Methods, systems, and / or computer readable media for local motion correction based on PET data

Abstract

A computer-implemented method includes: obtaining positron emission tomography (PET) data including a target moving tissue; and generating a set of short PET frames from the PET data, wherein each short PET frame in the set of short PET frames is based on a duration. The computer-implemented method further includes identifying a first target organization in the set of short PET frames and identifying at least a second target organization in the set of short PET frames. The computer-implemented method further includes separately and independently estimating a first motion of the first target tissue and a second motion of the second target tissue based on the short PET frames; and motion-correcting the first target tissue separately and independently for the first motion and motion-correcting the second target tissue for the second motion.

Description

Technical Field

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

[0002] Positron emission tomography (PET) is a functional imaging modality that utilizes radiopharmaceuticals with tissue-targeted radionuclides (i.e., radiotracers) to visualize and / or measure functional processes, such as metabolism, blood flow, absorption, etc. Prior to a PET scan, the radiopharmaceutical is administered to the patient. When the radionuclide accumulates in an organ, blood vessel, etc., the radionuclide undergoes positron emission decay and emits a positron. When the positron collides with an electron in the surrounding tissue, both the positron and the electron annihilate and are converted into a pair of photons or gamma rays, each with an energy of 511 keV.

[0003] The two photons are directed in substantially opposite directions along a line of response (LOR) and are detected in unison when they arrive at respective detectors positioned relative to each other on a detector ring assembly and spaced approximately one hundred and eighty degrees apart from each other. When the photons strike the scintillation crystals of the detectors, a scintillation event (e.g., a flash) is generated for each event, and the detectors detect the scintillation events and generate electrical signals indicative of the scintillation events. The electrical signals are processed to generate PET data representing the distribution of the radiopharmaceutical in the patient's body, which can be used to observe metabolic processes in the body and diagnose diseases, among other things.

[0004] A PET scan can take several minutes, for example, 8 to 10 minutes. As such, the acquired data may reflect moving tissue. Sources of such motion include periodic motion (such as breathing, heartbeat, etc.) and / or non-periodic motion (such as patient coughing, movement, etc.). Unfortunately, motion can manifest as visible artifacts, such as blur in the PET data. Existing methods of mitigating blur in PET data due to motion also generally tend to degrade image quality, such as reducing the signal-to-noise ratio. In view of at least the foregoing, there is an unresolved need for improved methods of mitigating motion-related artifacts in PET data. Summary of the invention

[0005] The various aspects described herein solve the above problems and other problems. The present summary 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.

[0006] This section will contain the independent claims written in paragraph form. Once the claims are approved, I will complete the section.

[0007] In one aspect, a computer-implemented method includes obtaining positron emission tomography (PET) data, the positron emission tomography (PET) data including target moving tissue. The computer-implemented method further includes generating a set of short PET frames from the PET data, wherein each short PET frame in the set of short PET frames is based on a duration. The computer-implemented method further includes identifying a first target tissue in the set of short PET frames. The computer-implemented method further includes identifying at least a second target tissue in the set of short PET frames. The computer-implemented method further includes separately and independently estimating a first motion of the first target tissue and a second motion of the second target tissue based on the short PET frames. The computer-implemented method further includes separately and independently motion correcting the first target tissue for the first motion and motion correcting the second target tissue for the second motion.

[0008] In another aspect, a computer system includes a computer-readable storage medium having instructions for correcting motion in data and a processor configured to execute the instructions. The instructions cause the processor to: obtain PET data including target moving tissue; generate a set of short PET frames from the PET data, wherein each short PET frame in the set of short PET frames is based on a duration; identify a first target tissue in the set of short PET frames; identify at least a second target tissue in the set of short PET frames; separately and independently estimate a first motion of the first target tissue and a second motion of the second target tissue based on the short PET frames; and separately and independently motion correct the first target tissue for the first motion and motion correct the second target tissue for the second motion.

[0009] In another aspect, a computer-readable medium is encoded with computer-executable instructions that, when executed by a processor, cause the processor to: obtain PET data including target moving tissue; generate a set of short PET frames from the PET data, wherein each short PET frame in the set of short PET frames is based on a duration; identify a first target tissue in the set of short PET frames; identify at least a second target tissue in the set of short PET frames; separately and independently estimate a first motion of the first target tissue and a second motion of the second target tissue based on the short PET frames; and separately and independently motion correct the first target tissue for the first motion and motion correct the second target tissue for the second motion.

[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 Schematically illustrated is a cross-sectional side view of a non-limiting example of an imaging system according to embodiments herein.

[0013] Figure 2 A non-limiting example of an imaging system configured for PET imaging according to embodiments herein is schematically illustrated.

[0014] Figure 3 A non-limiting example of an imaging system further configured for CT imaging according to embodiments herein is schematically illustrated.

[0015] Figure 4 A non-limiting example of a console of an imaging system according to embodiments herein is schematically illustrated.

[0016] Figure 5 A non-limiting example of a short frame generation module according to embodiments of the present invention is schematically illustrated.

[0017] Figure 6 Non-limiting examples of target tissue modules according to embodiments herein are schematically illustrated.

[0018] Figure 7 A PET MIP rendering according to embodiments herein is schematically illustrated.

[0019] Figure 8 The diagrammatically illustrates an embodiment according to the present invention Figure 7 A subsection of the PET MIP rendering.

[0020] Fig. 9 The diagrammatically illustrates an embodiment according to the present invention Figure 7 Selection of target tissue within a subsection of the PET MIP rendering.

[0021] Fig.10 The diagrammatically illustrates an embodiment according to the present invention Figure 7 Selection of another target tissue within a subsection of the PET MIP rendering.

[0022] Fig.11 Diagrammatically illustrated are selected target tissues defined by target regions according to embodiments herein.

[0023] Fig.12 Diagrammatically illustrated is a non-limiting display of PET data for aiding in definition of a target region in accordance with embodiments herein.

[0024] Fig.13 A non-limiting example of motion correction of a local block module according to embodiments herein is schematically illustrated.

