[0072] Return Figure 8 In addition, the motion characterization module 128 further includes a resampler 806. The resampler 806 is configured to resample the position values in the set of positions of the tissue of interest from position values over time to bins of position values per segment. For example, resample the loop 1002 in Fig.12 into M bins such that the position values of segment 1202 1 are in bin 1 , the position values of segment 1202 2 are in bin 2 , ……, and the position values of segment 1202 M are in bin M . This is performed for multiple (e.g., all or many but not all) of the identified loops (e.g., the loops shown in Fig. 9 ). Thus, in one instance, bin 1 will include all the position values of the first segment of all the loops, etc.

[0073] Return Figure 8 In addition, the motion characterization module 128 further includes an analyzer 808. The analyzer 808 is configured to determine the statistical values for each bin. Examples of statistical values include but are not limited to mean, standard deviation, etc. Thus, in one instance, the analyzer 808 calculates the mean and standard deviation for each of bin 1 , bin 2 , ……, and bin M . Briefly turning to Fig.13 , a graphical representation 1302 of an example of the statistical data is shown. The graphical representation 1302 includes a first axis 1304 that represents the displacement of the tissue of interest. The graphical representation 1302 also includes a second axis 1306 that represents the bins. The graphical representation 1302 shown includes a box plot that has an interquartile range 1308 for each bin to indicate the position where the motion is expected to be at half of the time, a mean 1310 for each interquartile range 1308, and whiskers 1312 that extend from each interquartile range 1308 of each bin to indicate the range of motion outside the interquartile range 1308.

[0074] Return Figure 8 In addition, the statistical data can be stored in the computing system 104, the repository 110, the treatment system 108, the multimodal imaging system 106, and / or other devices. The statistical data can be stored in a format similar to the box plot of Fig.13 and / or other formats.

[0075] Turning to Fig.14, schematically illustrates a non-limiting example of a cross-sectional side view of a portion of the multi-modality imaging system 106. The first imaging system 130 is configured for PET imaging, and the Nth imaging system 132 is configured for CT imaging. Fig.15 A non-limiting example of a front view of a first (PET) imaging system 130 is schematically shown, and Fig.16 A non-limiting example of a front view of the Nth (CT) imaging system 132 is schematically shown.

[0076] refer to Fig.14 and Fig.15 , the first computing system 104 includes a PET gantry 1402. The PET gantry 1402 includes a radiation sensitive detector array 1404 disposed in a generally annular ring around a PET examination region 1406. The radiation sensitive detector array 1404 includes a plurality of detectors (detector crystals) in optical communication with a scintillator material (scintillator crystals) disposed between the plurality of detectors and the PET examination region 1406.

[0077] The scintillator material converts 511 keV gamma radiation 1408 generated in response to a positron annihilation event 1410 occurring in an examination region 1406 of a patient 1412 disposed therein into photons, and a plurality of detectors convert the photons into electrical signals. The plurality of detectors include one or more photosensors, such as an avalanche photodiode, a photomultiplier tube, a silicon photomultiplier tube, and / or another type of photosensor.

[0078] The first imaging system 130 also includes a PET data acquisition system (DAS) 1414. The PET data acquisition system 1414 receives data from the radiation sensitive detector array 1404 and generates projection data including a list of events detected by the plurality of radiation sensitive detectors 1404. The PET data acquisition system 1414 identifies coincident gamma pairs by identifying events detected coincidentally (or nearly simultaneously) in time along a line of response (LOR), which is a straight line connecting two detectors that detected an event, and generates list mode data indicating the events on an event-by-event basis.

[0079] Coincidence can be determined by many factors, including event time stamps, which must be within a predetermined time period of each other to indicate coincidence, and LORs. Events that cannot be paired can be discarded. Events that can be paired are located and recorded as coincident event pairs. PET projection data provides information about the LOR of each event, such as the lateral and longitudinal positions of the LORs and the lateral and azimuth angles.

[0080] Where the PET imaging system 130 is configured for time of flight (TOF), the PET projection data may also include TOF information that allows the location of an event to be estimated along the LOR. For example, when a positron annihilation event occurs closer to a first detector crystal than a second detector crystal, one annihilation photon may arrive at the first detector crystal before (e.g., nanoseconds or picoseconds before) another annihilation photon arrives at the second detector crystal. The TOF difference may be used to constrain the location of the positron annihilation event along the LOR.

