PET image reconstruction method

Abstract

The present application relates to the technical field of PET image processing, and provides a PET image reconstruction method, which can suppress the image noise level of the axial edge area during the iterative reconstruction process. Steps S1 to S3 of the present application are: according to the PET projection data, the initial PET image of the axial layer is updated to obtain the updated PET image of the axial layer, and the first weight and the second weight of the axial layer obtained according to the relaxation factor of the axial layer are weighted and summed with the updated PET image of the axial layer and the initial PET image of the axial layer to obtain the weighted PET image of the axial layer; step S4, if it is determined that the iteration end condition is met according to the weighted PET image of the axial layer, the target PET image of the axial layer is obtained according to the weighted PET image of the axial layer; step S5, if it is determined that the iteration end condition is not met according to the weighted PET image of the axial layer, the weighted PET image of the axial layer is determined as the initial PET image of the axial layer, and the execution of steps S1 to S3 is returned.

Description

Technical Field

[0001] The present application relates to the technical field of PET image processing, and in particular to a PET image reconstruction method, apparatus, computer equipment, storage medium and computer program product. Background Art

[0002] In the 3D acquisition mode of PET (Positron Emission Tomography), the sensitivity of the axial edge region is lower than that of the axial center region, and therefore, the count level of the axial edge region is lower than that of the axial center region.

[0003] In the statistics-based iterative reconstruction algorithm, the low count level in the axial edge area will cause overfitting of Poisson noise (Chinese name: Poisson noise), amplifying the image noise level in the axial edge area, resulting in the image noise level in the axial edge area being higher than the image noise level in the axial center area. Summary of the invention

[0004] Based on this, it is necessary to provide a PET image reconstruction method, apparatus, computer equipment, storage medium and computer program product to address the above technical issues.

[0005] The present application provides a PET image reconstruction method, the method comprising:

[0006] Step S1, updating the initial PET image of each axial layer according to the PET projection data to obtain an updated PET image of each axial layer;

[0007] Step S2, obtaining the relaxation factor of each axial layer;

[0008] Step S3, for each of the axial layers, obtaining a first weight and a second weight of the axial layer according to the relaxation factor of the axial layer, and performing weighted summation on the updated PET image of the axial layer and the initial PET image of the axial layer according to the first weight and the second weight to obtain a weighted PET image of the axial layer;

[0009] Step S4, if it is determined that the iteration end condition is met according to the weighted PET image of each axial layer, a target PET image of each axial layer is obtained according to the weighted PET image of each axial layer;

[0010] Step S5, if it is determined according to the weighted PET image of each axial layer that the iteration end condition is not satisfied, the weighted PET image of each axial layer is determined as the initial PET image of each axial layer, and the execution of steps S1 to S3 is returned.

[0011] In one embodiment, obtaining the relaxation factor of each axial layer includes:

[0012] For each of the axial layers, obtaining a layer number difference between the axial layer and the central axial layer;

[0013] If the layer number difference is greater than the layer number difference threshold, the relaxation factor of the axial layer is determined according to the layer number difference and a negative correlation function; the relaxation factor of the axial layer is smaller than the relaxation factor of the central axial layer.

[0014] In one embodiment, the method further comprises:

[0015] If the layer number difference is less than or equal to the layer number difference threshold, the relaxation factor of the central axial layer is determined as the relaxation factor of the axial layer.

[0016] In one embodiment, obtaining the relaxation factor of each axial layer includes:

[0017] For each of the axial layers, obtaining a layer number difference between the axial layer and the central axial layer;

[0018] The relaxation factor of the axial layer is determined according to the layer number difference and the negative correlation function.

[0019] In one embodiment, obtaining the relaxation factor of each axial layer includes:

[0020] For each voxel, obtain the detection probability of each response line for the voxel;

[0021] Get the attenuation coefficient of each response line;

[0022] For each of the axial layers, the relaxation factor of the axial layer is obtained according to the detection probability of each response line of each voxel on the axial layer and the attenuation coefficient of each response line.