[0025] Fig.14 An example motion in a target volume in the x-direction according to embodiments herein is graphically illustrated.

[0026] Fig.15 An example motion in a target volume in the y-direction is graphically illustrated according to embodiments herein.

[0027] Fig.16 An example motion in a target volume in the z-direction according to embodiments herein is graphically illustrated.

[0028] Fig.17 A non-limiting example of a local motion compensation module according to embodiments herein is schematically illustrated.

[0029] Fig.18 The display of a first block of motion correction according to embodiments herein is schematically illustrated.

[0030] Fig.19 The display of the second block of motion correction according to embodiments herein is schematically illustrated.

[0031] Fig. 20 The display of the third block of motion correction according to embodiments herein is schematically illustrated.

[0032] Fig.21 The display of a fourth block of motion correction according to embodiments herein is schematically illustrated.

[0033] Fig. 22 Diagrammatically illustrates the display of a sub-portion of a PET MIP rendering of a block with motion correction according to embodiments herein.

[0034] Fig.23 Schematically illustrates a system having an imaging subsystem configured for MR imaging according to embodiments of the present invention. Figure 1 variants.

[0035] Fig.24 A non-limiting example of a flowchart of a computer-implemented method according to embodiments herein is shown. DETAILED DESCRIPTION

[0036] Positron emission tomography (PET) is a functional imaging modality that utilizes radiopharmaceuticals including radionuclides (i.e., radiotracers) that target tissues to visualize and measure functional processes, such as metabolism. The radionuclides may include fluorine-18, carbon-11, nitrogen-13, oxygen-15, etc. Non-limiting examples of such radiopharmaceuticals include F-18 fluorodeoxyglucose (FDG), which includes a glucose analog with the positron-emitting radionuclide fluorine-18 replacing the normal hydroxyl group at the C-2 position in the glucose molecule. The uptake of FDG by tissues is a sign of tissue uptake of glucose, which is associated with certain types of tissue metabolism.

[0037] For PET scanning, the prescribed radioactive drug is first administered to the patient. When the radioactive drug accumulates in organs, blood vessels, etc., the radionuclide undergoes positron emission decay and emits positrons. When the positron collides with the electrons in the surrounding tissue, the positron and the electron are annihilated and converted into a pair of photons or gamma rays each having an energy of 511keV. The two photons are guided in substantially opposite directions along the line of response (LOR) and are detected in unison when they arrive at the corresponding detectors positioned relative to each other on the detector ring assembly and spaced approximately one hundred and eighty degrees apart from each other. The detector generates an electrical signal indicating it.

[0038] The electrical signals are processed to generate PET data that represents the distribution of the radiopharmaceutical in the patient's body, which can be used to observe metabolic processes in the body and diagnose diseases. Typically, PET scan data acquisition takes several minutes, for example, 8 to 10 minutes, and the acquired data may include moving tissue, which manifests as visible artifacts such as blur in the PET data, which can degrade image quality. Sources of this motion include periodic motion (such as breathing, heartbeat, etc.) and / or non-periodic motion (such as patient coughing, moving, etc.). Unfortunately, existing methods of mitigating blur in PET data due to motion often degrade image quality, for example by reducing the signal-to-noise ratio.

[0039] Described herein is a method of mitigating motion artifacts and, therefore, blur. The method generally includes: generating a series of short PET frames spanning the PET acquisition from acquired PET data; identifying at least two target tissues in a rendering of the PET data; separately and independently estimating motion of the at least two target tissues; and generating motion-corrected blocks of the at least two target tissues based on the estimated motion. In one example, each motion-corrected block is displayed. Additionally or alternatively, each motion-corrected block is inserted into a rendering of the PET data at a corresponding location, and the rendering of the PET data is displayed.

[0040] In one instance, since each target volume is local to the corresponding target tissue, the motion of each target tissue therein is typically rigidly moved due to periodic (e.g., breathing, etc.) and / or non-periodic motion (e.g., large patient movement), and a linear projector is processed. In another instance, the motion includes affine motion, thereby allowing shearing and scaling, and a linear projector is used again. The rigid motion of each target tissue in the local target volume is estimated using a short PET frame using known (e.g., registration, maximum pixel, center of mass, etc.) and / or other motion estimation methods. The target tissue in the local target volume has a strong signal that can be easily tracked. Tissue in other areas may have a weak signal that cannot be easily tracked, which means that the local target area cannot be placed anywhere. In addition, the full FOV of the PET data will further include tissues that move differently, and therefore rigid motion cannot be assumed.

[0041] A full motion-corrected reconstruction of a block of target tissue around each target tissue moves each measured event to undo the estimated rigid motion, thereby providing a quantitatively accurate block. Motion-correcting the block rather than the entire PET data will require fewer resources (e.g., processing power and / or memory), at least because the total number of events in the block will be lower than the total number of events in the PET data. Embodiments will now be described by way of example with reference to the accompanying drawings.

[0042] Figure 1 Schematically illustrated is a cross-sectional side view of a non-limiting example of an imaging system 102. In one example, the imaging system 102 includes a first imaging subsystem 104 and a second imaging subsystem 106. The first imaging subsystem 104 is configured for PET imaging, and the second imaging subsystem 106 is configured for computed tomography (CT) imaging. In another example, the first imaging subsystem 104 is configured for CT imaging, and the second imaging subsystem 106 is configured for PET imaging. In yet another example, the first imaging subsystem 104 and the second imaging subsystem 106 are separate imaging systems.

[0043] Figure 2 Schematically illustrates a front view of a first imaging subsystem 104 configured for PET imaging. Figure 1 and Figure 2 , the first imaging subsystem 104 includes a PET gantry 108. The PET gantry 108 includes a radiation sensitive detector array 110 arranged in a generally annular ring around a PET examination region 112. The radiation sensitive detector array 110 includes a plurality of detectors (photoelectric sensors) in optical communication with a scintillator material (scintillation crystals) disposed between the plurality of detectors and the PET examination region 112.

