A PET image reconstruction method, system, and storage medium

Abstract

This specification provides a PET image reconstruction method, system, and storage medium. The method may include acquiring raw data and correction coefficients from at least two beds; and iteratively reconstructing the raw data from the at least two beds based on the correction coefficients to obtain a stitched target image of the at least two beds. At least one iteration in the iterative reconstruction includes: updating the initial image of each of the at least two beds; stitching the updated images of each bed to obtain a stitched image; processing the stitched image to obtain a processed image; and splitting the processed image to obtain the initial image of each of the at least two beds for the next iteration.

Description

Technical Field

[0001] This specification relates to the field of image processing technology, and in particular to a positron emission tomography (PET) image reconstruction method, system, and storage medium. Background Technology

[0002] Positron emission tomography (PET) is a radionuclide tracer imaging system. Conventional clinical PET systems employ a barrel-shaped structure, with an axial imaging field of view typically ranging from 15 to 30 cm. Due to this limited axial field of view, a single scan (also known as a single PET scan or bed scan) can only cover a portion of the scanned object (e.g., the human body). Therefore, multiple scans (i.e., multiple beds) are required to complete a full-body scan. In single-bed scans, the PET system's structure results in a triangular distribution of sensitivity along the axial direction, leading to uneven signal-to-noise ratio (SNR) in single-bed PET images, exhibiting a distribution that is low at both ends and high in the middle. To mitigate this issue, multiple-bed scans incorporate a certain overlap area between adjacent beds. By weighted averaging the images from the overlapping areas of adjacent beds, noise in the stitched areas can be reduced, improving the axial SNR of the PET images. Ideally, a 50% overlap area ensures a consistent axial SNR in the PET images. However, setting the overlap area too large means that more beds are needed to cover the entire human body scanning area, which reduces the scanning efficiency of the PET system.

[0003] Therefore, it is desirable to provide a PET image reconstruction method and system that can reduce noise in the stitching area of ​​the reconstructed image, obtain a PET image with a more uniform axial signal-to-noise ratio, thereby reducing the limitation on the length of the stitching area and improving the working efficiency of the PET system. Summary of the Invention

[0004] One aspect of this specification provides a PET image reconstruction method. The method may include acquiring raw data and correction coefficients for at least two beds; and iteratively reconstructing the raw data of the at least two beds based on the correction coefficients to obtain a target image stitched together from the at least two beds. At least one iteration in the iterative reconstruction includes: updating an initial image for each of the at least two beds; stitching the updated images of each bed together to obtain a stitched image; processing the stitched image to obtain a processed image; and splitting the processed image to obtain an initial image for each of the at least two beds in the next iteration.

[0005] In some embodiments, acquiring the raw data of the at least two beds may include: acquiring the scanning range of the scanned object; determining the number of beds and the overlapping area of ​​the scanning bed based on the scanning range; determining the position of the scanning bed in each of the at least two beds based on the number of beds and the overlapping area; acquiring the scanning time of each bed; and, based on the scanning time of each bed, controlling the imaging device to acquire data of the scanned object when the scanning bed moves to the position of each bed, so as to obtain the raw data of the at least two beds.

[0006] In some embodiments, obtaining the biological data of the at least two beds may further include: preprocessing the biological data of the at least two beds to filter out location information and time-of-flight (TOF) information that match the event.

[0007] In some embodiments, obtaining the correction coefficient may include: performing physical correction on the biological data of the at least two beds to obtain the correction coefficient.

[0008] In some embodiments, updating the initial image of each of the at least two beds may include: initializing and reconstructing the initial image of each of the at least two beds or obtaining the initial image of each of the at least two beds obtained in the previous iteration; forward-projecting the initial image of each of the at least two beds; comparing the forward-projected data of each of the at least two beds with the raw data of each of the at least two beds to obtain a deviation value; and back-projecting the deviation value and accumulating it into the initial image of the corresponding bed to update the initial image of each bed.

[0009] In some embodiments, at least two beds may include N beds, where N≥3 and N is a positive integer. Iterative reconstruction of the raw data from at least two beds to obtain a stitched target image may include: iterative reconstruction of the raw data from the first and second beds to obtain a stitched image M1; iterative reconstruction of the raw data from the Ni-th bed and the (N-i+1)-th bed, where 2≤i<N and i is a positive integer, to obtain a stitched image M... N-i Iterative reconstruction was performed on the raw data of bed N-1 and bed N to obtain the stitched image M. N-1 ; and the images M1, ..., M N-i ... M N-1 The images are then stitched together to obtain the target image.

[0010] In some embodiments, physical correction may include scattering correction. The correction coefficient may include scattering correction coefficients. Performing scattering correction on the biodata of the at least two beds includes: acquiring biodata of a previous bed and the current bed; performing iterative correction on the biodata of the previous bed and the current bed to obtain at least the scattering correction coefficient of the previous bed, wherein at least one iteration in the iterative correction includes: acquiring the previous initial activity distribution image of the previous bed and the current initial activity distribution image of the current bed obtained in the previous iteration; and stitching the previous initial activity distribution image and the current initial activity distribution image together. Next, an intermediate activity distribution image is obtained; at least one scattering point in the intermediate activity distribution image is acquired; based on the intermediate activity distribution image and the at least one scattering point, an intermediate scattering distribution estimate is determined; the intermediate scattering distribution estimate is split to obtain candidate scattering distribution estimates for each bed in the previous bed and the current bed; and image reconstruction is performed based on the candidate scattering distribution estimates for each bed in the previous bed and the current bed and the corresponding raw data of the bed, respectively, to update the previous initial activity distribution image and the current initial activity distribution image.

[0011] In some embodiments, the physical correction includes scattering correction, the correction coefficient includes scattering correction coefficients, and performing scattering correction on the raw data of the at least two beds includes: acquiring raw data of a first bed, a second bed, and a third bed consecutively; performing iterative correction on the raw data of the first bed, the second bed, and the third bed to obtain at least the scattering correction coefficient of the second bed, wherein at least one iteration in the iterative correction includes: acquiring a first initial activity distribution image of the first bed, a second initial activity distribution image of the second bed, and a third initial activity distribution image of the third bed obtained in the previous iteration; and converting the first initial activity distribution image and the second initial activity distribution image into a single image. The activity distribution image and the third initial activity distribution image are stitched together to obtain an intermediate activity distribution image; at least one scattering point in the intermediate activity distribution image is obtained; based on the intermediate activity distribution image and the at least one scattering point, an intermediate scattering distribution estimate is determined; the intermediate scattering distribution estimate is split to obtain candidate scattering distribution estimates for each of the first, second, and third beds; and image reconstruction is performed based on the candidate scattering distribution estimates for each of the first, second, and third beds and the corresponding raw data of the beds, updating the first initial activity distribution image, the second initial activity distribution image, and the third initial activity distribution image.

[0012] Another aspect of this specification provides a PET image reconstruction system. The system includes an acquisition module and a reconstruction module. The acquisition module can be used to acquire raw data and correction coefficients from at least two beds. The reconstruction module can be used to iteratively reconstruct the raw data from the at least two beds based on the correction coefficients to obtain a stitched target image of the at least two beds. At least one iteration in the iterative reconstruction may include updating the initial image of each of the at least two beds, stitching the updated images of each bed to obtain a stitched image, processing the stitched image to obtain a processed image, and splitting the processed image to obtain the initial image of each of the at least two beds for the next iteration.