[0023] In one embodiment, the relaxation factor of the axial layer is obtained according to the detection probability of each response line of each voxel on the axial layer and the attenuation coefficient of each response line, including:

[0024] For each voxel on the axial layer, weighted summing the detection probability of each response line of the voxel and the attenuation coefficient of each response line is performed to obtain a weighted value of the detection probability of the voxel;

[0025] The relaxation factor of the axial layer is obtained according to the detection probability weighted value of each voxel on the axial layer.

[0026] In one embodiment, according to the relaxation factor of the axial layer, obtaining the first weight and the second weight of the axial layer includes:

[0027] Obtaining a first weight of the axial layer according to a relaxation factor of the axial layer;

[0028] The second weight of the axial layer is obtained according to the difference between the set value and the relaxation factor of the axial layer.

[0029] In one embodiment, the initial PET image of each axial layer is updated according to the PET projection data to obtain an updated PET image of each axial layer, including:

[0030] Obtaining first PET projection prediction data according to the initial PET images of each axial layer;

[0031] According to the difference between the first PET projection prediction data and the PET projection data, the initial PET image of each axial layer is updated to obtain an updated PET image of each axial layer.

[0032] In one embodiment, the method further comprises:

[0033] Obtaining second PET projection prediction data according to the weighted PET images of each axial layer;

[0034] Whether the iteration end condition is satisfied is determined according to the difference between the second PET projection prediction data and the PET projection data.

[0035] The present application provides a PET image reconstruction device, the device comprising:

[0036] An updating module, used for updating the initial PET image of each axial layer according to the PET projection data to obtain an updated PET image of each axial layer;

[0037] A relaxation factor acquisition module, used to acquire the relaxation factor of each axial layer;

[0038] A weighted summation processing module is used to obtain, for each of the axial layers, a first weight and a second weight of the axial layer according to the relaxation factor of the axial layer, and to perform weighted summation on the updated PET image of the axial layer and the initial PET image of the axial layer according to the first weight and the second weight to obtain a weighted PET image of the axial layer;

[0039] A target image acquisition module, configured to obtain a target PET image of each axial layer according to the weighted PET image of each axial layer if it is determined that an iteration end condition is met according to the weighted PET image of each axial layer;

[0040] The execution module is returned to determine the weighted PET image of each axial layer as the initial PET image of each axial layer if it is determined that the iteration end condition is not met according to the weighted PET image of each axial layer, and return to the update module, the relaxation factor acquisition module and the weighted summation processing module.

[0041] The present application provides a PET image reconstruction method, the method comprising:

[0042] Acquire PET projection data;

[0043] Iteratively reconstruct the PET projection data at least once to obtain a target PET image of each axial layer;

[0044] Among them, during the current iterative reconstruction, the initial PET image of each axial layer at the current iteration is updated according to the PET projection data to obtain the updated PET image of each axial layer at the current iteration; the updated PET image of each axial layer at the current iteration and the initial PET image of each axial layer at the current iteration are weighted summed according to the two weights obtained from the relaxation factors of each axial layer to obtain the weighted PET image of each axial layer at the current iteration; the closer the axial layer within the set range is to the central axial layer, the greater the relaxation factor of the axial layer, the greater the weight assigned to the updated PET image of the axial layer at the current iteration, and the smaller the weight assigned to the initial PET image of the axial layer at the current iteration.