[0044] The scintillator material will respond to the patient 118 ( Figure 2 ) in the inspection region 112. Figure 2 ) and the 511keV gamma radiation 114( Figure 2 ) is converted into visible photons, and the plurality of detectors convert the photons into electrical signals. The plurality of detectors include one or more photosensors, such as avalanche photodiodes, photomultiplier tubes, silicon photomultiplier tubes, and / or another type of photosensor.

[0045] The first imaging subsystem 104 further includes a PET data acquisition system (DAS) 120. The PET data acquisition system 120 receives data from the radiation sensitive detector array 110 and generates PET data including a list of events detected by the plurality of radiation sensitive detectors 110. The PET data acquisition system 120 identifies coincident gamma pairs by identifying events detected in temporal coincidence (or near-simultaneity) along a line of response (LOR) that is a straight line connecting the two detectors that detected the events, and generates list mode data and / or a histogram (sinogram) indicative thereof.

[0046] Concordance can be determined by multiple factors, including event time stamps and LORs, which must be within a predetermined time period of each other to indicate concordance. Events that cannot be paired can be discarded. Events that can be paired are located and recorded as concordant event pairs. PET projection data provides information about the LOR of each event, such as the lateral and longitudinal positions of the LOR and the lateral and azimuth angles.

[0047] In the case where the first imaging subsystem 104 is configured for time of flight (TOF), the PET projection data may also include TOF information that allows the location of the 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.

[0048] Additionally or alternatively, the PET projection data is re-binned into one or more sinusoidal graphs or projection bins. The PET reconstructor 122 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.

[0049] Figure 3 Schematically illustrates a non-limiting example of a front view of the second imaging subsystem 104 configured for CT imaging. Figure 1 and Figure 3 , the second imaging subsystem 106 includes a CT gantry 124. The CT gantry 124 includes a radiation sensitive detector array 126 arranged in an annular ring around a CT examination region 128. The CT gantry 124 further includes a radiation source 130, such as an X-ray tube or source, that rotates around the CT examination region 128. The radiation sensitive detector 126 detects radiation emitted by the radiation source 130 that has passed through the examination region 128 and the subject 118 therein ( Figure 3 ) of radiation 132( Figure 3 ).

[0050] The radiation source 130 and the radiation sensitive detector array 126 are disposed opposite each other on a rotating frame 134 ( Figure 3 ). The rotating frame 134 rotates the X-ray source 130 in coordination with the X-ray radiation detector array 126. The X-ray source 130 emits X-ray radiation 132 that traverses the examination region 128 and the subject 118 disposed therein, and the X-ray radiation detector array 126 detects the X-ray radiation impinging thereon. For each arc segment, the X-ray radiation detector array 126 generates a projection view.

[0051] A CT data acquisition system (DAS) 136 processes signals from the CT detector 126 to generate data indicative of radiation attenuation along a plurality of lines or rays passing through the examination region 128. A CT reconstructor 138 reconstructs the data using a reconstruction algorithm to generate volumetric image data (i.e., CT image data) indicative of radiation attenuation of the patient 116. Suitable reconstruction algorithms include an analytical image reconstruction algorithm such as filtered back projection (FBP) or the like, an iterative reconstruction algorithm such as advanced statistical iterative reconstruction (ASIR), a maximum likelihood maximum expectation (MLEM) algorithm, another algorithm, and / or a combination thereof.

[0052] refer to Figure 1 , the subject support 140 includes a table 142 movably coupled to a frame / base 144. In one example, the table 142 is slidably coupled to the frame / base 144 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 142 along the frame / base 144 into and out of the examination region 128 and / or 112. The table 142 is configured to support an object or subject in the examination region 128 and / or 112 for loading, scanning, and / or unloading the subject or object.

[0053] The controller 146 is configured to control components such as rotation of the gantry 134, operation of the x-ray source 130, operation of the detector arrays 126 and / or 110, operation of the subject support 140, and the like. For example, in one embodiment, the controller 146 includes an x-ray controller configured to provide power and timing signals to the x-ray source 130. For another example, the controller 146 includes a gantry motor controller configured to control the rotational speed and / or position of the gantry 134 based on imaging requirements. In yet another example, the controller 146 includes a subject support controller configured to control the motion and / or height of the subject support 140 for loading, scanning, and / or unloading the patient 116. In the case where the first imaging subsystem 104 and the second imaging subsystem 106 are separate imaging systems, each of the subsystems 104 and 106 will have its own controller.

[0054] refer to Figure 1 , Figure 2 and Figure 3 , in one example, the PET examination region 112 and the CT examination region 128 are disposed along a common longitudinal axis or z-axis 150. In the case where the first imaging subsystem 104 and the second imaging subsystem 106 are separate imaging systems, each of the subsystems 104 and 106 will have its own subject support and z-axis. The imaging system 102 further includes an operator console 152 configured to control the imaging system 102. In one example, the operator console 152 is configured to receive image data from at least the PET DAS 120, the CT DAS 136, the PET reconstructor 122, and / or the CT reconstructor 138. In the case where the first imaging subsystem 104 and the second imaging subsystem 106 are separate imaging systems, each of the subsystems 104 and 106 will have its own operator console.

[0055] refer to Figure 1 , the imaging system 102 is in electronic communication with a data repository 154. In one example, the data repository 154 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 source, etc. In one example, the data repository 154 is configured to receive PET data and / or PET images from the imaging system 102 and / or send PET data and / or PET images to the imaging system 102, for example, via a Digital Imaging and Communications in Medicine (DICOM) protocol and / or other protocols.

[0056] Figure 4An example of an operator console 152 is schematically illustrated. The example operator console 152 includes a computing system such as a computer, workstation, server, etc., configured to operate the imaging system 102, including operating the first imaging subsystem 104 and the second imaging subsystem 106.