[0013] Another aspect of this specification provides a computer-readable storage medium that stores computer instructions, which, when read by a computer, execute the PET image reconstruction method described above.

[0014] Additional features will be set forth in part in the description which follows, and will become apparent to those skilled in the art upon consulting the following description and the accompanying drawings, or may be learned by the generation or operation of examples. The features of the invention can be realized and obtained by practice or use of various aspects of the methods, tools, and combinations set forth in the following detailed examples. Attached Figure Description

[0015] This specification can be further described with reference to exemplary embodiments. The exemplary embodiments can be described in detail with reference to the accompanying drawings. The embodiments described are not limiting exemplary embodiments, wherein the same reference numerals denote similar structures in several views of the drawings, and wherein:

[0016] Figure 1 These are schematic diagrams illustrating application scenarios of the image reconstruction system according to some embodiments of this specification;

[0017] Figure 2 These are exemplary block diagrams of an image reconstruction system according to some embodiments of this specification;

[0018] Figure 3 This is an exemplary flowchart of a PET image reconstruction method according to some embodiments of this specification;

[0019] Figure 4 This is an exemplary flowchart of a PET image reconstruction method according to some embodiments of this specification;

[0020] Figure 5 This is an exemplary flowchart of a PET image reconstruction method according to some embodiments of this specification;

[0021] Figure 6 This is an exemplary flowchart illustrating a method for determining scattering correction coefficients according to some embodiments of this specification; and

[0022] Figure 7 This is an exemplary flowchart illustrating a method for determining scattering correction coefficients according to some embodiments of this specification. Detailed Implementation

[0023] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0024] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0025] It should be understood that the terms “system,” “device,” “unit,” “component,” “module,” and / or “block” used herein are one method of distinguishing different components, elements, parts, sections, or components at different levels. However, if other words can achieve the same purpose, they may be replaced by other expressions.

[0026] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0027] Figure 1 These are schematic diagrams illustrating application scenarios of exemplary image reconstruction systems according to some embodiments of this specification. For example... Figure 1 As shown, the image reconstruction system 100 may include an imaging device 110, a network 120, a terminal device 130, a processing device 140, and a storage device 150. The components of the image reconstruction system 100 can be connected in various ways. This is just an example. Figure 1As shown, processing device 140 can be connected to imaging device 110 via network 120. Alternatively, processing device 140 can be directly connected to imaging device 110 (as shown by the dashed double-headed arrow connecting processing device 140 and imaging device 110). Alternatively, terminal devices (e.g., 131, 132, 133, etc.) can be directly connected to processing device 140 (as shown by the dashed double-headed arrow connecting terminal device 130 and processing device 140), or they can be connected to processing device 140 via network 120.

[0028] Imaging device 110 can be used to acquire image data relating to a scanned object or a portion thereof. In some embodiments, the scanned object may include a human body, an animal (e.g., a laboratory mouse or other animal), a phantom, or any combination thereof. In some embodiments, the scanned object may include a specific part of the human body, such as the head, chest, abdomen, or any combination thereof. In some embodiments, the scanned object may include a specific organ, such as the heart, thyroid gland, esophagus, trachea, stomach, gallbladder, small intestine, colon, bladder, ureter, uterus, fallopian tubes, etc. In some embodiments, imaging device 110 may include a positron emission tomography (PET) device, a PET-CT device, a PET / MRI device, a single-photon emission computed tomography (SPECT) device, etc. For ease of description, this specification will use a PET device as an example of imaging device 110, which does not limit the scope of this specification.

[0029] Network 120 can facilitate the exchange of information and / or data. In some embodiments, one or more components of the image reconstruction system 100 (e.g., imaging device 110, terminal device 130, processing device 140, storage device 150, etc.) can exchange information and / or data with other components in the image reconstruction system 100 via network 120. For example, processing device 140 can acquire raw data from at least two beds of imaging device 110 via network 120.

[0030] Terminal device 130 allows users to interact with other components in the image reconstruction system 100. For example, a user can acquire a target image from processing device 140 via terminal device 130. As another example, terminal device 130 can also retrieve data / information stored in storage device 150 via network 120. In some embodiments, terminal device 130 may include mobile device 131, tablet computer 132, laptop computer 133, etc., or any combination thereof.

[0031] Processing device 140 can process information and / or data obtained from imaging device 110, terminal device 130, and / or storage device 150. For example, processing device 140 can acquire raw data and correction coefficients from at least two beds to iteratively reconstruct the raw data from at least two beds based on the correction coefficients. Alternatively, processing device 140 can perform physical correction on the raw data from at least two beds to obtain the correction coefficients. In some embodiments, processing device 140 can be a single server or a group of servers. The server group can be centralized or distributed. In some embodiments, processing device 140 can be local or remote. For example, processing device 140 can access information and / or data from imaging device 110, terminal device 130, and / or storage device 150 via network 120. Alternatively, processing device 140 can be directly connected to imaging device 110, terminal device 130, and / or storage device 150 to access information and / or data. In some embodiments, processing device 140 can include one or more processing units (e.g., a single-core processing engine or a multi-core processing engine). By way of example only, processing device 140 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, or any combination thereof. In some embodiments, processing device 140 may be implemented on a cloud platform. For example, the cloud platform may include one or more of private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, etc. In some embodiments, processing device 140 may be part of imaging device 110 or terminal device 130.

[0032] Storage device 150 may store data, instructions, and / or any other information. In some embodiments, storage device 150 may store data obtained from imaging device 110, terminal device 130, and / or processing device 140. For example, storage device 150 may store parameters associated with imaging device 110 (e.g., reconstruction protocols). As another example, storage device 150 may store raw data (or image data) of a scanned object obtained from imaging device 110. In some embodiments, storage device 150 may store data and / or instructions that processing device 140 may execute or use to perform the exemplary methods described herein. In some embodiments, storage device 150 may include one or a combination of mass storage, removable memory, volatile read-write memory, read-only memory (ROM), etc. In some embodiments, storage device 150 may be implemented using the cloud platform described herein.

[0033] In some embodiments, storage device 150 may be connected to network 120 to communicate with one or more components of image reconstruction system 100 (e.g., imaging device 110, processing device 140, terminal device 130, etc.). One or more components of image reconstruction system 100 may read data or instructions from storage device 150 via network 120. In some embodiments, storage device 150 may be part of processing device 140 or may be independent and directly or indirectly connected to processing device 140.

[0034] It should be noted that the above description of the image reconstruction system 100 is for illustrative purposes only and is not intended to limit the scope of this specification. It is understood that those skilled in the art, upon understanding the principles of the system, can make various modifications and changes in form and detail to the application areas implementing the above system without departing from these principles. However, these changes and modifications do not depart from the scope of this specification. For example, the imaging device 110, processing device 140, and terminal device 130 may share a single storage device 150, or they may each have their own storage devices.

