A medical image processing method, system, device and storage medium

Abstract

Embodiments of the present specification provide a medical image processing method, system, device and storage medium. The method comprises: acquiring at least two groups of projection data of a region of interest, the at least two groups of projection data respectively corresponding to at least two motion phase phases, each group of projection data comprising at least two projection positions; acquiring a projection value corresponding to each projection position in each group of projection data; determining a target projection value corresponding to each projection position based on at least two projection values corresponding to each projection position; acquiring target projection data according to the target projection value corresponding to each projection position; reconstructing a target image of the region of interest based on the target projection data; and performing attenuation correction on a PET image based on the target image.

Description

Technical Field

[0001] This specification relates to the field of imaging, and in particular to a medical image processing method, system, and storage medium. Background Technology

[0002] Cine computed tomography (CCT) technology can acquire scan data sequences of an object over multiple motion cycles, reconstruct image sequences based on these scan data sequences, and use this reconstructed image sequences to perform attenuation correction on positron emission tomography (PET) images, thereby obtaining high-quality target images. However, reconstructing image sequences from scan data sequences is time-consuming and resource-intensive.

[0003] Therefore, it is necessary to provide a medical image processing scheme to improve the efficiency of acquiring high-quality target images based on movie CT sequences. Summary of the Invention

[0004] This specification provides one or more embodiments of a medical image processing method, characterized by comprising: acquiring at least two sets of projection data for a region of interest, wherein the at least two sets of projection data correspond to at least two motion phases respectively, and each set of projection data includes at least two projection positions; acquiring the projection value corresponding to each projection position in each set of projection data; determining a target projection value corresponding to each projection position based on the at least two projection values ​​corresponding to each projection position; acquiring target projection data according to the target projection value corresponding to each projection position; reconstructing a target image of the region of interest based on the target projection data; and performing attenuation correction on a PET image based on the target image.

[0005] This specification provides one or more embodiments of a medical image processing system, characterized in that it includes: a projection data acquisition module, used to acquire at least two sets of projection data for a region of interest, wherein the at least two sets of projection data correspond to at least two motion phases, and each set of projection data includes at least two projection positions; a projection value acquisition module, used to acquire the projection value corresponding to each projection position in each set of projection data; a target projection value determination module, used to determine the target projection value corresponding to each projection position based on the at least two projection values ​​corresponding to each projection position; a target projection data determination module, used to acquire target projection data according to the target projection value corresponding to each projection position; a reconstruction module, used to reconstruct a target image of the region of interest based on the target projection data; and an image correction module, used to perform attenuation correction on the PET image based on the target image.

[0006] One or more embodiments of this description improve a medical image processing apparatus, the apparatus including at least one storage medium storing computer instructions; and at least one processor executing the computer instructions to implement a medical image processing method.

[0007] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes a medical image processing method. Attached Figure Description

[0008] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0009] Figure 1 These are schematic diagrams illustrating application scenarios of a medical image processing system according to some embodiments of this specification;

[0010] Figure 2 These are exemplary block diagrams of medical image processing according to some embodiments of this specification;

[0011] Figure 3 This is an exemplary flowchart of medical image processing according to some embodiments of this specification;

[0012] Figure 4 This is an exemplary flowchart illustrating the determination of target projection values ​​based on target range according to some embodiments of this specification;

[0013] Figure 5 This is a schematic diagram illustrating the acquisition of projection data by a scanning device according to some embodiments of this specification;

[0014] Figure 6 This is a schematic diagram of a detector according to some embodiments of this specification;

[0015] Figure 7 This is a schematic diagram of a set of projection data according to some embodiments of this specification;

[0016] Figure 8 This is a schematic diagram illustrating the determination of target projection values ​​according to some embodiments of this specification;

[0017] Figure 9A and Figure 9B This is a schematic diagram illustrating the determination of a target range according to some embodiments of this specification.

[0018] Figure 10 This is a schematic diagram illustrating the mapping relationship between the object spatial domain and the projection domain according to some embodiments of this specification;

[0019] Figure 11 This is a schematic diagram illustrating the conversion of acquired signals into the projection domain according to some embodiments of this specification. Detailed Implementation

[0020] 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.

[0021] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0022] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0023] 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.

[0024] Figure 1 This is a schematic diagram illustrating the application scenarios of a medical image processing system according to some embodiments of this specification.

[0025] In some embodiments, such as Figure 1 As shown, the medical image processing system 100 may include an imaging device 110, a processing device 120, a terminal device 130, a network 140, and a storage device 150. The components of the medical image processing system 100 may be connected in one or more ways. This is merely an example. Figure 1As shown, imaging device 110 can be connected to processing device 120 via network 140. Alternatively, imaging device 110 can be directly connected to processing device 120 (as indicated by the dashed double-headed arrow connecting imaging device 110 and processing device 120). As a further example, storage device 150 can be connected to processing device 120 directly or via network 140. As a further example, terminal device 130 can be directly (as indicated by the dashed double-headed arrow connecting terminal device 130 and processing device 120) and / or via network 140 to processing device 120.

[0026] Imaging device 110 can acquire imaging data of the scanned object. In some embodiments, the scanned object may include, but is not limited to, the human body, organs, organisms, damaged sites, tumors, objects, phantoms, etc. In some embodiments, imaging device 110 may include a computed tomography (CT) device, a cone-beam computed tomography (CBCT) device, a multi-slice computed tomography (MSCT) device, or any combination thereof.

[0027] In some embodiments, the imaging device 110 may include a radiation source and a detector. In some embodiments, the radiation source may provide a radiation beam (e.g., X-rays). In some embodiments, the detector may receive the radiation beam passing through the object being scanned and convert it into a corresponding signal. In some embodiments, the detector may consist of multiple detection elements.

[0028] In some embodiments, the detector may be a single-row detector, consisting of multiple detection elements arranged in a row. For example, the detector may contain m columns × 1 row of detection elements. In some embodiments, the detector may be a multi-row detector. Figure 6 This is a schematic diagram of a detector according to some embodiments of this specification. For example... Figure 6 As shown, the detector may comprise m columns × n rows of detection elements, for example, 3200 (100 columns × 32 rows). In some embodiments, each detection element may receive a radiation beam passing through a location on the scanned object. In some embodiments, the detector may include, but is not limited to, a gas detector, a scintillator detector, a semiconductor detector, etc.