[0045] The present application provides a PET image reconstruction device, the device comprising:

[0046] A projection data acquisition module, used for acquiring PET projection data;

[0047] An iterative reconstruction module, used for iteratively reconstructing the PET projection data at least once to obtain a target PET image of each axial layer;

[0048] The iterative reconstruction module is also used to update the initial PET image of each axial layer at the current iteration according to the PET projection data during the current iterative reconstruction, so as to obtain the updated PET image of each axial layer at the current iteration; perform weighted summation on the updated PET image of each axial layer at the current iteration and the initial PET image of each axial layer at the current iteration according to two weights obtained from the relaxation factors of each axial layer, so as to obtain the weighted PET image of each axial layer at the current iteration; the closer the axial layer within the set range is to the central axial layer, the greater the relaxation factor of the axial layer, the greater the weight assigned to the updated PET image of the axial layer at the current iteration, and the smaller the weight assigned to the initial PET image of the axial layer at the current iteration.

[0049] The present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the above method.

[0050] The present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to execute the above method.

[0051] The present application provides a computer program product, on which a computer program is stored, and the computer program is executed by a processor to perform the above method.

[0052] The above-mentioned PET image reconstruction method, device, computer equipment, storage medium and computer program product, step S1, according to the PET projection data, the initial PET image of each axial layer is updated to obtain the updated PET image of each axial layer; step S2, the relaxation factor of each axial layer is obtained; step S3, for each axial layer, according to the relaxation factor of the axial layer, the first weight and the second weight of the axial layer are obtained, and according to the first weight and the second weight, the updated PET image of the axial layer and the initial PET image of the axial layer are weighted summed to obtain the weighted PET image of the axial layer; step S4, if it is determined that the iteration end condition is met according to the weighted PET image of each axial layer, then the target PET image of each axial layer is obtained according to the weighted PET image of each axial layer; step S5, if it is determined that the iteration end condition is not met according to the weighted PET image of each axial layer, then the weighted PET image of each axial layer is determined as the initial PET image of each axial layer, and the execution of steps S1 to S3 is returned; with the help of the scheme provided by the present application, the image noise level of the axial edge area can be suppressed during the iterative reconstruction process to avoid excessive convergence leading to an increase in the image noise level of the axial edge area. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A schematic diagram of a detector structure used in a PET system in one embodiment;

[0055] Figure 2 is a schematic flow chart of a method for reconstructing a PET image in one embodiment;

[0056] FIG3 (a) is a schematic diagram showing the change of relaxation factor with axial layer in one embodiment;

[0057] FIG3( b ) is a schematic diagram showing the change of relaxation factor with axial layer in another embodiment;

[0058] Figure 4 A schematic diagram of the change of relaxation factor with axial layer in yet another embodiment;

[0059] Figure 5 is a structural block diagram of a PET image reconstruction device in one embodiment;

[0060] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0062] The detector used in the PET system includes multiple detector rings arranged in parallel, such as Figure 1 In the example shown, the detector includes four detection rings, which are respectively denoted as Ring_1, Ring_2, Ring_3 and Ring_4, and each detection ring is composed of a number of detection units.

[0063] The direction in which several detection rings are arranged in parallel can be called the axial direction, and the plane perpendicular to the axial direction can be called the cross section. The axial direction can be recorded as z, and two mutually perpendicular directions can be defined on the cross section, which are recorded as x and y respectively.

[0064] The PET image reconstruction method provided in the present application can be executed by a computer device, including: Figure 2 Steps shown.

[0065] Step S1, updating the initial PET image of each axial layer according to the PET projection data to obtain an updated PET image of each axial layer.

[0066] In PET imaging technology, any two detection units in the detector can form a response line, which is called Line Of Response in English, or LOR in English. The number of detection units included in the detector can be determined based on the number of detection rings and the number of detection units included in each detection ring.

[0067] After performing a PET scan on the target object, the number of events detected by each response line can be obtained, and these event numbers form PET projection data, which belongs to the projection data acquired by the scan. For example, if there are m LORs, the PET projection data includes {y LOR_1 ,y LOR_2 ,...,yLOR_m},y LOR_m represents the number of events detected by the mth LOR; the PET projection data can be written in a matrix form, which can be in the form of m rows and one column, and can be denoted as Y.