[0035] Figure 2 These are exemplary block diagrams of an image reconstruction system according to some embodiments of this specification. In some embodiments, the image reconstruction system 200 may be implemented by a processing device 140. Figure 2 As shown, the image reconstruction system 200 may include an acquisition module 210 and a reconstruction module 220.

[0036] The acquisition module 210 can be used to acquire the raw data and correction coefficients of at least two beds. In some embodiments, before iteratively reconstructing the raw data, the acquisition module 210 can preprocess the raw data of at least two beds. For example, the acquisition module 210 can filter the raw data of at least two beds to filter out location information and time-of-flight (TOF) information that match the event. As another example, the acquisition module 210 can truncate the raw data of at least two beds (e.g., truncate the data from the first few minutes of each scan) to extract the target raw data desired by the user.

[0037] The reconstruction module 220 can be used to iteratively reconstruct the raw data of at least two beds based on correction coefficients to obtain a target image after stitching together the raw data of at least two beds. For example, the reconstruction module 220 can initialize the image of each bed to obtain an initial image of each bed. The reconstruction module 220 can update the initial image of each of the at least two beds and stitch together the updated images of each bed to obtain a stitched image. Further, the reconstruction module 220 can process the stitched image to obtain a processed image. The reconstruction module 220 can split the processed image to obtain the initial image of each of the at least two beds in the next iteration. When the iteration stopping condition is met, the reconstruction module 220 can determine the stitched image in this iteration as the target image. For example, when the number of beds is N, where N≥3 and N is an integer, the reconstruction module 220 can iteratively reconstruct the raw data of the first bed and the second bed to obtain a stitched image M1. The reconstruction module 220 can iteratively reconstruct the raw data of the second and third beds to obtain the stitched image M2. Similarly, the reconstruction module 220 can iteratively reconstruct the raw data of the Ni-th bed and the (N-i+1)-th (2≤i<N, i is an integer) beds to obtain the stitched image M. N-i Furthermore, following this process, the reconstruction module 220 can iteratively reconstruct the raw data of bed N-1 and bed N to obtain the stitched image M. N-1 The reconstruction module 220 can reconstruct the images M1, M2, ..., M... N-i ... M N-1 The images are then stitched together to obtain the target image. For more information on iterative reconstruction, please refer to [link to relevant documentation]. Figure 4 and Figure 5 And its description.

[0038] It should be understood that Figure 2The systems and modules shown can be implemented in various ways. For example, in some embodiments, the systems and modules can be implemented by hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the methods and systems described above can be implemented using computer-executable instructions and / or included in processor control code, for example, on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules of this specification can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software, for example, executed by various types of processors, or by a combination of the aforementioned hardware circuits and software (e.g., firmware).

[0039] It should be noted that the above description of the system and its modules is for convenience only and should not be construed as limiting this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from these principles. For example, in some embodiments, the acquisition module 210 may include two units, such as a raw data acquisition unit and a correction coefficient acquisition unit, to acquire raw data and correction coefficients respectively. As another example, the modules may share a single storage device, or each module may have its own separate storage device. Such variations are all within the scope of this specification.

[0040] Figure 3 This is an exemplary flowchart of a PET image reconstruction method according to some embodiments of this specification. In some embodiments, process 300 can be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to execute hardware simulations), etc., or any combination thereof. In some embodiments, process 300 may be implemented as a set of instructions (e.g., an application program) stored in storage device 150. Processing device 140 and / or Figure 2 The modules within can execute this set of instructions, and when the instructions are executed, the processing device 140 and / or modules can be configured to perform process 300. The operation of the process presented below is intended to illustrate the procedure. In some embodiments, process 300 may be accomplished by one or more additional operations not described and / or by one or more operations not discussed herein. Additionally, as... Figure 3 The order of operations shown and described below is not restrictive.

[0041] In step 310, the processing device 140 can acquire biological data and correction coefficients for at least two beds. In some embodiments, step 310 can be performed by the acquisition module 210 in the system 200.

[0042] In this specification, the raw data for a particular bed refers to the image data (also referred to as projection data or chord graph) of the scanned object (e.g., a patient, phantom, etc.) placed on the scanned object, obtained by using an imaging device (e.g., a PET device) when the scanned bed is located at the position corresponding to that bed, and is corresponding to that bed position. In some embodiments, the raw data for at least two beds may include respiratory gating information, line of response (LOR) position information, LOR energy information, and / or LOR time information, etc.

[0043] In some embodiments, the processing device 140 can acquire raw data and / or correction coefficients from at least two beds from the storage device 150 and / or the imaging device 110. For example, during multi-bed scanning, the imaging device 110 can transfer the raw data acquired from each bed to the storage device 150 for storage. After the imaging device 110 completes the scan, the processing device 140 can retrieve the raw data from at least two beds from the storage device 150. As another example, the imaging device 110 can transmit the raw data acquired from each bed to the processing device 140 in real time for subsequent processing (e.g., image reconstruction, correction coefficient calculation, etc.).

[0044] In some embodiments, when performing multi-bed scanning on a scanned object, the processing device 140 can acquire the scanning range of the scanned object and determine the number of beds and the overlapping area based on the scanning range. For example, a doctor can input the scanning range of the scanned object through the terminal device 130 based on the size of the patient's lesion or the purpose of the scan (e.g., a full physical examination). The processing device 140 can receive this scanning range and, in conjunction with the PET device's own structural parameters (e.g., the detector axial range), determine the number of beds (N, where N is a positive integer greater than 1) and the overlapping area (e.g., the overlap between two adjacent beds is 10%, 20%, 50%, etc., of a single bed). For example, if the axial scanning range of the PET device can be L0 and the axial range of the scanned object can be L1, then the processing device 140 can determine the number of beds (N) to be equal to ([L1 / L0]+1) and the overlapping area to be equal to... In some embodiments, the overlapping area can be a default value of the image reconstruction system 100, or it can be set by the user via the terminal device 130 (e.g., via physical buttons, touch screen, mouse, voice, etc.). The processing device 140 can further determine the number of beds (N) based on the overlapping area and the scanning range. In some embodiments, the user (e.g., a doctor) can directly set the number of beds (N) and / or the overlapping area via the terminal device 130.

[0045] Further, the processing device 140 can determine the position of the scanning bed in each of at least two beds based on the number of beds (N) and the overlapping area (e.g., the coordinates (or bed code values) of the head or foot of the bed corresponding to each bed, the center bed code value, etc.). Further, the processing device 140 can acquire the scanning time for each bed. In some embodiments, the scanning times for each bed can be the same or different. For example, for a whole-body scan of the human body, the scanning time for the bed corresponding to the feet can be shorter than the scanning time for the bed corresponding to the head. In some embodiments, the scanning time for each bed can be a default value of the image reconstruction system 100, or it can be set by the user through the terminal device 130. Further, the processing device 140 can control an imaging device (e.g., a PET device) to scan the object based on the scanning time and position of each bed to obtain raw data for each bed. Specifically, the processing device 140 can issue a scanning bed movement control command to control the scanning bed to move to a first position corresponding to the first bed. Once the scanning bed reaches the first position, the processing device 140 can issue a scanning control command to control the imaging device to scan the object (i.e., data acquisition). After scanning the object for the first time corresponding to the first bed position, the processing device 140 can issue another scanning bed control command to control the scanning bed to move to the second position corresponding to the second bed position and perform scanning of the second bed position. Similarly, the processing device 140 can move the scanning bed to the positions of each bed position according to the bed position order and scan the object on it, thereby obtaining raw data from at least two beds. In some embodiments, after data acquisition is completed, the processing device 140 can save all acquired data (i.e., raw data from each bed position) in a list mode (e.g., in the storage device 150) for easy retrieval and extraction during subsequent use (e.g., image reconstruction, correction coefficient calculation, etc.).