[0029] Processing device 120 can process data and / or information acquired from imaging device 110, terminal device 130, and / or storage device 150. For example, processing device 120 can acquire projection data from imaging device 110 and determine target projection data based on the projection data. Alternatively, processing device 120 can reconstruct a target image based on the target projection data. In some embodiments, processing device 120 may include a central processing unit (CPU), digital signal processor (DSP), system-on-a-chip (SoC), microcontroller unit (MCU), and / or any combination thereof. In some embodiments, processing device 120 may include a computer, user console, a single server, or a group of servers. The server group may be centralized or distributed. In some embodiments, processing device 120 may be local or remote. For example, processing device 120 may access information and / or data stored in imaging device 110, terminal device 130, and / or storage device 150 via network 140. Alternatively, processing device 120 may directly connect to imaging device 110, terminal device 130, and / or storage device 150 to access stored information and / or data. In some embodiments, the processing device 120 may be implemented on a cloud platform. By way of example only, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud, multi-cloud, etc., or any combination thereof. In some embodiments, the processing device 120 or a portion thereof may be integrated into the imaging device 110.

[0030] Terminal device 130 can display an image to a user, such as a target image. Terminal device 130 may include mobile device 131, tablet computer 132, laptop computer 133, etc., or any combination thereof. In some embodiments, terminal device 130 may be part of processing device 120.

[0031] Network 140 may include any suitable network that facilitates the exchange of information and / or data between the medical image processing system 100 and the medical image processing system 100. In some embodiments, one or more components of the medical image processing system 100 (e.g., imaging device 110, processing device 120, terminal device 130, storage device 150) may communicate information and / or data with one or more other components of the medical image processing system 100 via network 140. In some embodiments, network 140 may be and / or include public networks, private networks, wide area networks (WANs), wired networks, wireless networks, cellular networks, frame relay networks, virtual private networks, satellite networks, telephone networks, routers, hubs, switches, and any combination thereof. In some embodiments, network 140 may include one or more network access points. For example, network 140 may include wired and / or wireless network access points such as base stations and / or internet switching points, through which one or more components of the medical image processing system 100 may connect to network 140 to exchange data and / or information.

[0032] Storage device 150 can store data, instructions, and / or any other information. In some embodiments, storage device 150 can store data acquired from imaging device 110, terminal device 130, and / or processing device 120. In some embodiments, storage device 150 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and any combination thereof. In some embodiments, storage device 150 may be implemented on a cloud platform. In some embodiments, storage device 150 may be connected to network 140 to communicate with one or more other components of medical image processing system 100 (e.g., imaging device 110, processing device 120, terminal device 130). One or more components of medical image processing system 100 may access the data or instructions stored in storage device 150 via network 140. In some embodiments, storage device 150 may be directly connected to or communicate with one or more other components of medical image processing system 100 (e.g., imaging device 110, processing device 120, storage device 150, terminal device 130). In some embodiments, storage device 150 may be part of processing device 120.

[0033] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this specification. Various changes and modifications can be made by those skilled in the art based on the content of this specification. Features, structures, methods, and other features of the exemplary embodiments described herein can be combined in various ways to obtain other and / or alternative exemplary embodiments. However, these changes and modifications will not depart from the scope of this specification.

[0034] Figure 2These are exemplary block diagrams of a medical image processing system according to some embodiments of this specification. Figure 2 As shown, the medical image processing system 200 may include a projection data acquisition module 210, a projection value acquisition module 220, a target projection value determination module 230, a target projection data determination module 240, a reconstruction module 250, and an image correction module 260.

[0035] The projection data acquisition module 210 can be used to acquire at least two sets of projection data for the region of interest. In some embodiments, the at least two sets of projection data may correspond to at least two motion phases, and each set of projection data may include at least two projection positions. For a detailed description of the initial projection data acquisition module 210, please refer to the relevant description of step 310, which will not be repeated here.

[0036] The projection value acquisition module 220 can be used to acquire the projection value corresponding to each projection position in each set of projection data. A detailed description of the projection value acquisition module 220 can be found in the relevant description of step 320, and will not be repeated here.

[0037] The target projection value determination module 230 can be used to determine the target projection value corresponding to each projection position based on at least two projection values ​​corresponding to each projection position. In some embodiments, the target projection value determination module 230 can determine the maximum projection value among the at least two projection values ​​corresponding to each projection position; and determine the target projection value corresponding to each projection position based on the maximum projection value. In some embodiments, the target projection value determination module 230 can determine the average projection value of the at least two projection values ​​corresponding to each projection position; and determine the target projection value corresponding to each projection position based on the average projection value. In some embodiments, the target projection value determination module 230 may perform one or more of the following operations: determining a target range in each set of projection data, wherein the target range may include projection data corresponding to a target object in the region of interest; obtaining the projection value corresponding to each first projection position in each set of projection data, wherein the first projection position is a projection position within the target range; determining the maximum projection value among at least two projection values ​​corresponding to each first projection position, and using the maximum projection value as the target projection value corresponding to the first projection position; obtaining the projection value corresponding to each second projection position in each set of projection data, wherein the second projection position is a projection position outside the target range; determining the average projection value among at least two projection values ​​corresponding to each second projection position, and using the average projection value as the target projection value corresponding to the second projection position. In some embodiments, each set of projection data may correspond to a first direction and a second direction that are perpendicular to each other, wherein the first direction may correspond to the position of at least two detection units of the detector, and the second direction may correspond to a projection angle. In some embodiments, the target projection value determination module 230 may determine a first boundary and a second boundary along the first direction based on the position of the target object in the region of interest; and determine the target range based on the first boundary and the second boundary. In some embodiments, the target projection value determination module 230 can determine a first boundary and a second boundary based on the dimensional information of each set of projection data in the first direction, the size of the target object, and the dimensional information of the projection position in the first direction. In some embodiments, the target projection value determination module 230 can further determine the first boundary and the second boundary based on an expansion coefficient, wherein the expansion coefficient is used to adjust the first boundary and / or the second boundary along the first direction. A detailed description of the target projection value determination module 230 can be found in the relevant description of step 330, and will not be repeated here.

[0038] The target projection data determination module 240 can be used to obtain target projection data based on the target projection value corresponding to each projection position. A detailed description of the target projection data determination module 240 can be found in the relevant description of step 340, and will not be repeated here.

[0039] The reconstruction module 250 can be used to reconstruct a target image of the region of interest based on the target projection data. A detailed description of the reconstruction module 250 can be found in the relevant description of step 350, and will not be repeated here.