[0081] Additionally or alternatively, the PET projection data is rebinned into one or more sinograms or projection bins. A PET reconstructor 1416 reconstructs the PET projection data using known iterative or other techniques to generate volumetric image data (i.e., PET image data) indicating the distribution of the radionuclide in the scanned object. The PET image data can be co-registered with the CT image data, and the CT image data can be used to generate an attenuation map for attenuation and / or other desired corrections to the PET image data.

[0082] Typically, prior to a PET scan, a radionuclide (radiotracer) is administered to the patient. Radionuclides may include fluorine-18, carbon-11, nitrogen-13, oxygen-15, etc. Radionuclides may be incorporated into molecules that are metabolized by the body, or incorporated into molecules that bind to receptor targets. When radionuclides accumulate in organs, blood vessels, etc., the radionuclides undergo positron emission decay and emit positrons, which collide with electrons in surrounding tissues.

[0083] When a positron collides with an electron, both the positron and the electron annihilate and are converted into a pair of photons or gamma rays, each having an energy of 511 keV. The two photons are directed in substantially opposite directions and are detected when they each arrive at a respective detector positioned relative to each other on the detector ring assembly. The two detectors that detect the coincident scintillation event are positioned substantially one hundred and eighty degrees from each other. When a photon impinges on a scintillation crystal, a scintillation event (e.g., a flash of light) is generated, and the detector crystal detects the scintillation event and generates a signal indicative thereof.

[0084] refer to Fig.14 and Fig.16 , the Nth imaging system 132 includes a CT gantry 1418. The CT gantry 1418 includes a radiation sensitive detector array 1420 disposed in an annular ring around a CT examination region 1422. The CT gantry 1418 also includes a radiation source 1424, such as an X-ray tube, that rotates around the CT examination region 1422. The radiation sensitive detector 1420 detects radiation 1426 emitted by the radiation source 1424 that has passed through the examination region 1422 and the subject 1412 therein.

[0085] The radiation source 1424 and the radiation sensitive detector array 1420 are disposed relative to each other on a rotating frame 1428 across the CT examination region 1422. The rotating frame 1428 rotates the X-ray source 1424 in coordination with the array of X-ray radiation detectors 1420. The X-ray source 1424 emits X-ray radiation 1428 that traverses the examination region 1422 and the subject 1412 disposed therein, and the X-ray radiation detector array 1420 detects the X-ray radiation incident thereon. For each arc segment, the array of X-ray radiation detectors 1420 generates a projection view.

[0086] A CT data acquisition system (DAS) 1430 processes signals from the CT detector 1420 to generate data indicative of radiation attenuation along a plurality of lines or rays passing through the examination region 1422. A CT reconstructor 1432 reconstructs the data using a reconstruction algorithm to generate volumetric image data (i.e., CT image data) indicative of radiation attenuation of the object 1412. Suitable reconstruction algorithms include an analytical image reconstruction algorithm such as forward back projection (FBP), an iterative reconstruction algorithm such as advanced statistical iterative reconstruction (ASIR), a maximum likelihood expectation maximization (MLEM) algorithm, another algorithm, and / or a combination thereof.

[0087] The subject support 1436 includes a table 1438 movably coupled to a frame / base 1440. In one example, the table 1438 is slidably coupled to the frame / base 1440 via bearings or the like, and a drive system (not visible) including a controller, motor, lead screw, and nut (or other drive system) translates the table 1438 along the frame / base 1440 into and out of the examination region 1422 or 1406. The table 1438 is configured to support an object or subject in the examination region 1422 or 1406 for loading, scanning, and / or unloading the subject or object.

[0088] In one embodiment, the multi-modality imaging system 106 includes a controller 1434 to control the movement of components, such as rotation of the gantry 1428, operation of the x-ray source 1424, operation of the detector arrays 1404 and 1420, operation of the subject support 1436, etc. For example, in one embodiment, the controller 1434 includes an x-ray controller configured to provide power and timing signals to the x-ray source 1424. In another example, the controller 1434 includes a gantry motor controller configured to control the rotation speed and / or position of the gantry 1428 based on imaging requirements. In yet another example, the controller 1434 includes a subject support controller configured to control the movement and / or height of the subject support 1436 for loading, scanning, and / or unloading the patient 1412.