[0057] The operator console 152 includes an input device 402, such as a keyboard, a mouse, a touch screen, a microphone, etc. In one example, the input device 402 is configured to receive user input, such as selecting a target volume, etc. The operator console 152 further includes an output device 404, which includes a human-readable device such as a display monitor, etc. In one example, the output device 404 is configured to display PET data, PET images, etc. The operator console 152 further includes an input / output (I / O) 406 for sending and / or receiving signals and / or data.

[0058] The operator console 152 further includes a processor 410, such as a microprocessing unit (MPU), a central processing unit (CPU), a graphics processing unit (GPU), etc. The operator console 152 further includes a computer-readable storage medium 412, which includes non-transitory media (e.g., storage units, devices, etc.) and does not include transient media (i.e., signals, carrier waves, etc.). The computer-readable storage medium 412 is encoded with computer-executable instructions and / or data 414.

[0059] In one example, data 414 includes at least Figure 1 PET data and / or PET images received or retrieved by PET DAS 120, CT DAS 136, PET reconstructor 122, CT reconstructor 138, data repository 154, and / or other devices. In one example, data 414 further includes PET data, CT data, PET images, and / or CT images generated and / or manipulated by processor 410.

[0060] The processor 410 is configured to execute at least one of the computer executable instructions, to employ and / or generate data 414, etc. In one example, the computer executable instructions include a PET data viewing module 416, a short frame generation module 418, a target tissue (TOI) selection module 420, and a motion correction with local block module 422, which are briefly described below and are described in detail in the following. Figure 5 , Figure 6 , Fig.13 and Fig.17 Further described in more detail in .

[0061] The PET data viewing module 416 is configured to display the PET data, such as for selecting a target tissue, identifying a target volume, presenting a motion-corrected block, presenting the complete PET data with a motion-corrected block, etc. The short frame generation module 418 generally includes instructions for generating a short PET frame based on the acquired PET data and a predetermined duration. The target tissue (TOI) selection module 420 is configured to select at least two target tissues within a corresponding target region, respectively. The motion correction with local block module 422 is configured to separately and independently estimate the motion of the at least two target tissues, and separately and independently generate motion-corrected blocks of the at least two target tissues within the corresponding target region.

[0062] As described herein, individual motion-corrected tiles may be displayed and / or inserted into a rendering of the PET data. Again, target tissue in the local target volume has a strong signal that can be easily tracked, while tissue in other regions may have a weak signal that cannot be easily tracked, which means that the local target region cannot be placed anywhere, the full FOV of the PET data will further include tissue that moves differently, and thus rigid motion cannot be assumed, and the motion-corrected tiles rather than the entire PET data will require fewer resources (e.g., processing power and / or memory), at least because the total number of events will be lower than for the full PET data.

[0063] Figure 5 An example of a short frame generation module 418 is schematically illustrated. The short frame generation module 418 receives PET data as input, the PET data including data generated by moving target tissue. In one example, from Figure 1 PET DAS 120 and / or data repository 154, Figure 4 The PET data is received and / or retrieved by the data 414 and / or other devices. In one example, the short frame generation module 418 receives the PET data after the PET scan and generates the short PET frames from the PET data. In another example, the short frame generation module 418 receives the PET data when the PET data is acquired during the PET scan, and generates the short PET frames when the PET data is received.

[0064] The short frame generation module 418 includes a consistency event detector 502. The consistency event detector 502 is configured to identify consistent events in the PET data. For example, the consistency event detector 502 identifies a pair of events as consistent events, where the events are detected along a line of response (LOR) within a predetermined time of each other to indicate consistency. The detected event pair determined to be consistent events is recorded as consistent events. Events that cannot be paired are discarded.

[0065] The short frame generation module 418 includes a time interval library 504. The time interval library 504 includes at least one duration for defining a time window over which a short PET frame is generated. For example, in the case where the motion cycle is a respiratory cycle, the at least one duration may be about 0.5 seconds. In the case where short PET frames are generated 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 last 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.

[0066] 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 seconds to 0.5 seconds of a scan, a next PET frame may be generated using PET data acquired from 0.4 seconds to 0.9 seconds of a scan, ... In another example, one or more of the time windows may include a time gap between PET data that is not used to generate a PET frame. For example, a first PET frame may be generated using PET data acquired from 0.0 seconds to 0.5 seconds of a scan, a next PET frame may be generated using PET image data acquired from 0.6 seconds 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.

[0067] In one example, the time interval is a default value. In one example, the default value is a constant, such as about 0.5 seconds. In another example, the time interval varies, for example where the cyclic motion varies. In another example, the time interval library 504 includes multiple time intervals. Such intervals may be based on target tissue, the source of motion, the imaging center, the radiologist assigned to read the PET data, etc. In another case, the operator may change or specify the time interval, for example, from a list of optional time intervals. In another example, the short frame generation module 418 automatically selects the time interval from a list of optional time intervals based on, for example, the target tissue, the source of the cyclic motion, etc. In another example, the short frame generation module 418 directly calculates the time interval.

[0068] The short frame generation module 418 further includes a frame processing module 506. The frame processing module 506 is configured to generate short PET frames with consistency pairs in the bins for all bins in the data buffer and / or other memory. In this way, each short PET frame will represent a contiguous time window of the PET acquisition. The frame processing module 506 can use known iterative and / or other techniques to generate short PET frames. In one example, the frame processing module 506 is configured to apply attenuation and / or scatter correction and / or other signal processing techniques. In another example, the frame processor 506 does not apply any signal processing techniques.

[0069] In another example, the frame processing module 506 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" (the entire content of which is incorporated herein by reference) to generate short PET frames. In US11,179,128B2, a series of live PET frames are generated during a defined duration while the emission data is being acquired. In a variation, the frame processing module 506 uses the algorithm of US11,179,128B2 to generate a set of short PET frames from the fully acquired PET data after the PET scan rather than during the acquisition.

[0070] Figure 6 Schematically illustrated is an example of a target tissue (TOI) selection module 420. The TOI selection module 420 includes a segmentation module 602 and an algorithm 604, which includes a semi-automatic selection algorithm 606, a manual selection algorithm 608, and / or an automatic selection algorithm 610.