[0040] The image correction module 260 can be used to perform attenuation correction on PET images based on the target image. A detailed description of the image correction module 260 can be found in the relevant description of step 360, and will not be repeated here.

[0041] Figure 3 This is an exemplary flowchart of an automatic hardening correction method according to some embodiments of this specification. In some embodiments, process 300 may be executed by medical image processing system 100 (e.g., processing device 120) or medical image processing system 200. For example, process 300 may be stored in a storage device (e.g., storage device 150, system storage unit) in the form of a program or instructions, and process 300 may be implemented when processing device 120 or medical image processing system 200 executes the instructions. The operational schematic diagram of process 300 presented below is illustrative. In some embodiments, the process may be accomplished using one or more additional operations not described and / or one or more operations not discussed. Additionally, Figure 3 The order of operations shown in the diagram and described below in process 300 is not restrictive.

[0042] Step 310: Obtain at least two sets of projection data for the region of interest. Specifically, step 310 can be performed by the projection data acquisition module 210.

[0043] The region of interest can be a specific area on the scanned object. For example, the scanned object can be patient A, and the region of interest can be the upper chest of patient A.

[0044] In some embodiments, the projection data acquisition module 210 can utilize an imaging device to acquire projection data of the region of interest. For example, the X-ray source of a CT imaging device can generate a radiation beam; the radiation beam passes through the region of interest, and its energy attenuates; the attenuated ray is incident on the detector, and interacts with the working medium (such as gas, scintillation crystal, semiconductor, etc.) within multiple detection elements of the detector, losing energy and generating ionization and / or excitation; the detector converts the ionization or excitation effect into projection data.

[0045] Figure 10 This is a schematic diagram illustrating the mapping relationship between the object's spatial domain and its projection domain, according to some embodiments of this specification. For example... Figure 10As shown, the projection data in the projection domain can be described using a chord graph. The horizontal axis in the chord graph represents the detection element, and the vertical axis represents the projection angle. Therefore, a single projection can be represented by a horizontal line in the chord graph, and the projection of each individual point in the region of interest in the object's spatial domain is a sine curve in the chord graph. Furthermore, the data collected at all projection angles can form a two-dimensional image, and the intensity value of the pixels in the two-dimensional image can represent the amplitude of the projection sampling.

[0046] Figure 11 This is a schematic diagram illustrating the conversion of acquired signals into the projection domain according to some embodiments of this specification. For example... Figure 11 As shown, when the parallel radiation beam of the CT equipment illuminates the scanned object at an angle of 0°, the resulting projection data (i.e., chordal data) is the horizontal line corresponding to the angle of 0°. When the scanned object is illuminated at an angle of n°, the resulting projection data is the horizontal line corresponding to the angle of n°, ..., when the scanned object is illuminated at an angle of 180°, the resulting projection data is the horizontal line corresponding to the angle of 180°.

[0047] Step 320: Obtain the projection value corresponding to each projection position in each set of projection data. Specifically, step 320 can be executed by the projection value acquisition module 220.

[0048] Acquisition time can be the time it takes for the imaging device to acquire each set of projection data.

[0049] In some embodiments, the projection value acquisition module 220 can acquire projection data for at least one respiratory cycle of the scanned object. One respiratory cycle of the scanned object can be the time it takes for the scanned object to complete one respiratory movement. For example, if patient A completes one respiratory movement in 5 seconds, then one respiratory cycle of patient A is 5 seconds.

[0050] Each respiratory movement can include multiple phases, such as end-expiration and end-inspiration. Each respiratory cycle can include multiple phases. In some embodiments, each phase can correspond to a respiratory phase of the scanned object. For example, the first phase can correspond to end-expiration, and the second phase can correspond to end-inspiration.

[0051] In some embodiments, different acquisition times can correspond to different phases of motion. For example, an acquisition time of 1 second can correspond to the first phase of the first motion cycle.

[0052] In some embodiments, at least two sets of projection data can correspond to at least two kinetic phases. For example, the projection data acquisition module 210 can sequentially collect the first set of projection data S1, the second set of projection data S2, the third set of projection data S3, ... the j-th set of projection data Sj on the upper chest of patient A during the first respiratory kinetic phase T1, the second respiratory kinetic phase T2, the third respiratory kinetic phase T3, ... the j-th respiratory kinetic phase Tj.

[0053] In some embodiments, each set of projection data may include at least two projection locations.

[0054] The projection position can be the position of the detector element when it receives the attenuated rays. In some embodiments, the projection position can include the rotation angle of the detector (or detector element) and / or the relative position of the detector element on the detector.

[0055] Figure 5 This is a schematic diagram illustrating the acquisition of projection data using a scanning device according to some embodiments of this specification. For example... Figure 5 As shown, within each motion phase, the positions of the X-ray source and detector remain relatively constant, and they rotate synchronously 90° clockwise around the scanned object. Then, as... Figure 6 The detector element i in the first row of the first column shown 11 The corresponding projection position W1 (not shown) can be [θ1 = 90°, m1 = 1, n1 = 1]. Furthermore, the X-ray source and detector can continue to rotate synchronously 90° clockwise around the scanned object, then as follows... Figure 6 The detector element i in the first row of the first column shown 11 The corresponding projection position W2 (not shown) can be [θ2=180°,m2=1,n2=1].

[0056] Similarly, the projection data acquisition module 210 can determine the projection position of each detection element on the detector at different rotation angles during each motion phase.

[0057] In some embodiments, the projection data may be three-dimensional data converted from multiple rows of detectors. Figure 7 This is a schematic diagram illustrating a set of projection data according to some embodiments of this specification. For example... Figure 7As shown, a set of projection data can be three-dimensional data in the O-mnθ three-dimensional coordinate system, and can include the projection positions of m×n (i.e., m columns × n rows) detector elements at different rotation angles. For example, a set of projection data can include 12800 projection positions of 3200 (100 columns × 32 rows) detector elements at four rotation angles θ (0°, 90°, 180°, and 270°), where W1 and W2 can respectively represent detector element i in the first column and first row of the detector. 11 The projection positions are at rotation angles of 90° and 180°, respectively.

[0058] In some embodiments, the projection data can be two-dimensional data converted from a single row of detectors. Continuing with... Figure 7 For example, a set of projection data can be two-dimensional data in the O-mθ two-dimensional coordinate system, which can include the projection positions of m detection elements at different rotation angles. For example, a set of projection data can include 400 projection positions of 100 detection elements at 4 rotation angles θ (0°, 90°, 180° and 270°).