[0046] In some embodiments, the correction coefficient may include attenuation correction coefficient, scattering correction coefficient, random correction coefficient, normalized correction coefficient, etc., or any combination thereof. In some embodiments, a CT scanner, MR scanner, or similar device may be used to scan the object to obtain a CT image or MR image of the object. The processing device 140 may determine the attenuation correction coefficient for each bed position based on the CT image or MR image of the object. In some embodiments, the processing device 140 may obtain the correction coefficient by performing physical correction on the raw data of at least two beds. For example, physical correction may include scattering correction. The processing device 140 may perform scattering correction on the raw data of at least two beds to obtain the scattering correction coefficient corresponding to each bed position. Exemplary scattering correction methods may include single scatter simulation (SSS), Monte Carlo scattering simulation (MCS), etc. More details on determining the scattering correction coefficient can be found in [link to relevant documentation]. Figure 6 and Figure 7 And its description.

[0047] In step 320, the processing device 140 may iteratively reconstruct the raw data of at least two beds based on correction coefficients to obtain a target image after stitching together the data of at least two beds. In some embodiments, step 320 may be performed by the reconstruction module 220 in the system 200.

[0048] In some embodiments, before iteratively reconstructing the raw data, the processing device 140 may preprocess the raw data from at least two beds. In some embodiments, preprocessing may include one or more operations such as filtering, rearranging, downsampling, and truncation. For example, the processing device 140 may filter the raw data from at least two beds to extract location information and time-of-flight (TOF) information matching the event. As another example, the processing device 140 may truncate the raw data from at least two beds (e.g., truncate the data from the first few minutes of each scan) to extract the target raw data desired by the user.

[0049] In some embodiments, the processing device 140 can iteratively reconstruct raw data (or preprocessed raw data (e.g., target raw data)) based on correction coefficients to obtain a target image stitched together from at least two beds. For example, the processing device 140 can acquire an initial image for each bed. The processing device 140 can update the initial image of each of the at least two beds and stitch the updated images of each bed together to obtain a stitched image. Further, the processing device 140 can process the stitched image to obtain a processed image. The processing device 140 can split the processed image to obtain an initial image for each of the at least two beds in the next iteration. When the iteration stopping condition is met, the processing device 140 can determine that the stitched image in this iteration is the target image. For example, when the number of beds N≥3, the processing device 140 can iteratively reconstruct the raw data of the first and second beds to obtain a stitched image M1. The processing device 140 can iteratively reconstruct the raw data of the second and third beds to obtain a stitched image M2. Similarly, the processing device 140 can iteratively reconstruct the raw data of the Ni-th bed and the (N-i+1)-th (2≤i<N, i is an integer) beds to obtain the stitched image M. N-i Furthermore, by analogy, processing device 140 can iteratively reconstruct the raw data of bed N-1 and bed N to obtain the stitched image M. N-1 The processing device 140 can process the images M1, M2, ..., M... N-i ... M N-1 The images are then stitched together to obtain the target image. For more information on iterative reconstruction, please refer to [link to relevant documentation]. Figure 4 and Figure 5 And its description.

[0050] It should be noted that the above description is for convenience only and should not be construed as limiting this specification to the embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, can make various modifications and changes in form and detail to the application areas of the above methods and systems without departing from these principles.

[0051] Figure 4 This is an exemplary flowchart of a PET image reconstruction method according to some embodiments of this specification. In some embodiments, process 400 can be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to execute hardware simulations), etc., or any combination thereof. In some embodiments, process 400 may be implemented as a set of instructions (e.g., an application program) stored in storage device 150. Processing device 140 and / or Figure 2The modules within can execute this set of instructions, and when the instructions are executed, the processing device 140 and / or modules can be configured to perform process 400. The operation of the process presented below is intended to illustrate the procedure. In some embodiments, process 400 may be accomplished by one or more additional operations not described and / or by one or more operations not discussed herein. Additionally, as... Figure 4 The order of operations shown and described below is not limiting. According to some embodiments of this specification, process 400 can be used for offline reconstruction.

[0052] In step 410, the processing device 140 can acquire raw data and correction coefficients for at least two beds. In some embodiments, step 410 can be performed by the acquisition module 210 in the system 200. In some embodiments, the correction coefficients may include attenuation correction coefficients, scattering correction coefficients, random correction coefficients, etc., or any combination thereof.

[0053] In some embodiments, the processing device 140 can acquire raw data and / or correction coefficients for at least two beds from the storage device 150 and / or the imaging device 110. For example, during multi-bed scanning, the imaging device 110 can transfer the raw data acquired for each bed to the storage device 150 for storage. After the imaging device 110 completes the scan, the processing device 140 can retrieve the raw data for at least two beds from the storage device 150. As another example, after the imaging device 110 acquires raw data for all beds, it can transfer the raw data for all beds to the processing device 140. Further description of step 410 can be found in step 310, and will not be repeated here.

[0054] In step 420, the processing device 140 may update the initial image for each of at least two beds. In some embodiments, step 420 may be performed by the reconstruction module 220 in system 200.

[0055] In some embodiments, for the first iteration, the initial image can be the default image of the image reconstruction system 100 or a random initial image. Each bed can correspond to one initial image. The processing device 140 can directly access the initial image of each bed. In some embodiments, for iterations other than the first iteration, the processing device 140 can obtain the initial image of each of at least two beds obtained in the previous iteration.

[0056] The processing device 140 can reconstruct the raw data of at least two beds based on correction coefficients using a reconstruction algorithm to update the initial image of each bed. In some embodiments, the reconstruction algorithm may include iterative reconstruction (IR), simultaneous iterative reconstruction technique (SIRT), neural network model, adaptive statistical iterative reconstruction (ASIR), or any combination thereof. Exemplary iterative reconstruction algorithms may include ordered subset expectation maximization (OSEM) iterative algorithm, maximum likelihood expectation maximization (MLEM), etc.

[0057] Specifically, in some embodiments, the processing device 140 can perform forward projection on the initial image of each of at least two beds. The forward projection operation can convert the image (e.g., the initial image or an updated initial image) into the data domain (e.g., forward projection data). In some embodiments, this forward projection operation can be implemented using methods such as distance-driven methods or Monte Carlo simulation. Further, the processing device 140 can compare the forward projection data of each of the at least two beds with the raw data of each of the at least two beds to obtain a deviation value. The processing device 140 can back-project the deviation value and accumulate it into the initial image of the corresponding bed to update the initial image of each bed. The back-projection operation can convert data in the data domain into data in the image domain. The processing device 140 can use the deviation value to make the reconstructed image (i.e., the updated initial image) of each bed approximate its raw data, thereby making the reconstructed image more accurate.