[0071] In one example, to select a target tissue, Figure 4 The PET viewing module 416 displays a rendering of the PET data. Figure 7 Diagrammatically illustrated is an example of a rendering including a maximum intensity projection (MIP) rendering 702. The MIP rendering 702 includes a patient 704 from a head 706 to a thigh 708. In one example, the MIP rendering 702 is displayed in a viewport 710 of a display monitor of the output device 406. Other renderings (e.g., minimum intensity projection (mIP), etc.) are also contemplated herein.

[0072] In the MIP rendering 702, voxels corresponding to projections with higher intensity are darker gray or black, and voxels corresponding to projections with lower intensity are lighter gray or white. Higher intensity corresponds to tissue with greater uptake of radiopharmaceuticals and emission of gamma rays. Since PET scans are acquired over several minutes during cyclic and / or non-cyclic motion, tissues (including those with radiopharmaceutical uptake) will move with the cyclic and / or non-cyclic motion, and thus tissue with radiopharmaceutical uptake will appear blurry in the MIP rendering 702.

[0073] An example of this ambiguity is Figure 8 It is shown in Figure 7 FIG. 8 is a magnified view of a sub-portion 802 of the MIP rendering 702. Figure 8In the diagram, tissue 804 is seen as a stripe between a first end 806 and a second end 808. Generally, first end 806 represents the extent of movement of tissue 804 in a first visible direction, and second end 808 represents the extent of movement of tissue 804 in a second relatively visible direction. Figure 8 Move in a direction not visible in the Figure 8 In the embodiment of the present invention, the movement of tissue 804 is generally up and down relative to subsection 802. In another example, the movement can be left and right, in and out, and / or a combination of these three directions.

[0074] refer to Figure 6 , Figure 7 and Figure 8 , the TOI selection module 420 receives as input a signal from the input device 402 indicating a user selection on the displayed MIP rendering 702. For example, the user may utilize a mouse (touch screen, stylus, and / or other input device) of the input device 402 to hover over a portion of the target tissue and click on the portion. Fig. 9 An example is shown in Figure 8 802, wherein the pointer 902 is hovering over the blurred tissue 804. In one example, the user clicks the mouse to select the tissue 804.

[0075] As described herein, at least two organizations are selected. Fig.10 A second tissue 1002 is shown as a stripe between a first end 1004 and a second end 1006, wherein the first end 1004 represents the extent of movement of the second tissue 1002 in a first direction and the second end 1006 represents the extent of movement of the second tissue 1002 in a second relative direction. Fig.10 Move in a direction not visible in the Fig.10 , the pointer 1008 hovers over the fuzzy tissue 1002, and the user clicks the mouse to select the fuzzy tissue 1002.

[0076] return Figure 6 , the segmentation module 602 segments the selected tissue, e.g. Fig. 9 Organizations in 804 and / or Fig.10 In one example, the segmentation module 602 segments the tissue after each selection, for example, after selecting the tissue 804 ... Fig. 9 Organization 804 in the split after selecting organization 1002 Fig.10 In another example, a plurality of tissues are first selected, and then the segmentation module 602 segments the plurality of tissues continuously and / or in parallel.

[0077] return Figure 6, the segmentation module 602 segments the selected tissue based on one or more of the algorithms 604. Using a non-limiting example semi-automatic selection algorithm 606, after the user selects the target tissue (e.g., 804 and / or 1002), the segmentation module 602 segments the selected target tissue using known and / or other segmentation algorithms. Examples of suitable algorithms include histogram-based, edge detection, clustering, thresholding, gradient, volume growing, artificial intelligence-based, and / or other methods.

[0078] The semi-automatic algorithm 606 then automatically places a bounding shape (also referred to herein as a target volume or VOI) around the selected target tissue. The bounding shape can be circular, elliptical, spherical, square, rectangular, cubic, cylindrical, etc. In one example, the semi-automatic algorithm estimates the central area (e.g., the centroid) of the selected tissue and centers the bounding shape about the center. The semi-automatic algorithm estimates the overall size of the selected tissue and generates a bounding shape that is large enough to cover the movement (e.g., the overall size + margin) and small enough to cause other tissues in the bounding shape to move with approximately the same displacement.

[0079] Fig.11 Examples of several boundary shapes Figure 8 Subsection 802, including for Figure 8 The first boundary shape 1102 of the selected tissue 804, Fig.10 1002, a second boundary shape 1104 for selected tissue 1002, a third boundary shape 1106 for selected tissue 1108, and a fourth boundary shape 1110 for selected tissue 1112. Target tissues 1108 and / or 1112 may be selected as described above with respect to target tissues 804 and / or 1002 and / or otherwise. Again, at least two target tissues are selected, and more than four target tissues may be selected.

[0080] Return to Figure 6 In one example, in addition to displaying the sub-portion 802 having a boundary shape (e.g., Fig.11 ), Figure 4 The PET data viewing module 416 may also display one or more other viewports (e.g., different from the Figure 7 710). Examples of such planes are axial, sagittal, coronal, oblique, irregular, etc. For example, in one example, the PET data viewing module 416 presents four viewports, one with MIP 702 or sub-portions 802, one with axial slices, one with sagittal slices, and one with coronal slices.

[0081] Fig.12The diagram schematically illustrates a system having N viewports, viewport 1 (VP1) 1204, ..., viewport I (VP I )1206, ... and viewport N (VP N ) 1208, where N is a positive integer greater than two. In the illustrated embodiment, viewport 1 and the set of viewports IN are positioned horizontally relative to each other, and viewports I to N are positioned vertically relative to each other. In addition, viewport 1 is shown as being larger than viewports I and N. This illustration is non-limiting, and other configurations are contemplated herein. In addition, the user can toggle the visibility of one or more of the N viewports on and off.