[0059] As described above, the detector can receive the radiation beam passing through the scanned object and convert it into a corresponding signal. In some embodiments, the detector element at each projection position can convert the attenuated rays received at different acquisition times into corresponding signals. Different acquisition times can correspond to different respiratory phases. For example, the detector element at projection position W1 can convert the attenuated rays received in the first respiratory phase T1, the second respiratory phase T2, the third respiratory phase T3, ..., the j-th respiratory phase Tj into corresponding signals. Similarly, the projection element at projection position W2 can convert the attenuated rays received at different acquisition times into corresponding signals. The projection element at projection position W3 can convert the attenuated rays received at different acquisition times into corresponding signals.

[0060] Furthermore, in some embodiments, the projection value acquisition module 220 can acquire the projection value corresponding to each projection position in each set of projection data based on the signal corresponding to each projection position at each acquisition time. Continuing the above example, the projection value acquisition module 220 can acquire the projection value corresponding to each projection position W1 in the first respiratory phase T1, the second respiratory phase T2, the third respiratory phase T3, ... the j-th respiratory phase Tj based on the signal corresponding to the projection position W1 in the first respiratory phase T1, the second respiratory phase T2, the third respiratory phase T3, ... the j-th respiratory phase Tj. Obtain the projection position W1 corresponding to the projection value in the first group of projection data S1, the second group of projection data S2, the third group of projection data S3, ... the j-th group of projection data Sj. Similarly, the projection value acquisition module 220 can acquire the projection value corresponding to the projection position W2 in each group of projection data. The projection value of projection position W3 in each group of projection data.

[0061] Step 330: Based on at least two projection values ​​corresponding to each projection position, determine the target projection value corresponding to each projection position. Specifically, step 330 can be executed by the target projection value determination module 230.

[0062] The target projection value can be the projection value used to reconstruct the image.

[0063] In some embodiments, the target projection value determination module 230 can determine the maximum projection value among at least two projection values ​​corresponding to each projection position.

[0064] Figure 8 This is a schematic diagram illustrating the determination of target projection values ​​according to some embodiments of this specification. For example, such as... Figure 8 As shown, the target projection value determination module 230 can determine the projection value corresponding to the projection position W1 in the first set of projection data S1, the second set of projection data S2, the third set of projection data S3...the j-th set of projection data Sj. The maximum projection value in is Similarly, the target projection value determination module 230 can determine the maximum projection value among the projection positions W2, W3... in the first group of projection data S1, the second group of projection data S2, the third group of projection data S3... the j-th group of projection data Sj, respectively.

[0065] Furthermore, in some embodiments, the target projection value determination module 230 can determine the target projection value corresponding to each projection position based on the maximum projection value.

[0066] Continue with Figure 8 For example, the target projection value determination module 230 can determine the target projection value P at the projection position W1. W1 That is, the maximum projection value corresponding to the projection position W1. Similarly, the target projection value determination module 230 can determine the target projection value P at projection positions W2, W3... W2 P W3 ...represent the maximum projection values ​​corresponding to projection positions W2, W3...

[0067] In some embodiments of this specification, the maximum projection value of each projection position in the projection data sequence is used as the target projection value to obtain target projection data. This allows the target image reconstructed based on the target projection data to reduce motion artifacts caused by the scanning object's breathing and other movements when used for PET image attenuation correction. A detailed description of the reconstructed target image can be found in the relevant description of step 350, and will not be repeated here.

[0068] In some embodiments, the target projection value determination module 230 can determine the average projection value of at least two projection values ​​corresponding to each projection position.

[0069] For example, such as Figure 8 As shown, the target projection value determination module 230 can determine the projection value corresponding to the projection position W1 in the first set of projection data S1, the second set of projection data S2, the third set of projection data S3...the j-th set of projection data Sj. The average projection value in Similarly, the target projection value determination module 230 can determine the average projection values ​​of the corresponding projection values ​​in the first set of projection data S1, the second set of projection data S2, the third set of projection data S3, ... the j-th set of projection data Sj, respectively.

[0070] Furthermore, in some embodiments, the target projection value determination module 230 can determine the target projection value corresponding to each projection position based on the average projection value.

[0071] Continue with Figure 8 For example, the target projection value determination module 230 can determine the target projection value P at the projection position W1. W1 That is, the average projection value corresponding to the projection position W1 Similarly, the target projection value determination module 230 can determine the target projection value P at projection positions W2, W3... W2 P W3 ...represent the average projection values ​​corresponding to projection positions W2, W3...

[0072] In some embodiments of this specification, the average value of the projection values ​​of each projection position in the projection data sequence is used as the target projection value to obtain target projection data. This allows for reduced noise in the background area outside the target object range in the reconstructed target image based on the target projection data. A detailed description of the target range can be found in the relevant description of step 410, and a detailed description of the reconstructed target image can be found in the relevant description of step 350, which will not be repeated here.

[0073] In some embodiments, the target projection value determination module 230 may determine the target projection value based on the target range.

[0074] For a detailed description of determining the target projection value based on the target range, please refer to [link / reference]. Figure 4 The details and related descriptions will not be repeated here.

[0075] Step 340: Obtain target projection data based on the target projection value corresponding to each projection position. Specifically, step 340 can be executed by the target projection data determination module 240.

[0076] Target projection data can be projection data used to reconstruct the image. In some embodiments, target projection data may include target projection values ​​corresponding to each location. For example... Figure 8 As shown, the target projection data S may include the target projection value P corresponding to the projection position W1. W1 Similarly, the target projection data S may include the target projection values ​​P corresponding to the projection positions W2, W3... W2 P W3 ...

[0077] Step 350: Based on the target projection data, reconstruct the target image of the region of interest. Specifically, step 350 can be performed by the reconstruction module 250.

[0078] The target image can be a scanned image of the region of interest. In some embodiments, the format of the target image may include, but is not limited to, Joint Photographic Experts Group (JPEG) format, Tagged Image File Format (TIFF) format, Graphics Interchange Format (GIF) format, Kodak Flash PiX (FPX) format, Digital Imaging and Communications in Medicine (DICOM) format, etc.

[0079] In some embodiments, the reconstruction module 250 can reconstruct a target image of the region of interest based on the target projection data according to a reconstruction algorithm. In some embodiments, the reconstruction algorithm may include, but is not limited to, analytical reconstruction algorithms, iterative reconstruction algorithms, etc.