[0068] Different axial layers correspond to different axial positions. For example, the axial positions corresponding to two adjacent axial layers are z_i and z_i+1 respectively, and these two axial layers can be recorded as slices. z_i and slice z_i+1 Among the plurality of axial layers, the axial layer that is consistent with or nearly consistent with the axial position of the central detection ring is the central axial layer; the central detection ring may be: among the plurality of detection rings arranged in parallel, the detection ring that is in the middle of the arrangement.

[0069] Slice in axial layers z_i Take this as an example:

[0070] If the current iteration is the first iteration, the axial layer slice can be randomly generated z_i The initial PET image at the first iteration (which can be denoted as ); Based on the PET projection data Update to get the axial layer slice z_i The updated PET image at the first iteration (which can be recorded as ).

[0071] If the current iteration is not the first iteration, the current iteration is recorded as the kth iteration, where k is a positive integer greater than or equal to 1, and the axial layer slice can be obtained. z_i The weighted PET image obtained at the k-1th iteration is used as the axial slice z_i The initial PET image at the kth iteration (which can be recorded as ); Based on the PET projection data Update to get the axial layer slice z_i The updated PET image at the kth iteration (which can be recorded as ).

[0072] Slice the axial layer z_i The processing method can be used to process other axial layers to obtain PET images of other axial layers that are updated in each iteration.

[0073] Step S2, obtaining the relaxation factor of each axial layer.

[0074] The present application sets corresponding relaxation factors for different axial layers, which can control the convergence degree of different axial layers and avoid image edge noise enhancement due to excessive convergence.

[0075] Step S3, for each axial layer, according to the relaxation factor of the axial layer, obtain the first weight and the second weight of the axial layer, and according to the first weight and the second weight, perform weighted summation on the updated PET image of the axial layer and the initial PET image of the axial layer to obtain a weighted PET image of the axial layer.

[0076] Slice in axial layers z_i Take this as an example:

[0077] If the current iteration is the first iteration, the axial slice can be obtained through step S1. z_i Updated PET image at the first iteration ; According to the axial layer slice z_i The relaxation factor of the axial slice can be obtained z_i The first weight and the second weight of the slice according to the axial layer z_i The first and second weights for the axial layer slice z_i Updated PET image at the first iteration With axial layer slice z_i Initial PET image at the first iteration Perform weighted summation to obtain the axial layer slice z_i The weighted PET image obtained at the first iteration. z_i The axial layer is within the set range, and the axial layer slice z_i The closer to the central axial layer, the smaller the axial layer slice z_i The larger the relaxation factor, the more z_i Updated PET image at the first iteration The larger the weight, the higher the initial PET image of the axial layer at the first iteration. The smaller the weight.

[0078] If the current iteration is not the first iteration, the current iteration is recorded as the kth iteration, and the axial layer slice can be obtained through step S1. z_i Updated PET image at the kth iteration ; According to the axial layer slice z_i The relaxation factor of the axial slice can be obtained z_i The first weight and the second weight of the slice according to the axial layer z_i The first and second weights for the axial layer slice z_i Updated PET image at the kth iteration With axial layer slicez_i Initial PET image at iteration k Perform weighted summation to obtain the axial layer slice z_i The weighted PET image obtained at the kth iteration. z_i The axial layer is within the set range, and the axial layer slice z_i The closer to the central axial layer, the smaller the axial layer slice z_i The larger the relaxation factor, the more z_i Updated PET image at the kth iteration The larger the weight, the more it is assigned to the axial layer slice. z_i Initial PET image at iteration k The smaller the weight.

[0079] Slice the axial layer z_i The processing method can be used to process other axial layers to obtain weighted PET images of other axial layers obtained in each iteration.

[0080] Step S4: if it is determined that the iteration end condition is met according to the weighted PET images of each axial layer, a target PET image of each axial layer is obtained according to the weighted PET images of each axial layer.