[0082] In one example, a VOI is also presented in each plane. A user can accept, reject, and / or modify the VOI automatically generated and placed in one or more dimensions via an input device in input device 402. In one example, the VOI further includes a mask or sub-VOI within the VOI. In this example, for example, during motion tracking, the VOI can be set larger and the mask or sub-VOI can be moved over the target tissue within the larger VOI. Generally speaking, the target tissue will move from frame to frame in a known direction and in small increments, so the mask or sub-VOI can be automatically moved for a frame based on its position for the previous frame, which will provide a more accurate motion measurement.

[0083] The TOI selection module 420 provides a soft tool for manually selecting target tissue using a non-limiting example manual selection algorithm 608. In one example, the soft tool is provided via a drop-down menu, a pop-up menu, a list box, a directory tree structure, etc. In this case, the user selects a bounding shape (e.g., a rectangle, etc.) from the menu, and the TOI selection module 420 displays the bounding shape, which the user can move around the viewport, change its shape, and fix it to a point in the viewport.

[0084] Similar to the semi-automatic selection algorithm 606 discussed above, the bounding box may be sized and placed Figure 8 MIP rendering 702 and / or Figure 8 802, and / or having one or more views displayed in one or more viewports. Likewise, a user via an input device of the input device 402 can accept, reject, and / or modify the VOI in one or more dimensions. In one instance, the target tissue is selected using the semi-automatic algorithm 606 described above, and another target tissue is selected using a subject manual algorithm 608. In another example, the manual tool is a freehand drawing tool.

[0085] Using a non-limiting example automatic selection algorithm 610, the TOI selection module 420, the algorithm identifies the at least two target tissues and generates a VOI. This may include automatically identifying the target tissue (such as a tumor, lesion, organ, etc.) based on information with the PET data file (such as a header, metadata, DICOM fields, etc.). In one example, the algorithm includes a deep learning (DL) based algorithm that is trained to identify organs and / or abnormalities in normal tissue. In a longitudinal study, the VOI in a previous image of the patient can be used to identify the target tissue in the current image of the patient by mapping the VOI between the images.

[0086] Fig.13 An example of a motion correction with a local block module 422 is schematically illustrated. The example motion correction with a local block module 422 includes a local motion estimation module 1302 and a local motion compensation module 1304. The motion correction with a local block module 422 obtains as input the set of short PET frames generated by the frame processing module 506 and / or other processors of the short frame generation module 418. The set of short PET frames may be obtained (e.g., received and / or retrieved) from the data 414, the repository 154, and / or other devices.

[0087] The position motion estimation module 1302 estimates the motion of the target tissue in each VOI and / or mask / sub-VOI separately and independently. The position motion estimation module 1302 estimates the motion in each different VOI and / or mask / sub-VOI in series or in parallel. In the following discussion, the position motion estimation module 1302 estimates the motion based on short PET frames. Additionally or alternatively, the position motion estimation module 1302 estimates the motion based on a time of flight (TOF) point cloud representation of the data, back projection, centroid calculation from list mode, sinogram data, and / or other information.

[0088] For a selected TOI and a corresponding VOI, the local motion estimation module 1302 tracks the motion of the TOI through short PET frames. In one example, the local motion estimation module 1302 defines a spherical region of constant radius (e.g., 0.5 cm, 1 cm, 2 cm, 5 cm, etc.) centered at the previous position of the target. For the first motion point, the provided target position can be used to center the spherical region. Non-limiting examples of suitable methods for estimating the target position include calculating the centroid, registering the image to a selected reference frame, locating the maximum pixel, etc. In one embodiment, one or more of these methods and / or other methods can be combined with image pre-processing steps such as smoothing, thresholding, etc.

[0089] In general, the centroid is a weighted average of the pixels and can be calculated, for example, by summing the product of the value and position of each pixel and dividing the result by the sum of the values. In one instance, the pixel values ​​in the VOI are first thresholded, for example, by subtracting a portion of the maximum pixel value from all pixel values ​​and then setting all negative pixel values ​​to zero. The centroid provides an estimate of the center of the target in each 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 target tissue. Another non-limiting method includes registering the frames to determine the displacement of the identified target tissue from frame to frame. Other methods are contemplated herein.

[0090] Where a mask / sub-VOI is utilized, the position motion estimation module 1302 masks out all pixels inside the VOI that are outside the mask / sub-VOI. The pixel values ​​in the mask / sub-VOI may first be thresholded, as discussed herein and / or otherwise. Thus, the pixel values ​​at the edge of the mask / sub-VOI are zero or close to zero. For each TOI, the local motion estimation module 1302 determines the motion within a spherical volume of constant radius. In one example, the local motion estimation module 1302 estimates motion with at least one degree of freedom, e.g., at least one of a translation in the x-direction, a rotation in the x-direction, a translation in the y-direction, a rotation in the y-direction, a translation in the z-direction, and a rotation in the z-direction in a Cartesian coordinate system. In another example, the local motion estimation module 1302 estimates motion with two, three, four, five, or six degrees of freedom.

[0091] Fig.14 , Fig.15 and Fig.16 The diagrams show Fig.11 The estimated motion of the TOI 804 in the VOI 1102 in the x-direction, y-direction and z-direction. Fig.14 Initially, a first axis 1402 represents position, e.g., in millimeters or other units. A second axis 1404 represents time, e.g., in seconds or other units. A graph 1406 represents position versus time in the x-direction. Generally, the time points of the second axis 1404 are based on a time window of a short PET frame, e.g., 0.5 s increments. Interpolation and / or other processing may be used to derive position data between time points.

[0092] exist Fig.15 In FIG. 1 , a first axis 1502 represents position, for example, in millimeters or other units. A second axis 1504 represents time, for example, in seconds or other units. A graph 1506 represents position versus time in the y direction. Fig.16, a first axis 1602 represents position, e.g., in millimeters or other units. A second axis 1604 represents time, e.g., in seconds or other units. A graph 1606 represents position versus time in the z direction. Similarly, the time points of the second axes 1504 and 1604 are determined by a time window of a short PET frame, e.g., 0.5 s increments, and interpolation and / or other processing can be used to derive position data between time points.