[0080] Step 360: Based on the target image, perform attenuation correction on the PET image. Specifically, step 360 can be performed by the image correction module 260.

[0081] PET images are reconstructed images reflecting the metabolic state of various tissues in the human body by detecting rays emitted from the body. However, the accuracy of the images is affected by scattering and attenuation of rays as they pass through human tissue. Therefore, attenuation correction is required for PET images to obtain corrected PET images. In some embodiments, the PET image of the region of interest can be obtained by scanning the region of interest with imaging device 110, or by scanning the region of interest with other imaging devices.

[0082] In some embodiments, the image correction module 260 can obtain the attenuation coefficient in the PET image based on the target image, thereby performing attenuation correction on the PET image. For example, the attenuation coefficient can be multiplied by the PET image to obtain the corrected PET image. Since the CT radiation energy is different from the PET radiation energy, it is necessary to convert the attenuation coefficient obtained under CT energy to the attenuation coefficient under PET energy.

[0083] In some embodiments, the method for attenuation correction of PET images based on target images may include scaling, segmentation, bilinear and dual-energy methods, etc., and this application does not limit the scope of the method.

[0084] In some embodiments, the image correction module 260 can also correct the SPECT image based on the target image. The method for correcting the SPECT image based on the target image can be found in the method for correcting PET images, and will not be described again here.

[0085] In some embodiments of this specification, the system 200 determines the target projection data corresponding to the target object from multiple sets of projection data in the projection domain, and reconstructs the target image based on the target projection data. This can achieve the goal of obtaining a target image corresponding to the projection data sequence without having to reconstruct each set of projection data, but only reconstructing one set of projection data (i.e., the target projection data), thereby improving the efficiency of image reconstruction.

[0086] It should be noted that the above description of process 300 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Various changes and modifications can be made by those skilled in the art based on the description herein. However, these changes and modifications do not depart from the scope of this specification. In some embodiments, process 300 may include one or more additional operations, or one or more of the aforementioned operations may be omitted.

[0087] Figure 4This is an exemplary flowchart illustrating the determination of target projection values ​​based on a target range according to some embodiments of this specification. In some embodiments, process 400 may be executed by medical image processing system 200 (e.g., target projection value determination module 230) and / or processing device 120. For example, process 400 may be stored in a storage device (e.g., storage device 150, system storage unit) in the form of a program or instructions, and process 400 may be implemented when medical image processing system 200 (e.g., target projection value determination module 230) and / or processing device 120 executes the instructions. The operational schematic diagram of process 400 presented below is illustrative. In some embodiments, the process may be accomplished using one or more additional operations not described and / or one or more operations not discussed. Additionally, Figure 4 The order of operations shown in the diagram and described below in process 400 is not restrictive.

[0088] Step 410: Determine the target range in each set of projection data.

[0089] In some embodiments, each set of projection data may correspond to a first direction and a second direction that are perpendicular to each other.

[0090] The first direction can be the plane direction of the detector. In some embodiments, the first direction can correspond to the positions of at least two detection units of the detector.

[0091] In some embodiments, the first direction can be a one-dimensional direction. For example, the first direction can be the column direction of the detection elements on a single row of detectors. Figure 7 As shown, the first direction of the two-dimensional projection data in the O-mθ two-dimensional coordinate system can be the m direction.

[0092] In some embodiments, the first direction can be a two-dimensional direction. For example, the first direction may include the column direction and row direction of the detection units on a multi-row detector. Figure 7 As shown, the first direction of the three-dimensional projection data in the O-mnθ three-dimensional coordinate system can include the m direction and the n direction.

[0093] In some embodiments, the second direction may correspond to a projection angle. For example... Figure 7 As shown, the second direction can be the θ direction, which is perpendicular to the first direction m and / or n.

[0094] The target object can be a part of the user's area of ​​interest that they are particularly interested in. For example, if the area of ​​interest is patient A's upper chest, the target object could be the heart in patient A's upper chest.

[0095] The target range for each set of projection data can be the range of the projection position of the target object in each set of projection data. In some embodiments, the target range can include the projection data corresponding to the target object in the region of interest. Continuing the example above, the target range can include the projection data of the heart in the upper chest of patient A.

[0096] The first and second boundaries can be the limits of the target range at both ends in the first direction.

[0097] It is understood that the position of the target object in the region of interest can correspond to the position of the target object's projection in the projection data. Therefore, in some embodiments, the target projection value determination module 230 can determine the first boundary and the second boundary along the first direction based on the position of the target object in the region of interest.

[0098] In some embodiments, the target projection value determination module 230 can determine the first boundary and / or the second boundary based on the dimensional information of each set of projection data in the first direction, the size of the target object, and the dimensional information of the projection position in the first direction.

[0099] In some embodiments, the target projection value determination module 230 can determine the direction of the first boundary and / or the second boundary based on the dimensional information of each set of projection data in the first direction.

[0100] As mentioned above, the first direction can be the plane direction of the detector. Therefore, the dimension of the projected data in the first direction can be the same as the dimension of the detector elements arranged on the detector. For example, the dimension of each set of two-dimensional projected data in the first direction is one dimension (i.e., the column direction of the detector elements on a single row of detectors). As another example, the dimension of each set of three-dimensional projected data in the first direction is two dimensions (i.e., the column direction and row direction of the detector elements on multiple rows of detectors).

[0101] Specifically, in some embodiments, the target projection value determination module 230 can determine the dimensions of the first boundary and / or the second boundary based on the dimension information of the projection data in the first direction.

[0102] In some embodiments, the dimension of the projected data in the first direction may be the same as the dimension of the corresponding first boundary and / or second boundary. For example, if the dimension of each set of two-dimensional projected data in the first direction is one-dimensional (i.e., the column direction of the detection unit on a single row detector), then the dimension of the corresponding first boundary and / or second boundary is one-dimensional. As another example, if the dimension of each set of three-dimensional projected data in the first direction is two-dimensional (i.e., the column direction and row direction of the detection unit on a multi-row detector), then the dimension of the corresponding first boundary and / or second boundary is two-dimensional.

[0103] Furthermore, in some embodiments, the target projection value determination module 230 can determine the direction of the first boundary and / or the second boundary based on the dimensional information of the projection data in the first direction and the dimensions of the first boundary and / or the second boundary.