[0081] Slice in axial layers z_i Take this as an example:

[0082] If the current iteration is the first iteration, the axial layer slice can be obtained through step S2. z_i The weighted PET image obtained at the first iteration; if the iteration end condition is determined to be met based on the weighted PET image obtained at the first iteration of each axial layer, the axial layer slice z_i The weighted PET image obtained at the first iteration is determined as the axial slice z_i Target PET image.

[0083] If the current iteration is not the first iteration, the current iteration is recorded as the kth iteration, and the axial layer slice can be obtained through step S2. z_i The weighted PET image obtained at the kth iteration; if the weighted PET image obtained at the kth iteration of each axial layer determines that the iteration end condition is met, the axial layer slice z_i The weighted PET image obtained at the kth iteration is determined as the axial slice z_i Target PET image.

[0084] Slice the axial layerz_i The processing method can be used to process other axial layers to obtain target PET images of other axial layers.

[0085] Step S5, if it is determined according to the weighted PET images of each axial layer that the iteration end condition is not met, the weighted PET images of each axial layer are determined as the initial PET images of each axial layer, and the execution of steps S1 to S3 is returned.

[0086] Slice in axial layers z_i Take this as an example:

[0087] If the current iteration is the first iteration, the axial layer slice can be obtained through step S2. z_i The weighted PET image obtained at the first iteration; if the iteration end condition is determined to be met based on the weighted PET image obtained at the first iteration of each axial layer, the axial layer slice z_i The weighted PET image obtained at the first iteration is determined as the axial slice z_i Initial PET image at the second iteration.

[0088] If the current iteration is not the first iteration, the current iteration is recorded as the kth iteration, and the axial layer slice can be obtained through step S2. z_i The weighted PET image obtained at the kth iteration; if the weighted PET image obtained at the kth iteration of each axial layer determines that the iteration end condition is met, the axial layer slice z_i The weighted PET image obtained at the kth iteration is determined as the axial slice z_i The initial PET image at the k+1th iteration, and returns to execute steps S1 to S3.

[0089] Slice the axial layer z_i The processing method can be used to process other axial layers, obtain initial PET images of other axial layers in the next iteration, and return to execute steps S1 to S3.

[0090] In the above-mentioned PET image reconstruction method, step S1, according to the PET projection data, the initial PET image of each axial layer is updated to obtain the updated PET image of each axial layer; step S2, the relaxation factor of each axial layer is obtained; step S3, for each axial layer, according to the relaxation factor of the axial layer, the first weight and the second weight of the axial layer are obtained, and according to the first weight and the second weight, the updated PET image of the axial layer and the initial PET image of the axial layer are weighted summed to obtain the weighted PET image of the axial layer; step S4, if it is determined that the iteration end condition is met according to the weighted PET image of each axial layer, then the target PET image of each axial layer is obtained according to the weighted PET image of each axial layer; step S5, if it is determined that the iteration end condition is not met according to the weighted PET image of each axial layer, then the weighted PET image of each axial layer is determined as the initial PET image of each axial layer, and the execution of steps S1 to S3 is returned. With the help of the scheme provided in the present application, the image noise level in the axial edge area can be suppressed during the iterative reconstruction process to avoid excessive convergence leading to an increase in the image noise level in the axial edge area.

[0091] In one embodiment, obtaining the relaxation factor of each axial layer includes:

[0092] For each axial layer, the layer number difference between the axial layer and the central axial layer is obtained; if the layer number difference is greater than the layer number difference threshold, the relaxation factor of the axial layer is determined according to the layer number difference and the negative correlation function; the relaxation factor of the axial layer is less than the relaxation factor of the central axial layer.

[0093] Slice in axial layers z_i For example, you can slice z_i Subtract the central axial layer and use the subtraction result as the layer difference; if the layer difference is greater than the layer difference threshold, the axial layer slice can be determined based on the layer difference and the negative correlation function. z_i The relaxation factor and axial slice z_i The relaxation factor of is smaller than that of the central axial layer; the negative correlation function describes the negative correlation between the layer number difference and the relaxation factor. Therefore, the larger the layer number difference, the smaller the relaxation factor determined by the negative correlation function.