[0089] refer to Fig.13 , Fig.14 and Fig.15 In one example, the PET examination region 1406 and the CT examination region 1422 are disposed along a common longitudinal axis or z-axis 1442. The multi-modality imaging system 106 also includes an operator console 1444 configured to control the first imaging system 130 and the Nth imaging system 132. The operator console 1444 can be substantially similar to the computing system 104 in that it can include a processor, a computer-readable storage medium, an I / O interface, input and output devices, etc. In another example, the first imaging system 130 and the Nth imaging system 132 include their own operator consoles.

[0090] Fig.17 Schematically illustrated is a non-limiting example of a treatment system 110. In one example, the treatment system 110 includes a linear accelerator 1702. The linear accelerator 1702 includes a stationary gantry 1704 and a rotating gantry 1706 rotatably attached to the stationary gantry 1704. The rotating gantry 1706 rotates about a treatment region 1710 (e.g., 180 degrees, etc.) relative to a rotation axis 1708.

[0091] A subject support 1712 supports a portion of a subject in a treatment region 1710. The rotating gantry 1706 includes a treatment head 1714 having a therapy (e.g., megavolt, MV) radiation source 1716 that delivers therapeutic radiation and a collimator 1718 (e.g., a multi-leaf collimator) that can shape the radiation field leaving the treatment head 1714 into different shapes. The radiation source 1716 rotates around the treatment region 1710 in coordination with the rotating gantry 1706. The collimator 1718 includes a set of jaws that can move independently to shape the field.

[0092] The controller 1720 is configured to control the rotation of the rotating gantry 1706 and deliver therapeutic radiation via the megavoltage radiation source 1716 during treatments such as intensity modulated proton therapy (IMPT), volumetric modulated arc therapy (VMAT), and / or other radiation therapies. The controller 1720 is also configured to control the linear accelerator 1702 for one or more other modes, such as step and shoot delivery at a set of beam positions, combined volumetric arc and step and shoot delivery, and one or more coplanar or non-coplanar arc delivery.

[0093] The linear accelerator 1702 includes an operator console 1722. The operator console 1722 can be substantially similar to the computing system 104 in that it can include a processor, a computer-readable storage medium, an I / O interface, input and output devices, etc. The operator console 1722 can include a treatment planner module. In another example, the treatment planner module is located on another computing system. The treatment planner generates a radiation treatment plan.

[0094] In one example, the operator employs the characterized motion generated by the motion characterization module 128 to facilitate generating a treatment plan via the treatment planning module. For example, in one example, the characterized motion is employed to plan modulation of the treatment beam based on the expected motion of the tissue of interest. In one example, the modulation includes, for example, changing the range of the beam via the collimator 1718. Additionally or alternatively, the modulation includes shutting off or blocking the beam (e.g., via the beam collimator 1718) so as not to irradiate the tissue of interest during certain phases, for example, based on Fig.13 Box plots and / or other information for stages with large interquartile ranges.

[0095] Fig.18 Another non-limiting example of a cross-sectional side view of a portion of the multi-modality imaging system 106 is schematically shown. The first imaging system 130 is configured for PET imaging, such as in combination with Fig.14 and Fig.15 However, the Nth imaging system 132 is configured for MR imaging and will be described next.

[0096] The Nth imaging system 132 includes an MR gantry 1802. The MR gantry 1802 includes a main magnet 1804, gradient (x, y, and z) coils 1806, and an RF coil 1808. The main magnet 1804 (which may be a superconducting, resistive, permanent, or other type of magnet) generates a substantially uniform, temporally constant main magnetic field B0 in an MR examination region 1810. The gradient coils 1806 generate time-varying gradient magnetic fields along the x, y, and z axes of the MR examination region 1810. The RF coil 1808 includes a transmit portion that generates a radio frequency signal (at the Larmor frequency of the nucleus of interest (e.g., hydrogen, etc.)) that excites the nuclei of interest in the examination region 1810, and a receive portion that detects the MR signals emitted by the excited nuclei. In other embodiments, the transmit portion and the receive portion of the RF coil 1808 are located in separate RF coils 1808. An MR data acquisition system (DAS) 1812 processes the MR signals, and an MR reconstructor 1814 reconstructs the data and generates an MR image.

[0097] The PET examination region 1406 and the MR examination region 1810 are arranged along a common longitudinal axis or z-axis 1442. The multi-modality imaging system 106 also includes an operator console 1816 configured to control the first imaging system 130 and the Nth imaging system 132, including a subject support 1436 for both PET and MR scanning. The operator console 1816 can be substantially similar to the computing system 104, in that it can include a processor, a computer-readable storage medium, an I / O interface, input and output devices, etc. In another example, the first imaging system 130 and the Nth imaging system 132 include their own operator consoles.