[0093] according to Fig.14 , Fig.15 and Fig.16 , the least amount of movement of TOI 804 due to periodic and / or non-periodic motion is in the x-direction because the magnitude of change 1408 in graph 1406 is less than the magnitude of change 1508 in graph 1506 and the magnitude of change 1608 in graph 1606. The greatest amount of movement of TOI 804 due to periodic and / or non-periodic motion is in the z-direction because the magnitude of change 1608 in graph 1606 is greater than the magnitude of change 1508 in graph 1506 and the magnitude of change 1408 in graph 1406. Similarly, rotation and / or other movements may also be tracked.

[0094] If it is determined that the VOI for the target tissue is not large enough for the short PET frame and the target tissue in the VOI moves outside its VOI for the short PET frame, the VOI may be adjusted in real time for the short PET frame, for example based on the estimated motion, and increased by a predetermined amount. This determination may be made based on the known location of the edge or perimeter of the VOI and the estimated motion. Since the motion is tracked for each VOI, this determination may be made for each VOI for all short PET frames.

[0095] return Fig.13 , the position motion estimation module 1302 estimates the motion of the target tissue for at least one other VOI (i.e., at least two VOIs in total). As discussed herein, the local motion estimation module 1302 estimates the motion of the TOIS in the VOIS separately and independently, serially or in parallel. That is, the local motion estimation module 1302 estimates the motion of the TOIS in the VOIS based on the local motion estimation module 1302. Fig.11 The events in the VOI 1102 are estimated Fig.11 The target organization in the 804 campaign is based on Fig.11 The events in the VOI 1104 are estimated Fig.11 The target organization 1002 movement etc.

[0096] Fig.17A non-limiting example of a local motion compensation module 1304 is schematically illustrated. The local motion compensation module 1304 includes a block determination module 1702 and a block image generation module 1704. The block determination module 1702 determines blocks of the VOI. In one example, the size of the blocks for the TOI in the VOI is based on the estimated motion. In one example, the size of the blocks is the same as the VOI. In another example, the size of the blocks is smaller than the size of the corresponding VOI, for example, in the case where it is determined that the size of the blocks can be reduced and all motion can still be corrected based on the estimated motion. Similar to the VOI, the blocks can be of different shapes, such as rectangular, spherical, cylindrical, etc.

[0097] The tile image generation module 1704 processes the tiles separately and independently. In one example, only events in the tile are processed to correct the events in the tile for the estimated motion. In one example, the tile image generation module 1704 accounts for activity outside the tile during processing. For list mode processing, this can be achieved by forward projecting through the full field of view (FOV) image. The FOV image can be low fidelity and therefore a low resolution image without motion correction can be utilized. Then, during each image update, the forward projection of each event through the full FOV image can be added to the forward projection reconstructed by patching. Accounting for activity outside the tile ensures quantitative accuracy.

[0098] As discussed herein, motion-corrected blocks may be displayed and / or inserted into Figure 7 MIP rendering 702 and / or Figure 8 In sub-portion 802 of . Fig.18 , Fig.19 , Fig. 20 and Fig.21 They are shown for Fig.11 The target tissues 804, 1002, 1108 and 1112 in the blocks 1802, 1902, 2002 and 2102 are respectively motion corrected. Fig.11 The corresponding target tissues 804, 1002, 1108 and 1112 that are not corrected in FIG. 1 are clearer and include less blur. Thus, blocks 1802, 1902, 2002 and 2102 more accurately capture the activity and distribution in the target tissues.

[0099] Fig. 22 Shows the insertion into Figure 8 Blocks 1802, 1902, 2002, and 2102 in subsection 802. Fig. 22, visible borders 2202, 2204, 2206, and 2208 are provided around blocks 1802, 1902, 2002, and 2102, respectively, to at least let the viewer know which portion of sub-portion 802 is corrected for motion and which portion of the sub-portion is not corrected for motion. Figure 8 Subsection 802 of allows visualization of the target tissue for motion correction in the context of the patient's anatomy.

[0100] In another example, averaging, smoothing, etc. may be used to stitch the blocks into the sub-portion 802 to obtain a more natural appearance. In another example, the block image generation module 1704 motion corrects the full PET data. In this example, the blocks 1802, 1902, 2002, and 2102 are segmented or cropped from the motion corrected full PET data and displayed as described herein, for example, as Figures 18 to 21 As shown separately and / or inserted into Fig. 22 As shown in subsection 802 or Figure 7 , as shown in MIP rendering 702. However, processing only the tiles may require fewer processing resources and / or take less time than processing the entire complete PET data.

[0101] exist Figure 1 In the present invention, the imaging system is a PET / CT hybrid imaging system. Fig.23 Schematically illustrates Figure 1 2300, in which imaging system 102 includes PET imaging subsystem 104 and MR imaging subsystem 2302. MR imaging subsystem 2302 includes main magnet 2304, gradient (x, y, and z) coils 2308, and RF coils 2306. Main magnet 2304 (which may be a superconducting, resistive, permanent, or other type of magnet) generates a substantially uniform, temporally constant main magnetic field B0 in MR examination region 2310. Gradient coils 2308 generate time-varying gradient magnetic fields along the x, y, and z axes of MR examination region 2310.

[0102] The RF coil 2306 includes a transmit portion that generates a radio frequency signal (at the Larmor frequency of the target nuclei (e.g., hydrogen, etc.) that excites the target nuclei in the examination region 2310, 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 2306 are located in separate RF coils 2306. An MR data acquisition system (DAS) 2312 processes the MR signals, and an MR reconstructor 2313 reconstructs the data and generates an MR image.

[0103] The subject support 2314 includes a table 2316 movably coupled to a frame / base 2318. In one example, the table 2316 is slidably coupled to the frame / base 2318 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 2316 along the frame / base 2318 into and out of the examination region 2310 and / or 112. The table 2416 is configured to support an object or subject in the examination region 2310 and / or 112 for loading, scanning, and / or unloading the subject or object.