[0094] In one embodiment, the method provided by the present application further includes:

[0095] If the layer number difference is less than or equal to the layer number difference threshold, the relaxation factor of the central axial layer is determined as the relaxation factor of the axial layer.

[0096] Slice in axial layers z_i For example, you can slice z_iSubtract the central axial layer from the central axial layer, and use the subtraction result as the layer difference; if the layer difference is less than or equal to the layer difference threshold, the relaxation factor of the central axial layer is determined as the axial layer slice. z_i The relaxation factor.

[0097] Specifically, a curve of relaxation factor versus axial layer may be set, and the curve describes the values ​​of relaxation factors of different axial layers.

[0098] In the example shown in Figure 3 (a), there are 50 axial slices in total, and the central axial slice is the axial slice corresponding to the axial position z_25, which can be recorded as slice z_25 The axial layer corresponding to the axial position z_15 is recorded as slice z_15 , the axial layer corresponding to the axial position z_35 is recorded as slice z_35 ; slice z_15 To slice z_30 The axial layers between them belong to the axial layers close to the central axial layer. In the example shown in Figure 3(a), the layer number difference threshold is set to 10.

[0099] In slice z_15 To slice z_50 In the axial layers between, the relaxation factor of each non-central axial layer is the central axial layer slice z_25 Relaxation factor; axial slice z_25 The relaxation factor is the preset maximum Value, denoted as .

[0100] In slice z_1 To slice z_15 In the axial layers between, the relaxation factor of each axial layer is taken according to the following negative correlation function: ; j is a positive integer between 1 and 15; belongs to the pre-set value, is a value greater than 0; belongs to the pre-set value, is a value greater than 0; Axial slice z_j The relaxation factor.

[0101] In slice z_35 To slice z_50 In the axial layers between, the relaxation factor of each axial layer is taken according to the following negative correlation function: ; q is a positive integer between 15 and 50; belongs to the pre-set value, is a value less than 0; belongs to the pre-set value, is a value greater than 0; Axial slice z_q The relaxation factor.

[0102] In this embodiment, for any axial layer within the set range (such as in slice z_1 To slice z_15 Between or in slices z_35 To slice z_50 The closer the axial layer is to the central axial layer, the greater the relaxation factor of the axial layer. Correspondingly, the greater the weight assigned to the weighted PET image of the axial layer in each iteration, the smaller the weight assigned to the initial PET image of the axial layer in each iteration, thereby suppressing the image noise level in the axial edge area during the iterative reconstruction process.

[0103] In one embodiment, obtaining the relaxation factor of each axial layer includes:

[0104] For each axial layer, the layer number difference between the axial layer and the central axial layer is obtained; and the relaxation factor of the axial layer is determined according to the layer number difference and the negative correlation function.

[0105] Slice in axial layers z_i For example, you can slice z_i Subtract the central axial layer and use the subtraction result as the layer difference; determine the axial layer slice based on the layer difference and the negative correlation function z_i The relaxation factor and axial slice z_i The relaxation factor of is smaller than that of the central axial layer; the negative correlation function describes the negative correlation between the layer number difference and the relaxation factor. Therefore, the larger the layer number difference, the smaller the relaxation factor determined by the negative correlation function.

[0106] In the example shown in Figure 3 (b), there are 50 axial slices in total, and the central axial slice is the axial slice corresponding to the axial position z_25, which can be recorded as slice z_25 . Axial slice z_25 The relaxation factor is the preset maximum Value, denoted as .

[0107] In slice z_1 To slice z_24 In the axial layers between, the relaxation factor of each axial layer is taken according to the following negative correlation function: ; p is a positive integer between 1 and 24; belongs to the pre-set value, is a value greater than 0; belongs to the pre-set value, is a value greater than 0; Axial slice z_p The relaxation factor.