[0058] In some embodiments, the processing device 140 may update the biological data of at least two beds based on formula (1). Where, f j (n) M represents the value of the pixel labeled j in the reconstructed image during the nth iteration. ij Let a represent the system matrix. i n represents the attenuation correction factor. i P represents the normalized correction coefficient. i r represents the measured projection value of the i-th response line. i s represents the random correction coefficient. i This represents the scattering correction coefficient.

[0059] It should be noted that the initial images of each of the at least two beds can also be updated in other ways, which are not limited to this method.

[0060] In step 430, the processing device 140 can stitch together the updated images of each bed to obtain a stitched image. In some embodiments, step 420 can be performed by the reconstruction module 220 in the system 200.

[0061] In some embodiments, the processing device 140 may perform weighted stitching on the updated initial images of two adjacent beds based on the overlapping area and bed order to obtain a stitched image. For example, the processing device 140 may perform weighted summation on the overlapping areas of the updated initial images of two adjacent beds to stitch the updated initial images of two adjacent beds together to obtain a stitched image (i.e., an image after merging multiple beds). In some embodiments, the processing device 140 may zoom in or out on the updated initial images of two adjacent beds. The processing device 140 may perform weighted summation on the corresponding zoomed-in or zoomed-out images, and then zoom in or out on the stitched image.

[0062] In step 440, the processing device 140 can process the stitched image to obtain a processed image. In some embodiments, step 420 can be performed by the reconstruction module 220 in the system 200.

[0063] In some embodiments, the processing device 140 may perform noise reduction, contrast enhancement, and other operations on the stitched image to obtain a processed image. In some embodiments, step 440 may be omitted. In other words, the processing device 140 may obtain the stitched image and then directly execute subsequent steps without performing post-processing.

[0064] In step 450, the processing device 140 may determine whether the iteration stop condition is met. In some embodiments, step 420 may be performed by the reconstruction module 220 in the system 200.

[0065] In some embodiments, the iteration stopping condition may include whether the number of iterations has reached a preset number, whether the difference between the images obtained in two adjacent iterations (e.g., a stitched image or a processed image) is less than a predetermined threshold, etc.

[0066] In response to the iteration stopping condition being met, processing device 140 may specify the processed image (or stitched image) as the target image (e.g., a complete PET image of the scanned object) in step 470. In response to the iteration stopping condition not being met, processing device 140 may split the processed image (or stitched image) in step 460 to obtain the initial image for each of at least two beds in the next iteration. For example, processing device 140 may directly use a portion of the corresponding overlapping area in the processed image (or stitched image) as a portion of the corresponding overlapping area in the split image. In other words, the portion of the corresponding overlapping area in the split image includes the same image information or data as the portion of the corresponding overlapping area in the processed image (or stitched image). Further, processing device 140 may execute process 400 to return to step 420 to update the initial image for each of at least two beds (i.e., the image obtained in the previous iteration after splitting the processed image (or stitched image)), and execute steps 430 to 440 until the iteration stopping condition is met.

[0067] Figure 5 This is an exemplary flowchart of a PET image reconstruction method according to some embodiments of this specification. In some embodiments, process 500 can be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to execute hardware simulations), etc., or any combination thereof. In some embodiments, process 500 may be implemented as a set of instructions (e.g., an application program) stored in storage device 150. Processing device 140 and / or Figure 2 The modules within can execute this set of instructions, and when the instructions are executed, the processing device 140 and / or modules can be configured to perform process 500. The operation of the process presented below is intended to illustrate the procedure. In some embodiments, process 500 may be accomplished by one or more additional operations not described and / or by one or more operations not discussed in this specification. Additionally, as... Figure 5 The order of operations shown and described below is not limiting. According to some embodiments of this specification, process 500 can be used for online reconstruction.

[0068] In step 510, the processing device 140 can acquire the raw data and correction coefficients of the first bed and the second bed. In some embodiments, step 510 can be performed by the acquisition module 210 in the system 200.

[0069] Imaging device 110 can transmit the raw data of each bed to processing device 140 in real time. In other words, after imaging device 110 completes the scanning of a bed, it transmits the raw data of that bed to processing device 140 for subsequent processing. Alternatively, processing device 140 can acquire the raw data of each bed from imaging device 110 in real time. For example, when imaging device 110 completes the scanning of the first bed, processing device 140 can immediately acquire the raw data of the first bed from imaging device 110 and preprocess it. Furthermore, processing device 140 can also determine the attenuation correction coefficient for the first bed based on the CT image of the scanned object. When imaging device 110 completes the scanning of the second bed, processing device 140 can immediately acquire the raw data of the second bed from imaging device 110 and preprocess it. Furthermore, processing device 140 can also determine the attenuation correction coefficient for the second bed based on the CT image of the scanned object. In some embodiments, the processing device 140 may further perform physical correction on the raw data of the first bed and the second bed to obtain correction coefficients (e.g., scattering correction coefficients) for the first bed and the second bed. The correction coefficients for the second bed obtained at this time are relatively coarse correction coefficients because the influence of the third bed is not considered.

[0070] In step 520, the processing device 140 can iteratively reconstruct the raw data of the first bed and the second bed to obtain the stitched image M1. In some embodiments, step 520 can be performed by the reconstruction module 220 in the system 200.

[0071] The processing device 140 can perform iterative reconstruction of the raw data of the first and second beds based on a similar iterative reconstruction method as in process 400 (e.g., steps 420 to 470), which will not be described in detail here. In some embodiments, after determining the accurate correction coefficient of the second bed in step 530, the processing device 140 can then perform iterative reconstruction of the raw data of the first and second beds based on the correction coefficient of the first bed and the accurate correction coefficient of the second bed to obtain the stitched image M1.

[0072] In step 530, the processing device 140 can acquire the biological data and correction coefficients of the third bed. In some embodiments, step 530 can be performed by the acquisition module 210 in the system 200.

[0073] The processing device 140 can acquire and preprocess the raw data of the third bed from the imaging device 110 in real time. Furthermore, the processing device 140 can determine the attenuation correction coefficient for the third bed based on the CT image of the scanned object. In some embodiments, the processing device 140 can further perform physical correction on the raw data of the first, second, and third beds to obtain an accurate correction coefficient (e.g., a scattering correction coefficient) for the second bed and a coarse correction coefficient for the third bed.

[0074] In step 540, the processing device 140 can iteratively reconstruct the raw data of the second and third beds to obtain the stitched image M2. In some embodiments, step 540 can be performed by the reconstruction module 220 in the system 200. In some embodiments, the processing device 140 can determine the accurate correction coefficient of the third bed in the next step (not shown), and then iteratively reconstruct the raw data of the second and third beds based on the accurate correction coefficients of the second and third beds to obtain the stitched image M2.

[0075] Similarly, in step 550, the processing device 140 can acquire the raw data and correction coefficients for the (N-1)th (N≥3, and an integer) bed. In some embodiments, step 550 can be performed by the acquisition module 210 in the system 200.