[0098] In a variation, an external device such as a respiratory bellows, spirometer, optical tracker, etc. may be used to detect cyclic motion that causes tissue to move during a PET scan. The cyclic motion from the external device may then be correlated with and mapped to a motion representation determined from the acquired PET data. The cyclic motion detected by the external device may then be used to estimate the position of the tissue of interest based on the mapping between the cyclic motion from the external device and the motion representation determined from the PET data during treatment and / or other procedures.

[0099] In another variation, the estimated motion provides a direct measure of internal motion, which allows for amplitude-based gating without external hardware, which mitigates ambiguity due to amplitude variations of conventional gated imaging. Using amplitude-based gating, which is a known technique, a specific gate is constructed from the time interval at which the amplitude of the motion reaches a specific point, and a large amount of data is generated based on the gate. In one example, a set of motion amplitude ranges (i.e., gates) is determined based on the estimated motion. PET data from each motion cycle corresponding to a motion amplitude range of interest in the set of motion amplitude ranges is identified. The identified PET data is then combined to generate a frame of PET data. The result can be used to generate a movie showing all motion while retaining full count statistics in each frame.

[0100] Fig.19 A non-limiting example of amplitude-based gating is shown. A first axis 1902 represents amplitude, a second axis 1904 represents time, and a curve 1906 represents multiple motion cycles. In the example shown, the estimated motion is used to determine a range of motion amplitudes of interest 1908. PET data from each cycle corresponding to the range of motion amplitudes of interest 1908 is identified. As shown, one or more cycles (e.g., cycle 1910 from 1912 to 1914) may not have PET data within the range of motion amplitudes of interest 1908. For such cycles, it is clear that no PET data is identified for the range of motion amplitudes of interest 1908. In general, depending on the amplitude of a given respiratory cycle, the relationship between trigger time and data aggregation changes.

[0101] Fig. 20 A non-limiting example of a flow chart of a computer-implemented method is shown. It should be understood that the order of the actions in the method is not limiting. Therefore, other orders are contemplated herein. In addition, one or more actions may be omitted, and / or one or more additional actions may be included.

[0102] At 2002, PET data of a moving tissue of interest is obtained as described herein and / or otherwise. At 2004, the PET data is divided into a plurality of short time frames based on a predetermined duration as described herein and / or otherwise. At 2006, the divided PET data is processed as described herein and / or otherwise to generate a set of short PET frames.

[0103] At 2008, tissue of interest is identified in each of the short PET frames as described herein and / or otherwise. At 2010, motion of the tissue of interest is estimated using the set of short PET frames as described herein and / or otherwise. At 2012, motion of the tissue of interest is characterized based on the set of PET frames and the estimated motion as described herein and / or otherwise. The characterized motion can be used, for example, to plan treatment and / or other procedures as described herein and / or otherwise.

[0104] Fig.21 A non-limiting example of a flow chart of a computer-implemented method is shown. It should be understood that the order of the actions in the method is not limiting. Therefore, other orders are contemplated herein. In addition, one or more actions may be omitted, and / or one or more additional actions may be included.

[0105] At 2102, a motion position value of tissue of interest determined from PET data of the moving tissue of interest is obtained as described herein and / or otherwise (eg, Fig. 20 At 2104, each motion cycle of the tissue of interest in the plurality of motion cycles captured in the PET data is identified based on the obtained motion position values ​​of the tissue of interest as described herein and / or otherwise. At 2106, each motion cycle of the tissue of interest is segmented into a plurality of segments as described herein and / or otherwise.

[0106] At 2108, the position values ​​of the tissue of interest are resampled from the position values ​​over time into bins of position values ​​for each segment as described herein and / or otherwise. At 2110, the position values ​​over each bin are statistically analyzed as described herein and / or otherwise. At 2112, the motion is characterized based on the results of the statistical analysis as described herein and / or otherwise, the statistical analysis characterizing the motion over different phases of the motion cycle. The characterized motion can be used, for example, to plan treatments and / or other procedures as described herein and / or otherwise.

[0107] The above method can be implemented by computer-readable instructions encoded or embedded on a computer-readable storage medium, which, when executed by a computer processor, causes the processor to perform the described actions or functions. Additionally or alternatively, at least one of the computer-readable instructions is executed by a signal, carrier wave, or other transient medium that is not a computer-readable storage medium.