[0104] The controller 2320 is configured to control components such as the main magnet 2304, the gradient coils 2308, the RF coils 2306, the operation of the detector array 110, the operation of the subject support 2314, etc. The PET examination region 112 and the MR examination region 2310 are arranged along a common longitudinal axis or z-axis 2322. The imaging system 2300 further includes an operator console 2320, which is substantially similar to Figure 1 The operator console 152 of the imaging subsystem 104 and 2302 is not described in detail (except that it is configured for use with the MR imaging subsystem 2302 rather than the CT imaging subsystem 106), and therefore is not described again. In the instance where the imaging subsystems 104 and 2302 are separate imaging systems, each of the subsystems 104 and 106 will have its own controller, subject support, and operator console.

[0105] Fig.24 A non-limiting example of a flow chart of a computer-implemented method is illustrated. 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.

[0106] At 2402, PET data of a target moving tissue is obtained, as described herein and / or otherwise. At 2404, the PET data is divided into a plurality of short time frames based on a predetermined duration, as described herein and / or otherwise. At 2406, the divided PET data is processed to generate a set of short PET frames, as described herein and / or otherwise. At 2408, at least two target tissues are identified in the PET data, as described herein and / or otherwise.

[0107] At 2410, motion of each of the at least two target tissues is separately and independently estimated using the set of short PET frames, as described herein and / or otherwise. At 2412, motion of each of the at least two target tissues is separately and independently corrected based on the estimated motion, as described herein and / or otherwise. As discussed herein, the motion-corrected at least two target tissues may be displayed and / or inserted into a PET data rendering.

[0108] 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.

[0109] 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.

[0110] 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 further 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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: Acquiring positron emission tomography (PET) data including target moving tissue; generating a set of short PET frames from the PET data, wherein each short PET frame in the set of short PET frames is based on a duration; identifying a first target tissue in the set of short PET frames; identifying at least a second target tissue in the set of short PET frames; separately and independently estimating a first motion of the first target tissue and a second motion of the second target tissue based on the short PET frame; as well as The first target tissue is motion corrected for the first motion and the second target tissue is motion corrected for the second motion separately and independently.

2. The computer-implemented method of claim 1 , further comprising: The motion-corrected first target tissue and the second motion-corrected second target tissue are displayed.

3. The computer-implemented method of claim 1 , further comprising: inserting the motion-corrected first target tissue into a rendering of the PET data at a corresponding first location; inserting the motion corrected second target tissue into the rendering of the PET data at a corresponding second location; and The rendering of the PET data is displayed.

4. The computer-implemented method of claim 1 , wherein at least one of the first motion and the second motion comprises at least one of a translation in an x-direction, a rotation about the x-direction, a translation in a y-direction, a rotation about the y-direction, a translation in a z-direction, and a rotation about the z-direction.

5. The computer-implemented method of claim 1 , further comprising: generating a first target volume for the first target tissue, wherein the first target volume is smaller than the entire field of view of the PET data, and estimating the first motion using only the PET data within the first target volume; as well as generating a second target volume for the second target tissue, wherein the second target volume is smaller than the entire field of view of the PET data, and estimating the second motion using only the PET data within the second target volume, The first target volume and the second target volume are different target volumes.

6. The computer-implemented method of claim 5, further comprising: determining a first region within the first target volume for motion correction; as well as A second region within the second target volume is determined for motion correction.

7. The computer-implemented method of claim 6, wherein the first target volume has a first size, the second target volume has a second size, the first block has a third size, and the second block has a fourth size, and the third size is equal to the first size, and the fourth size is equal to the second size.

8. The computer-implemented method of claim 6, wherein the first target volume has a first size, the second target volume has a second size, the first block has a third size, and the second block has a fourth size, and the third size is smaller than the first size, and the fourth size is smaller than the second size.

9. The computer-implemented method of claim 5, further comprising: At least one of a first size, a first position, or both of the first target volume and a second size, a second position, or both of the second target volume is modified for a short PET frame based on the estimated motion during motion estimation.

10. The computer-implemented method of claim 1, wherein at least one of the first motion and the second motion is a rigid motion.

11. A computing system (154), the computing system comprising: a computer-readable storage medium (412) comprising instructions for correcting motion in the data; and A processor (410) configured to execute the instructions, wherein the instructions cause the processor to: Acquiring PET data including target moving tissue; generating a set of short PET frames from the PET data, wherein each short PET frame in the set of short PET frames is based on a duration; identifying a first target tissue in the set of short PET frames; identifying at least a second target tissue in the set of short PET frames; separately and independently estimating a first motion of the first target tissue and a second motion of the second target tissue based on the short PET frame; as well as The first target tissue is motion corrected for the first motion and the second target tissue is motion corrected for the second motion separately and independently.

12. The computing system of claim 11, wherein the instructions cause the processor to: The motion-corrected first target tissue and the second motion-corrected second target tissue are displayed.

13. The computing system of claim 11, wherein the instructions further cause the processor to: inserting the motion-corrected first target tissue into a rendering of the PET data at a corresponding first location; inserting the motion corrected second target tissue into the rendering of the PET data at a corresponding second location; and The rendering of the PET data is displayed.

14. The computing system of claim 11, wherein at least one of the first motion and the second motion comprises at least one of a translation in an x-direction, a rotation about the x-direction, a translation in a y-direction, a rotation about the y-direction, a translation in a z-direction, and a rotation about the z-direction.

15. A computer-readable storage medium encoded with computer-executable instructions which, when executed by a processor, cause the processor to: Acquiring PET data including target moving tissue; generating a set of short PET frames from the PET data, wherein each short PET frame in the set of short PET frames is based on a duration; identifying a first target tissue in the set of short PET frames; identifying at least a second target tissue in the set of short PET frames; separately and independently estimating a first motion of the first target tissue and a second motion of the second target tissue based on the short PET frame; as well as The first target tissue is motion corrected for the first motion and the second target tissue is motion corrected for the second motion separately and independently.

Images