[0076] The processing device 140 can acquire and preprocess the raw data of the (N-1)th bed position from the imaging device 110 in real time. Furthermore, the processing device 140 can determine the attenuation correction coefficient for the (N-1)th bed position based on the CT image of the scanned object. In some embodiments, the processing device 140 can further perform physical correction on the raw data of the (N-3)th bed position (not shown), the (N-2)th bed position (not shown), and the (N-1)th bed position to obtain the accurate correction coefficient for the (N-2)th bed position and the coarse correction coefficient for the (N-1)th bed position.

[0077] In step 560, the processing device 140 can iteratively reconstruct the raw data of the (N-2)th bed and the (N-1)th bed to obtain the stitched image M. N-2 In some embodiments, step 560 may be performed by the reconstruction module 220 in system 200. In some embodiments, after determining the accurate correction coefficient of bed N-1 in step 570, the processing device 140 may iteratively reconstruct the raw data of bed N-2 and bed N-1 based on the accurate correction coefficients of bed N-2 and bed N-1 to obtain the stitched image M. N-2 .

[0078] In step 570, the processing device 140 may acquire raw data and a correction coefficient for the Nth bed position. In some embodiments, step 570 may be performed by an acquisition module 210 in the system 200.

[0079] The processing device 140 may acquire raw data of the Nth bed position from the imaging device 110 in real time and preprocess the raw data. In addition, the processing device 140 may also determine an attenuation correction coefficient for the Nth bed position based on a CT image of a scanned object. In some embodiments, the processing device 140 may further perform physical correction on the raw data of the (N-2)th bed position (not shown), the (N-1)th bed position, and the Nth bed position, to obtain an accurate correction coefficient for the (N-1)th bed position and a correction coefficient for the Nth bed position.

[0080] In step 580, the processing device 140 may perform iterative reconstruction on the raw data of the (N-1)th bed position and the Nth bed position to obtain a stitched image M N-1 . In some embodiments, step 580 may be performed by a reconstruction module 220 in the system 200.

[0081] In step 590, the processing device 140 may combine images M1, M2, ..., M N-2 , M N-1 for stitching to obtain a target image (that is, a complete PET image of the scanned object). In some embodiments, step 590 may be performed by a reconstruction module 220 in the system 200. In some embodiments, after obtaining partially stitched images (for example, M1, M2, ..., M N-x where 1≤x<N and x is an integer), the processing device 140 may stitch the partially stitched images together.

[0082] It should be noted that when the processing device 140 performs iterative reconstruction on the acquired raw data, the imaging device 110 may continue scanning the remaining bed positions. In other words, scanning by the imaging device 110 and image reconstruction by the processing device 140 are performed simultaneously. By performing image reconstruction and scanning simultaneously, it is convenient to quickly determine whether scanning is successfully performed, whether the system status is normal, and whether the quality of the reconstructed image meets requirements. If abnormal image quality is found, troubleshooting can be performed in time, which avoids the situation where a scanned object (e.g., a patient) is injected with a tracer but cannot complete the examination. In addition, in some embodiments, the above-mentioned reconstruction steps (e.g., step 520, step 540, step 560, step 580) may be performed in parallel. For example, the above-mentioned reconstruction steps may be performed by different graphics processing units (GPUs).

[0083] Figure 6This is an exemplary flowchart of a scattering correction coefficient determination method according to some embodiments of this specification. In some embodiments, process 600 can be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to perform hardware simulation), etc., or any combination thereof. In some embodiments, process 600 may be implemented as a set of instructions (e.g., an application program) stored in storage device 150. Processing device 140 and / or Figure 2 The modules within can execute this set of instructions, and when the instructions are executed, the processing device 140 and / or modules can be configured to perform process 600. The operation of the process presented below is intended to illustrate the procedure. In some embodiments, process 600 may be accomplished by one or more additional operations not described and / or by one or more operations not discussed in this specification. Additionally, as... Figure 6 The order of operations shown and described below is not restrictive.

[0084] In step 610, the processing device 140 can acquire the biological data of the previous bed and the current bed. In some embodiments, step 610 can be performed by the acquisition module 210 in the system 200.

[0085] In some embodiments, the previous bed and the current bed can be any two adjacent (or consecutive) beds.

[0086] In step 620, the processing device 140 can reconstruct the raw data of the previous bed and the current bed respectively to obtain the previous initial activity distribution image of the previous bed and the current initial activity distribution image of the current bed. In some embodiments, step 620 can be performed by the reconstruction module 220 in the system 200.

[0087] In some embodiments, PET images can reflect the activity distribution of radiopharmaceuticals; therefore, activity distribution images can also be PET images. Processing device 140 can utilize the reconstruction algorithm described in step 420 to reconstruct the raw data of the previous and current beds to obtain the previous initial activity distribution image of the previous bed and the current initial activity distribution image of the current bed.

[0088] In step 630, the processing device 140 can stitch together the previous initial activity distribution image and the current initial activity distribution image to obtain an intermediate activity distribution image. In some embodiments, step 630 can be performed by the reconstruction module 220 in the system 200.

[0089] The processing device 140 can perform a weighted summation on the overlapping area of ​​the previous initial activity distribution image and the current initial activity distribution image to stitch the previous initial activity distribution image and the current initial activity distribution image together to obtain an intermediate activity distribution image.

[0090] In step 640, the processing device 140 may acquire at least one scattering point in the intermediate activity distribution image. In some embodiments, step 640 may be performed by the acquisition module 210 in the system 200.

[0091] The scattering point can be any point (or pixel) in the intermediate activity distribution image. In some embodiments, the scattering point can be set according to the default value of the image reconstruction system 100, or it can be set by a user (e.g., a doctor or engineer) through the terminal device 130. For example, the scattering point can be located in a region with an activity distribution in the intermediate activity distribution image. In some embodiments, the processing device 140 can obtain an attenuation map based on CT images, MR images, etc., of the scanned object. The processing device 140 can obtain spatial distribution information of the scattering point based on the attenuation in the attenuation map, thereby determining the location of the scattering point. For example, the processing device 140 can randomly sample and determine at least one scattering point based on the spatial distribution information of the scattering point.

[0092] In step 650, the processing device 140 may determine an intermediate scattering distribution estimate based on the intermediate activity distribution image and at least one scattering point. In some embodiments, step 650 may be performed by the reconstruction module 220 in system 200.

[0093] In some embodiments, a scattering correction coefficient for each response line can be obtained from the scattering distribution estimate. In some embodiments, the scattering distribution estimate may include image domain data or data domain data (e.g., a chordogram). In some embodiments, the processing device 140 may simulate the photon scattering process (e.g., using the Monte Carlo method) to determine the intermediate scattering distribution estimate.

[0094] In step 660, the processing device 140 can split the intermediate scattering distribution estimate to obtain candidate scattering distribution estimates for each bed in the previous and current beds. In some embodiments, step 660 can be performed by the acquisition module 210 in system 200.

[0095] The processing device 140 can directly use a portion of the corresponding overlapping region in the intermediate scattering distribution estimate as a portion of the corresponding overlapping region in the separated image. In other words, the portions of the corresponding overlapping regions in the candidate scattering distribution estimates for each bed in the previous and current beds include the same image information or data.

[0096] In step 670, the processing device 140 may determine whether the iteration stop condition is met. In some embodiments, step 670 may be performed by the reconstruction module 220 in the system 200.