[0104] In some embodiments, the direction of the first boundary and / or the second boundary of each dimension may be perpendicular to one dimension of the first direction and parallel to the second direction.

[0105] Figure 9A and Figure 9B This is a schematic diagram illustrating the determination of a target range according to some embodiments of this specification.

[0106] For example, such as Figure 9A As shown, the first direction of the two-dimensional projection data is the m direction (i.e., the column direction of the detection unit on the single-row detector), and the second direction is the θ direction (i.e., the direction of the projection angle). Then, the direction of the first boundary and / or the second boundary can be perpendicular to the m direction and parallel to the θ direction. The first boundary and the second boundary can be the boundaries of the two-dimensional projection data at both ends (left and right ends) in the m direction (i.e., the column direction of the detection unit on the single-row detector).

[0107] For example, such as Figure 9B As shown, the first direction of the three-dimensional projection data includes the m direction (i.e., the column direction of the detection unit on the single-row detector) and the n direction (i.e., the row direction of the detection unit on the single-row detector), and the second direction is the θ direction (i.e., the direction of the projection angle). Then the direction of the first boundary and / or the second boundary may include: the boundaries on both sides (left and right sides) of the Onθ plane direction which is perpendicular to the m direction and parallel to the θ direction, and the boundaries on both sides (upper and lower sides) of the Omθ plane direction which is perpendicular to the n direction and parallel to the θ direction.

[0108] In some embodiments, the target projection value determination module 230 can obtain the position of the first boundary and / or the second boundary in the first direction based on the size information of the target object and the projection position in the first direction, thereby determining the first boundary and the second boundary along the first direction.

[0109] The size of the target object can be the size of the target object in the first direction.

[0110] In some embodiments, the size of the target object can be determined based on the standard size of the target object. The standard size of the target object can be the maximum diameter of the standard model corresponding to the target object. For example, if the target object is a heart, the standard size of the heart can be the maximum diameter of the standard heart model, which is 14 cm. In some embodiments, the standard model of the target object can be determined based on a medical model database.

[0111] It is understandable that as the detector rotates, the detection elements that receive the attenuated rays passing through the edge of the target object differ. For example... Figure 5 As shown, when the detector is not rotated (i.e., the projection angle is 0°), the maximum diameter direction of the target object is parallel to the detector plane, and the detection elements a and b that receive attenuated rays passing through the edge of the target object are used. When the detector is rotated 90° clockwise (i.e., the projection angle is 90°), the maximum diameter direction of the target object is not parallel to the detector plane, and the detection elements c and d that receive attenuated rays passing through the edge of the target object are used. Therefore, in some embodiments, the target projection value determination module 230 can use the maximum size of the standard model as the standard size of the target object to determine the first boundary and the second boundary.

[0112] In some embodiments, the target projection value determination module 230 can determine the ratio of the size of the target object to the standard size (i.e., the first ratio) based on the ratio of the source-image distance to the source-axis distance of the imaging device. For example, if the source-image distance of the imaging device is 20cm and the source-axis distance is 10cm, then the first ratio is 2.

[0113] Furthermore, in some embodiments, the target projection value determination module 230 can determine the size of the target object based on a standard size and a first ratio. For example, the size of the heart can be determined to be 28cm by multiplying the standard size of the heart (14cm) by the first ratio of 2.

[0114] In some embodiments, the target projection value determination module 230 can determine the size information of the projection position in the first direction based on the number of detector elements and the size of the detector. Specifically, the size information of the projection position in the first direction can be determined based on the ratio of the size of the detector in the first direction to the number of detector elements.

[0115] For example, if a multi-row detector with 100 columns and 32 rows has dimensions of 100cm and 32cm in the first direction m and n respectively, then the dimensions of the projected position in the first direction m are 100 / 100 = 1cm and the dimensions in the first direction n are 32 / 32 = 1cm.

[0116] Specifically, in some embodiments, the target projection value determination module 230 can determine the position of the first boundary and the second boundary in the first direction according to formula (1). Where L1 and L2 represent the first boundary and the second boundary, respectively; N represents the size of the projection data in the first direction; d represents the size of the target object; and p represents the size of each projection position in the first direction.

[0117] For example, the target projection value determination module 230 can determine the first boundary along the first direction (i.e., the column direction of the detection unit) based on the target object's size of 28 cm and the projection position's size of 1 cm in the first direction. Second boundary

[0118] For example, the target projection value determination module 230 can determine the first boundary of the column direction dimension of the detection unit along the first direction based on the target object's size of 28cm and the projection position's size of 1cm in the first direction (i.e., the first direction of each set of three-dimensional projection data includes the column direction and row direction of the detection unit). Second boundary The first boundary of the array direction dimension of the detector unit along the first direction. Second boundary

[0119] Furthermore, in some embodiments, the target projection value determination module 230 can determine the target range based on the first boundary and the second boundary. Specifically, in some embodiments, the target projection value determination module 230 can determine the area between the first boundary and the second boundary as the projection range.

[0120] For example, such as Figure 9A As shown, the target area corresponding to the two-dimensional projection data can be a rectangular region between the first boundary (the dashed line at the left end of the column direction) and the second boundary (the dashed line at the right end of the column direction). For example, as... Figure 9B As shown, the target range corresponding to the three-dimensional projection data can be a cuboid region between the first boundary (left-end Onθ plane and upper-end Omθ plane) and the second boundary (right-end Onθ plane and lower-end Omθ plane).

[0121] In some embodiments of this specification, the target range corresponding to the target object in the projection data is determined by the first boundary and the second boundary. The projection value corresponding to the target object is segmented in the projection domain. That is, it is not necessary to segment the target object in the image sequence in the image domain. Thus, it is possible to process the projection value corresponding to the target object in the projection data sequence in the projection domain. The target image is directly reconstructed based on the processed (i.e., the reduced data volume) projection data, avoiding the reconstruction of all projection data and improving the efficiency of image processing for the target object.

[0122] Because the projected value corresponding to the target object cannot be accurately identified in the projection domain, Llow and Lhigh are used for partitioning. The expansion coefficient can be a parameter for adjusting the errors of the first and / or second boundaries. In some embodiments, the errors of the first and / or second boundaries may be due to the difference between the actual size of the target object and the size of the annotated model. It is understood that the difference between the actual size of the target object and the size of the standard model may cause the determined first and / or second boundaries to be located on or away from the target object.