[0108] As used herein, the element or step that is listed in the singular and begins with the word "one" or "a kind of" should be understood as not excluding a plurality of the elements or steps, unless such exclusion is explicitly stated. In addition, the reference to "an embodiment" of the present invention is not intended to be interpreted as excluding the existence of the additional embodiment that also includes the quoted feature. In addition, unless explicitly stated to the contrary, the embodiment of "including", "comprising" or "having" an element or multiple elements with a specific attribute may include such additional elements that do not have the attribute. The terms "including" and "in..." are used as the concise language equivalents of the corresponding terms "including" and "wherein". In addition, the terms "first", "second" and "third" etc. are only used as marks, and are not intended to impose numerical requirements or specific position order on their objects.

[0109] Various embodiments and / or components (e.g., modules or components and controllers therein) may also be implemented as part of one or more computers or processors. A computer or processor may include a computing device, an input device, a display unit, and an interface, such as for accessing the Internet. A computer or processor may include a microprocessor. The microprocessor may be connected to a communication bus. A computer or processor may also include a memory. The memory may include a random access memory (RAM) and a read-only memory (ROM). A computer or processor may also include a storage device, which may be a hard drive or a removable storage drive, such as a floppy disk drive, an optical drive, etc. A storage device may also be other similar devices for loading a computer program or other instructions into a computer or processor.

[0110] As used herein, the term "computer" or "module" may include any processor-based or microprocessor-based system, including systems using microcontrollers, reduced instruction set computers (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuits or processors capable of performing the functions described herein. The above examples are exemplary only, and are therefore not intended to limit the definition and / or meaning of the term "computer" in any way. A computer or processor executes an instruction set stored in one or more storage elements to process input data. Storage elements may also store data or other information as desired or required. Storage elements may be in the form of an information source or physical memory element within a processing machine.

[0111] The instruction set may include various commands that instruct a computer or processor to perform specific operations (such as the methods and processes of various embodiments of the present invention) as a processing machine. The instruction set may be in the form of a software program. The software may be in various forms, such as system software or application software. In addition, the software may be in the form of a collection of separate programs or modules, a program module within a larger program, or a portion of a program module. The software may also include modular programming in the form of object-oriented programming. The processing of input data by the processing machine may be in response to an operator command, or in response to the results of a previous process, or in response to a request made by another processing machine.

[0112] As used herein, the terms "software" and "firmware" are interchangeable and include any computer program stored in a memory for execution by a computer, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are exemplary only, and thus do not limit the types of memory that can be used to store computer programs.

[0113] It should be understood that the above description is intended to be illustrative and not restrictive. For example, the above embodiments (and / or aspects thereof) can be used in combination with each other. In addition, without departing from the scope of the present invention, many modifications can be made to adapt specific situations or materials to the teachings of the various embodiments of the present invention. Although the size and type of the material described herein are intended to limit the parameters of the various embodiments of the present invention, these embodiments are by no means restrictive but exemplary embodiments. After reviewing the above description, many other embodiments will be apparent to those skilled in the art.

[0114] This written description uses examples to disclose various embodiments of the invention, including the best mode, and also to enable those skilled in the art to practice the various embodiments of the invention, including making and using any device or system and performing any included method. The patentable scope of the various embodiments of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to fall within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if the examples include equivalent structural elements that have minor differences from the literal language of the claims.

[0115] The embodiments of the present disclosure shown in the drawings and described above are exemplary embodiments only and are not intended to limit the scope of the appended claims, including any equivalents included within the scope of the claims. Various modifications are possible and will be apparent to those skilled in the art. It is intended that any combination of non-mutually exclusive features described herein is within the scope of the present disclosure. That is, the features of the embodiments may be combined with any appropriate aspect described above, and the optional features of any one aspect may be combined with any other appropriate aspect. Similarly, the features listed in the dependent claims may be combined with non-mutually exclusive features of other dependent claims, particularly where the dependent claims are dependent on the same independent claim. In some jurisdictions that require single claim dependencies, these dependencies may have been used as a practice, but this should not be taken to mean that the features in the dependent claims are mutually exclusive.