[0097] In some embodiments, the iteration stopping condition may include whether the number of iterations has reached a preset number, whether the difference between the images (e.g., each initial activity distribution image or intermediate activity distribution image) and / or the scattering distribution estimates (e.g., the candidate scattering distribution estimates of the previous bed) obtained in two adjacent iterations is less than a predetermined threshold, etc.

[0098] In some embodiments, in response to the iteration stopping condition being met and the current bed not being the last bed, the processing device 140 may specify the candidate scattering distribution estimate of the previous bed as the scattering correction coefficient of the previous bed in step 690. In some embodiments, in response to the iteration stopping condition being met and the current bed being the last bed, the processing device 140 may specify the candidate scattering distribution estimate of the previous bed as the scattering correction coefficient of the previous bed, and specify the candidate scattering distribution estimate of the current bed as the scattering correction coefficient of the current bed in step 690. The processing device 140 may store the scattering correction coefficient of the previous bed and / or the scattering correction coefficient of the current bed in the storage device 150 for retrieval. In this specification, for the previous bed, since the influence of the scattering of its next bed (i.e., the current bed) on the previous bed is considered in the calculation, a more accurate scattering correction coefficient for the previous bed can be obtained. For the current bed, since the calculation does not consider the potential impact of scattering from the next bed on the current bed, a more accurate scattering correction coefficient for the current bed can be obtained by determining whether to output the scattering correction coefficient based on whether the current bed is the last bed.

[0099] In response to the failure to meet the iteration stopping condition, the processing device 140 may, in step 680, perform image reconstruction based on the candidate scattering distribution estimates for each bed in the previous and current beds, and the corresponding raw data, respectively, to update the previous initial activity distribution image and the current initial activity distribution image. In other words, the processing device 140 may use the candidate scattering distribution estimates for each bed in the previous and current beds, and the corresponding raw data, to perform image reconstruction, resulting in an initial activity distribution image that has been scatter-corrected. Further, the processing device 140 may execute process 600 to return to step 630 to stitch the updated previous initial activity distribution image and the updated current initial activity distribution image together to obtain an updated intermediate activity distribution image, and execute steps 640 to 660 until the iteration stopping condition is met.

[0100] Figure 7This is an exemplary flowchart of a scattering correction coefficient determination method according to some embodiments of this specification. In some embodiments, process 700 can be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to perform hardware simulation), etc., or any combination thereof. In some embodiments, process 700 may be implemented as a set of instructions (e.g., an application program) stored in storage device 150. Processing device 140 and / or Figure 2 The modules within can execute this set of instructions, and when the instructions are executed, the processing device 140 and / or modules can be configured to perform process 700. The operation of the process presented below is intended to illustrate the procedure. In some embodiments, process 700 may be accomplished by one or more additional operations not described and / or by one or more operations not discussed in this specification. Additionally, as... Figure 7 The order of operations shown and described below is not restrictive.

[0101] In step 710, the processing device 140 can acquire raw data of the first, second, and third consecutive beds. In some embodiments, step 710 can be performed by the acquisition module 210 in the system 200.

[0102] In step 720, the processing device 140 may acquire the first initial activity distribution image of the first bed, the second initial activity distribution image of the second bed, and the third initial activity distribution image of the third bed obtained in the previous iteration. In some embodiments, step 720 may be performed by the reconstruction module 220 in the system 200.

[0103] In step 730, the processing device 140 can stitch together the first initial activity distribution image, the second initial activity distribution image, and the third initial activity distribution image to obtain an intermediate activity distribution image. In some embodiments, step 730 can be performed by the reconstruction module 220 in the system 200.

[0104] In step 740, the processing device 140 may acquire at least one scattering point in the intermediate activity distribution image. In some embodiments, step 740 may be performed by the acquisition module 210 in system 200.

[0105] In step 750, the processing device 140 may determine an intermediate scattering distribution estimate based on the intermediate activity distribution image and at least one scattering point. In some embodiments, step 750 may be performed by the reconstruction module 220 in system 200.

[0106] In step 760, the processing device 140 may split the intermediate scattering distribution estimate to obtain candidate scattering distribution estimates for each of the first, second, and third beds. In some embodiments, step 770 may be performed by the reconstruction module 220 in system 200.

[0107] In step 770, the processing device 140 may determine whether the iteration stop condition is met. In some embodiments, step 770 may be performed by the reconstruction module 220 in the system 200.

[0108] In some embodiments, the iteration stopping condition may include whether the number of iterations has reached a preset number, whether the difference between the images (e.g., each initial activity distribution image or intermediate activity distribution image) and / or the scattering distribution estimate (e.g., the candidate scattering distribution estimate for the second bed) obtained in two adjacent iterations is less than a predetermined threshold, etc.

[0109] In some embodiments, in response to the iteration stopping condition being met and the third bed not being the last bed, the processing device 140 may specify the candidate scattering distribution estimate of the second bed as the scattering correction coefficient of the second bed in step 690. In some embodiments, in response to the iteration stopping condition being met and the third bed being the last bed, the processing device 140 may specify the candidate scattering distribution estimate of the second bed as the scattering correction coefficient of the second bed and specify the candidate scattering distribution estimate of the third bed as the scattering correction coefficient of the third bed in step 790. The processing device 140 may store the scattering correction coefficient of the second bed and / or the scattering correction coefficient of the third bed in the storage device 150 for retrieval. In this specification, for the second bed, since the influence of the scattering from its preceding bed (i.e., the first bed) and the following bed (i.e., the third bed) on the second bed is considered simultaneously in the calculation, a more accurate scattering correction coefficient for the second bed can be obtained.

[0110] In response to the failure to meet the iteration stopping condition, the processing device 140 may, in step 780, perform image reconstruction based on the candidate scattering distribution estimates for each of the first, second, and third beds and the corresponding raw data, respectively, and update the first initial activity distribution image, the second initial activity distribution image, and the third initial activity distribution image. In other words, the processing device 140 may use the candidate scattering distribution estimates for each of the first, second, and third beds and the corresponding raw data to perform image reconstruction, and the resulting initial activity distribution image is the scattering-corrected image. Further, the processing device 140 may execute process 700 to return to step 730 to stitch the updated first initial activity distribution image, the updated second initial activity distribution image, and the updated third initial activity distribution image together to obtain an updated intermediate activity distribution image, and execute steps 740 to 760 until the iteration stopping condition is met.

[0111] The beneficial effects that the embodiments of this specification may bring include, but are not limited to: (1) This specification proposes a PET image reconstruction method. In the multi-bed scanning mode, the parallel multi-bed reconstruction sequence is used to make it possible to take into account the influence of the scanning data of the front and rear beds on the current bed during the iterative reconstruction process, reduce the noise in the splicing area of ​​the reconstructed image, and obtain a PET image with a more uniform axial signal-to-noise ratio, thereby reducing the limitation on the length of the splicing area and improving the working efficiency of the system; (2) By reconstructing the raw data obtained from the scanning in pairs in real time, scanning and image reconstruction can be carried out simultaneously, which makes it easier to quickly find problems that occur during the scanning process; (3) In the process of scattering correction calculation, the influence of the scanning data of the front and rear beds on the current bed is taken into account, which improves the accuracy of scattering correction; (4) The PET reconstruction method proposed in this specification does not require additional hardware upgrades and will not introduce new errors. It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced can be any one or a combination of the above, or any other possible beneficial effects.