[0123] In some embodiments, the target projection value determination module 230 can determine the expansion coefficient based on the features of the scanned object and the relationship between the feature coefficients.

[0124] In some embodiments, the characteristics of the scanned object may include, but are not limited to, at least one of the following: the scanned object's gender, age, height, weight, and medical history.

[0125] The eigenvalue evaluation relationship can characterize the eigenvalues ​​corresponding to different eigenvalues ​​of the scanned object. For example, if the scanned object's gender is male, the corresponding gender eigenvalue coefficient is 1; if the scanned object's gender is female, the corresponding gender eigenvalue coefficient is 0.8. As another example, if the scanned object's age is greater than 18 years, the corresponding age eigenvalue coefficient is 1; if the scanned object's age is less than or equal to 18 years, the corresponding age eigenvalue coefficient is the ratio of the scanned object's age to 18 years. Furthermore, if the scanned object has no medical history related to the target object, the corresponding medical history eigenvalue coefficient is 1; if the scanned object has a medical history related to the target object, the corresponding medical history eigenvalue coefficient can be determined based on the medical history (e.g., if the target object is the heart, the medical history eigenvalue coefficient for a patient with cardiomegaly can be determined based on the actual size of the heart).

[0126] Specifically, in some embodiments, the target projection value determination module 230 can determine the corresponding feature coefficients based on the relationship between each feature and feature coefficient of the scanned object. For example, if patient A is female, 28 years old, and has no history of heart-related diseases, the gender feature coefficient for patient A can be determined to be 0.8, the age feature coefficient to be 1, and the medical history feature coefficient to be 1.

[0127] Furthermore, in some embodiments, the target projection value determination module 230 can process multiple feature coefficients to determine the corresponding expansion coefficients. In some embodiments, the processing methods may include, but are not limited to, averaging, weighted summation, multiplication, etc. Continuing the above example, the expansion coefficient corresponding to patient A's heart can be obtained by multiplying the gender feature coefficient 0.8, the age feature coefficient 1, and the medical history feature coefficient 1, which is 0.8.

[0128] In some embodiments, the target projection value determination module 230 may further determine the first boundary and the second boundary based on the expansion coefficient. In some embodiments, the expansion coefficient may be used to adjust the first boundary and / or the second boundary along a first direction. In some embodiments, the first boundary and the second boundary are adjusted based on the expansion coefficient according to formula (2). Where n represents the expansion coefficient.

[0129] For example, the target projection value determination module 230 can determine the first boundary based on the target object's size of 28cm, the projection position's size of 1cm in the first direction, and the dimension "1" of the first boundary and / or the second boundary (i.e., the first direction of each set of two-dimensional projection data is the column direction of the detection unit). Rounded to the left to 38cm, and the second boundary. Rounding to the right to 62cm means adjusting the first boundary 2cm to the right and the second boundary 2cm to the left, thus narrowing the target area.

[0130] For example, the target projection value determination module 230 can determine the first boundary based on the target object's size of 28cm, the projection position's size of 1cm in the first direction, and the dimension "2" of the first boundary and / or the second boundary (i.e., the first direction of each set of three-dimensional projection data includes the column direction and row direction of the detection unit). Rounded to the left, the result is 38cm. Rounded up to 4cm and the second boundary Rounded to the right, it is 62cm; Rounding down to 28cm means that the first boundary was adjusted 2cm to the right in the column direction and 2cm down in the row direction; the second boundary was adjusted 2cm to the left in the column direction and 2cm up in the row direction, thus narrowing the target range.

[0131] In some embodiments of this specification, adjusting the positions of the first and second boundaries using adjustment coefficients can eliminate the error in determining the target range caused by individual differences in the scanned object, thereby improving the accuracy of determining the target range.

[0132] Step 420: Obtain the projection value corresponding to each first projection position in each group of projection data.

[0133] The first projection position can be a projection position within the target area. For example... Figure 9B As shown, the first projection position can include the projection position W3 within the target range. The projection values ​​corresponding to the first projection position W3 in the first group of projection data S1, the second group of projection data S2, the third group of projection data S3, ... the j-th group of projection data Sj can be obtained. Similarly, the projection values ​​of other first projection positions (such as W4) in each group of projection data can be obtained.

[0134] Step 430: Determine the maximum projection value among at least two projection values ​​corresponding to each first projection position, and use the maximum projection value as the target projection value corresponding to the first projection position.

[0135] For a detailed description of determining the maximum projection value among at least two projection values ​​corresponding to each first projection position, please refer to step 330, which will not be repeated here.

[0136] For example, continue with Figure 9B For example, the projection value corresponding to the first projection position W3 can be determined. The maximum projection value in is and the maximum projection value The target projection value P corresponding to the first projection position W3 W3 Similarly, the target projection value P corresponding to the first projection position W4 can be determined. W4 ,...

[0137] Step 440: Obtain the projection value corresponding to each second projection position in each group of projection data.

[0138] The second projection position can be a projection position outside the target range. For example... Figure 9B As shown, the second projection position can include the projection position W1 outside the target range. The projection value corresponding to the second projection position W1 in the first group of projection data S1, the second group of projection data S2, the third group of projection data S3...the j-th group of projection data Sj can be obtained. Similarly, the projection values ​​of other second projection positions (such as W2) in each set of projection data can be obtained.

[0139] Step 450: Determine the average projection value among at least two projection values ​​corresponding to each second projection position, and use the average projection value as the target projection value corresponding to the second projection position.

[0140] For a detailed description of determining the average projection value among at least two projection values ​​corresponding to each second projection position, please refer to step 330, which will not be repeated here.

[0141] For example, continue with Figure 9B For example, the projection value corresponding to the second projection position W1 can be determined. The average projection value in and average projection value The target projection value P corresponding to the second projection position W1 W1Similarly, the target projection value P corresponding to the second projection position W1 can be determined. W1 ,...

[0142] In some embodiments, taking the CT parallel-beam acquisition mode as an example, a localization image of the region of interest is obtained based on a CT scan, and the position of the heart in the scanning field of view is determined by an organ segmentation algorithm. Based on the projection data (projection domain chord diagram) of the region of interest acquired by Cine CT, the position of the heart region in the localization image is marked with data acquired at different projection angles in the projection data, thereby determining the region containing the heart contour (target range), and further determining the first and second boundaries of the target range. The projection data of the heart contour region is used to determine the maximum projection value corresponding to each projection position by taking the maximum value, and the other regions outside the heart contour region are used to determine the average projection value corresponding to each projection position by taking the average value. These projection values ​​are then merged, and image reconstruction is performed to obtain the target CT image used for attenuation correction of PET reconstruction. It should be noted that this embodiment is not limited to the CT parallel-beam acquisition mode; other acquisition methods such as cone-beam can also be solved in a similar way.