Claims

1. A computer-implemented method, the computer-implemented method comprising: obtaining positron emission tomography (PET) data of the tissue of interest acquired during a plurality of motion cycles of the moving tissue of interest; generating a set of short PET frames from the PET data based on a predetermined duration, the predetermined duration being less than a period of the plurality of motion cycles; identifying the tissue of interest in each short PET frame in the set of short PET frames; estimating a motion of the identified tissue of interest within each of the short PET frames; as well as The motion of the tissue of interest within the plurality of motion cycles is characterized based on the estimated motion of the tissue of interest identified within each of the short PET frames.

2. The computer-implemented method of claim 1 , further comprising: A procedure is planned for the tissue of interest based on the characterization of the motion of the tissue of interest.

3. The computer-implemented method of claim 1 , wherein the estimated motion comprises a position value of the tissue of interest for each of the short PET frames, and the computer-implemented method further comprises: identifying each motion cycle of the tissue of interest based on the position value of the tissue of interest in each of the short PET frames; as well as The motion of the tissue of interest is characterized based on the identified motion cycles.

4. The computer-implemented method of claim 3, further comprising: identifying a local minimum among the position values, a local maximum among the position values, or both a local minimum and a local maximum among the position values; as well as Each of the movement cycles is identified based on the identified local minima, local maxima, or local minima and local maxima.

5. The computer-implemented method of claim 3, further comprising: segmenting each of the identified motion cycles into a plurality of segments; resampling the position values ​​based on the segments into bins according to a motion phase; as well as The motion of the tissue of interest is characterized based on the bins of position values.

6. The computer-implemented method of claim 5, further comprising: Determine the statistical value of each bin; as well as The motion of the tissue of interest is characterized based on the statistical value.

7. The computer-implemented method of claim 6, wherein the statistical value comprises a mean and a standard deviation, and the computer-implemented method further comprises: The mean and the standard deviation are graphically represented.

8. The computer-implemented method of claim 1 , further comprising: determining a set of motion amplitude ranges based on the estimated motion; identifying PET data from each motion cycle corresponding to a motion amplitude range of interest in the set of motion amplitude ranges; as well as The identified PET data are combined to generate a PET data frame.

9. A computing system (104), the computing system comprising: a computer-readable storage medium (122) comprising instructions for characterizing motion of moving tissue of interest in PET data; and A processor (120) configured to execute the instructions, wherein the instructions cause the processor to: Acquire PET data of the tissue of interest acquired during a plurality of motion cycles of the moving tissue of interest; generating a set of short PET frames from the PET data based on a predetermined duration, the predetermined duration being less than a period of the plurality of motion cycles; identifying the tissue of interest in each short PET frame in the set of short PET frames; estimating a motion of the identified tissue of interest within each of the short PET frames; and The motion of the tissue of interest within the plurality of motion cycles is characterized based on the estimated motion of the tissue of interest identified within each of the short PET frames.

10. The computing system of claim 9, wherein the estimated motion comprises a position value of the tissue of interest for each of the short PET frames, and the instructions cause the processor to: identifying each motion cycle of the tissue of interest based on the position value of the tissue of interest in each of the short PET frames; and The motion of the tissue of interest is characterized based on the identified motion cycles.

11. The computing system of claim 10, wherein the instructions cause the processor to: identifying a local minimum among the position values, a local maximum among the position values, or both a local minimum and a local maximum among the position values; and Each of the movement cycles is identified based on the identified local minima, local maxima, or local minima and local maxima.

12. The computing system of claim 11, wherein the instructions cause the processor to: segmenting each of the identified motion cycles into a plurality of segments; resampling the position values ​​based on the segments into bins according to a motion phase; and The motion of the tissue of interest is characterized based on the bins of position values.

13. The computing system of claim 12, wherein the instructions cause the processor to: Determine the mean and standard deviation for each bin; and The motion of the tissue of interest is characterized based on a mean and a standard deviation of the bins.

14. The computing system of claim 13, wherein the instructions cause the processor to: The mean and the standard deviation are graphically represented.

15. A computer-readable storage medium encoded with computer-executable instructions that, when executed by a processor, cause the processor to: Acquire PET data of the tissue of interest acquired during a plurality of motion cycles of the moving tissue of interest; generating a set of short PET frames from the PET data based on a predetermined duration, the predetermined duration being less than a period of the plurality of motion cycles; identifying the tissue of interest in each short PET frame in the set of short PET frames; estimating a motion of the identified tissue of interest within each of the short PET frames; and The motion of the tissue of interest within the plurality of motion cycles is characterized based on the estimated motion of the tissue of interest identified within each of the short PET frames.

Images