[0112] The basic concepts have been described above. It is clear that the above disclosure is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to this specification by those skilled in the art. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

Claims

1. A PET image reconstruction method, comprising: Obtain biological data and correction coefficients for at least two beds; as well as Based on the correction coefficients, iterative reconstruction is performed on the raw data of the at least two beds to obtain a target image after stitching together the data of the at least two beds, wherein at least one iteration in the iterative reconstruction includes: The initial image of each of the at least two beds is updated. The updated images of each bed are then stitched together to obtain a stitched image. The stitched image is processed to obtain the processed image, and The processed image is split to obtain the initial image of each of the at least two beds in the next iteration; Obtaining the correction coefficients includes: Physical correction is performed on the biological data of the at least two beds to obtain the correction coefficients; the physical correction includes scattering correction, and the correction coefficients include scattering correction coefficients; Performing the scattering correction on the biological data of the at least two beds includes: Obtain the patient data of the previous and current patients; The raw data of the previous bed and the current bed are iteratively corrected to obtain at least the scattering correction coefficient of the previous bed, wherein at least one iteration in the iterative correction includes: Obtain the previous initial activity distribution image of the previous bed obtained in the previous iteration and the current initial activity distribution image of the current bed; The previous initial activity distribution image and the current initial activity distribution image are stitched together to obtain an intermediate activity distribution image; The intermediate scattering distribution estimate is determined based at least on the intermediate activity distribution image; The intermediate scattering distribution estimate is split to obtain candidate scattering distribution estimates for each bed in the previous and current beds; and Image reconstruction is performed based on the candidate scattering distribution estimates of each bed in the previous bed and the current bed, and the corresponding raw data of the bed, respectively, to update the previous initial activity distribution image and the current initial activity distribution image; or Performing the scattering correction on the biological data of the at least two beds includes: Obtain the biological data of the first, second, and third consecutive beds; The raw data of the first bed, the second bed, and the third bed are iteratively corrected to obtain at least the scattering correction coefficient of the second bed, wherein at least one iteration of the iterative correction includes: Obtain the first initial activity distribution image of the first bed, the second initial activity distribution image of the second bed, and the third initial activity distribution image of the third bed obtained in the previous iteration; The first initial activity distribution image, the second initial activity distribution image, and the third initial activity distribution image are stitched together to obtain an intermediate activity distribution image; The intermediate scattering distribution estimate is determined based at least on the intermediate activity distribution image; The intermediate scattering distribution estimate is decomposed to obtain candidate scattering distribution estimates for each of the first, second, and third beds; and Image reconstruction is performed based on the candidate scattering distribution estimation of each of the first, second, and third beds and the corresponding raw data of the beds, and the first initial activity distribution image, the second initial activity distribution image, and the third initial activity distribution image are updated.

2. The method according to claim 1, characterized in that, Obtaining the biological data of the at least two beds includes: Get the scan range of the object being scanned; Based on the scanning range, determine the number of scanning beds and the overlapping area; Based on the number of beds and the overlapping area, the position of the scanning bed in each of the at least two beds is determined; Obtain the scan time for each bed; and Based on the scanning time for each bed, as the scanning bed moves to the position of each bed, the imaging device is controlled to acquire data of the scanned object to obtain the raw data of the at least two beds.

3. The method according to claim 2, characterized in that, Obtaining the biological data of the at least two beds further includes: The biological data from the at least two beds are preprocessed to filter out location information and time-of-flight information that match the event.

4. The method according to claim 1, characterized in that, Updating the initial image for each of the at least two beds includes: Initialize and reconstruct the initial image of each of the at least two beds or obtain the initial image of each of the at least two beds obtained in the previous iteration; Forward projection is performed on the initial image of each of the at least two beds; The forward projection data of each of the at least two beds is compared with the raw data of each of the at least two beds to obtain the deviation value; and The deviation value is back-projected and accumulated into the initial image of the corresponding bed to update the initial image of each bed.

5. The method according to claim 1, characterized in that, The at least two beds include N beds, where N≥3, and N is a positive integer. Iterative reconstruction is performed on the raw data of the at least two beds to obtain the target image obtained by stitching together the data of the at least two beds, including: Iterative reconstruction is performed on the raw data of every pair of adjacent beds in a pool of N beds to obtain N-1 stitched images; and The target image is obtained by sequentially stitching together the N-1 stitched images.

6. A PET image reconstruction system, characterized in that, include: The acquisition module is used to acquire biological data and correction coefficients for at least two beds; as well as A reconstruction module is used to iteratively reconstruct the raw data of the at least two beds based on the correction coefficients to obtain a target image after stitching together the at least two beds, wherein at least one iteration in the iterative reconstruction includes: The initial image of each of the at least two beds is updated. The updated images of each bed are then stitched together to obtain a stitched image. The stitched image is processed to obtain the processed image, and The processed image is split to obtain the initial image of each of the at least two beds in the next iteration; Obtaining the correction coefficients includes: Physical correction is performed on the biological data of the at least two beds to obtain the correction coefficients; the physical correction includes scattering correction, and the correction coefficients include scattering correction coefficients; Performing the scattering correction on the biological data of the at least two beds includes: Obtain the patient data of the previous and current patients; The raw data of the previous bed and the current bed are iteratively corrected to obtain at least the scattering correction coefficient of the previous bed, wherein at least one iteration in the iterative correction includes: Obtain the previous initial activity distribution image of the previous bed obtained in the previous iteration and the current initial activity distribution image of the current bed; The previous initial activity distribution image and the current initial activity distribution image are stitched together to obtain an intermediate activity distribution image; The intermediate scattering distribution estimate is determined based at least on the intermediate activity distribution image; The intermediate scattering distribution estimate is split to obtain candidate scattering distribution estimates for each bed in the previous and current beds; and Image reconstruction is performed based on the candidate scattering distribution estimates of each bed in the previous bed and the current bed, and the corresponding raw data of the bed, respectively, to update the previous initial activity distribution image and the current initial activity distribution image; or Performing the scattering correction on the biological data of the at least two beds includes: Obtain the biological data of the first, second, and third consecutive beds; The raw data of the first bed, the second bed, and the third bed are iteratively corrected to obtain at least the scattering correction coefficient of the second bed, wherein at least one iteration of the iterative correction includes: Obtain the first initial activity distribution image of the first bed, the second initial activity distribution image of the second bed, and the third initial activity distribution image of the third bed obtained in the previous iteration; The first initial activity distribution image, the second initial activity distribution image, and the third initial activity distribution image are stitched together to obtain an intermediate activity distribution image; The intermediate scattering distribution estimate is determined based at least on the intermediate activity distribution image; The intermediate scattering distribution estimate is decomposed to obtain candidate scattering distribution estimates for each of the first, second, and third beds; and Image reconstruction is performed based on the candidate scattering distribution estimation of each of the first, second, and third beds and the corresponding raw data of the beds, and the first initial activity distribution image, the second initial activity distribution image, and the third initial activity distribution image are updated.

7. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the method as described in any one of claims 1 to 5.

Images