[0143] In some embodiments of this specification, the maximum projection value of each projection position corresponding to the target object (i.e., within the target range) in the projection data sequence is taken as the target projection value within the target range, and the average projection value of each projection position corresponding to the background area (i.e. outside the target range) in the projection data sequence is taken as the target projection value outside the target range. This allows the target image reconstructed based on the target projection data to reduce motion artifacts caused by the breathing and other movements of the scanned object in the image area of ​​the target object, while also reducing noise interference in the background area at the edge of the target object, thereby improving the accuracy of the target image.

[0144] The beneficial effects that the embodiments of this specification may bring include, but are not limited to: (1) determining the target projection data corresponding to the target object from multiple sets of projection data in the projection domain, and reconstructing the target image based on the target projection data, which can achieve the goal of obtaining a target image corresponding to the projection data sequence without reconstructing each set of projection data, but only reconstructing one set of projection data (i.e., the target projection data), thereby improving the efficiency of image reconstruction; (2) determining the target projection sequence from the projection data sequence based on the maximum value method or the average value method, which can meet the different needs of users for image quality, such as reducing motion artifacts in the target object area or reducing noise in the background area in the target image; (3) taking the maximum projection value of each projection position corresponding to the target object (i.e., within the target range) in the projection data sequence as the target projection value within the target range, and taking the background area as the target projection value, the target projection data can be reconstructed. The average projection value of each projection position (i.e., outside the target range) in the projection data sequence is used as the target projection value outside the target range, thereby obtaining the target projection data, making the attenuation correction factor more accurate, achieving a certain motion correction effect, and reducing noise interference in the background area of ​​the target object edge, thereby improving the accuracy of the target image; (4) The target range corresponding to the target object in the projection data is determined by the first boundary and the second boundary, and the projection value corresponding to the target object is segmented in the projection domain. That is, there is no need to segment the target object in the image sequence in the image domain, so that the projection value corresponding to the target object in the projection data sequence can be processed in the projection domain, and the target image is reconstructed directly based on the processed (i.e., after the data volume is reduced) projection data, avoiding the reconstruction of all projection data and improving the efficiency of image processing for the target object.

[0145] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0146] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0147] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0148] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0149] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0150] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0151] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A medical image processing method, characterized in that, include: Acquire at least two sets of projection data for the region of interest, wherein the at least two sets of projection data correspond to at least two motion phases, and each set of projection data includes at least two projection positions, wherein the projection positions are the positions of the detector elements when they receive attenuated rays; Obtain the projection value corresponding to each projection position in each group of projection data; Based on at least two projection values ​​corresponding to each projection position, determine the target projection value corresponding to each projection position; Based on the target projection value corresponding to each projection position, the target projection data is determined from the at least two sets of projection data in the projection domain; Based on the target projection data, reconstruct the target image of the region of interest; Attenuation correction is performed on the PET image based on the target image.

2. The method as described in claim 1, characterized in that, Determining the target projection value corresponding to each projection position based on at least two projection values ​​corresponding to each projection position includes: Determine the maximum projection value among the at least two projection values ​​corresponding to each projection position; Based on the maximum projection value, the target projection value corresponding to each projection position is determined.

3. The method as described in claim 1, characterized in that, Determining the target projection value corresponding to each projection position based on at least two projection values ​​corresponding to each projection position includes: Determine the average projection value of the at least two projection values ​​corresponding to each projection position; Based on the average projection value, the target projection value corresponding to each projection position is determined.

4. The method as described in claim 1, characterized in that, Determining the target projection value corresponding to each projection position based on at least two projection values ​​corresponding to each projection position includes: Determine the target range in each set of projection data, wherein the target range includes the projection data corresponding to the target object in the region of interest; Obtain the projection value corresponding to each first projection position in each group of projection data, wherein the first projection position is the projection position within the target range; Determine the maximum projection value among the at least two projection values ​​corresponding to each first projection position, and use the maximum projection value as the target projection value corresponding to the first projection position; Obtain the projection value corresponding to each second projection position in each group of projection data, wherein the second projection position is the projection position outside the target range; Determine the average projection value among the at least two projection values ​​corresponding to each second projection position, and use the average projection value as the target projection value corresponding to the second projection position.

5. The method as described in claim 4, characterized in that, Each set of projection data corresponds to a first direction and a second direction that are perpendicular to each other. The first direction corresponds to the position of at least two detection units of the detector, and the second direction corresponds to the projection angle. Determining the target range in each set of projection data includes: Based on the position of the target object in the region of interest, a first boundary and a second boundary are determined along the first direction; The target range is determined based on the first boundary and the second boundary.

6. The method as described in claim 5, characterized in that, Determining the first boundary and the second boundary along the first direction based on the position of the target object in the region of interest includes: The first boundary and the second boundary are determined based on the dimensional information of each set of projection data in the first direction, the size of the target object, and the size information of the projection position in the first direction.

7. The method as described in claim 6, characterized in that, Also includes: The first boundary and the second boundary are further determined based on the expansion coefficient, which is used to adjust the first boundary and / or the second boundary along the first direction.

8. A medical image processing system, characterized in that, include: The projection data acquisition module is used to acquire at least two sets of projection data for the region of interest. The at least two sets of projection data correspond to at least two motion phases, and each set of projection data includes at least two projection positions, which are the positions of the detector elements when they receive attenuated rays. The projection value acquisition module is used to acquire the projection value corresponding to each projection position in each group of projection data; The target projection value determination module is used to determine the target projection value corresponding to each projection position based on at least two projection values ​​corresponding to each projection position; The target projection data determination module is used to determine target projection data from at least two sets of projection data in the projection domain based on the target projection value corresponding to each projection position. A reconstruction module is used to reconstruct a target image of the region of interest based on the target projection data; The image correction module is used to perform attenuation correction on the PET image based on the target image.

9. A medical image processing device, characterized in that, The device includes: At least one storage medium that stores computer instructions; At least one processor executes the computer instructions to implement the medical image processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer instructions that, when read by a computer, execute the medical image processing method as described in any one of claims 1 to 7.

Images