[0108] In slice z_26 To slice z_50 In the axial layers between, the relaxation factor of each axial layer is taken according to the following negative correlation function: ; h is a positive integer between 26 and 50; belongs to the pre-set value, is a value less than 0; belongs to the pre-set value, is a value greater than 0; Axial slice z_h The relaxation factor.

[0109] In this embodiment, for any axial layer within the set range (such as in slice z_1 To slice z_50 The closer the axial layer is to the central axial layer, the greater the relaxation factor of the axial layer. Correspondingly, the greater the weight assigned to the weighted PET image of the axial layer in each iteration, the smaller the weight assigned to the initial PET image of the axial layer in each iteration, thereby suppressing the image noise level in the axial edge area during the iterative reconstruction process.

[0110] In one embodiment, obtaining the relaxation factor of each axial layer includes:

[0111] For each voxel, the detection probability of each response line for the voxel is obtained; the attenuation coefficient of each response line is obtained; for each axial layer, the relaxation factor of the axial layer is obtained according to the detection probability of each response line of each voxel on the axial layer and the attenuation coefficient of each response line.

[0112] For example, the number of voxels is n and the number of response lines is m, and the n voxels can be recorded as: v_1, v_2, v_3, ..., v_ n .

[0113] When the number of response lines is m and the number of voxels is n, the orthographic projection model of the PET system can be expressed as: Y = PI; the orthographic projection model is expanded as:

[0114] .

[0115] Wherein, Y is the PET projection data acquired and scanned, P is the system response matrix, and I is the PET image;

[0116] Y includes the number of events detected by m LORs, which are recorded as: LOR_1 ,y LOR_2 ,y LOR_3 ,...,y LOR_m P includes the probability of m LOR detections for each voxel, represents the probability that the mth LOR detects the nth voxel. I includes the tracer concentration values ​​of the n voxels, which are respectively denoted as: i v_1 、i v_2 、i v_3 、...、i v_n .

[0117] For each voxel, such as voxel v_1, the detection probability of each response line to voxel v_1 can be obtained in the system response matrix, that is, { In addition, the attenuation coefficients of the m response lines can be obtained, which are recorded as { }, the attenuation coefficient of each response line can be obtained according to the forward projection of the attenuation image.

[0118] If in n voxels, located in the axial layer slice z_i The voxels include v_1 to v_5, then the detection probability of each response line for voxel v_1 can be { }、The detection probability of each response line for voxel v_2{ }、The detection probability of each response line for voxel v_3{ }、The detection probability of each response line for voxel v_4{ }、The detection probability of each response line for voxel v_5{ }, and the attenuation coefficients of the m response lines { }, get the axial layer slice z_i The relaxation factor.

[0119] In this embodiment, the relaxation factor of each axial layer is obtained by combining the detection probability of each response line for a voxel and the attenuation coefficient of each response line. In the iterative reconstruction process, the image noise level in the axial edge area is better suppressed.

[0120] In one embodiment, the relaxation factor of the axial layer is obtained according to the detection probability of each response line of each voxel on the axial layer and the attenuation coefficient of each response line, including:

[0121] For each voxel on the axial layer, the detection probability of each response line of the voxel and the attenuation coefficient of each response line are weighted and summed to obtain the weighted value of the detection probability of the voxel; according to the weighted value of the detection probability of each voxel on the axial layer, the relaxation factor of the axial layer is obtained.

[0122] Still using the above example, in the above example, the slice located in the axial layer z_i The voxels include v_1 to v_5. The detection probability of each response line to voxel v_1 is { } and the attenuation coefficients of the m response lines { Specifically, the attenuation coefficient of a certain response line is used as a weight to assign the detection probability of the response line to the voxel v_1, so as to obtain the weight of the detection probability of each response line to the voxel v_1; according to the weight, the detection probability of each response line to the voxel v_1 is { } to perform weighted summation, which can be expressed as: ; The weighted summation result can be used as the detection probability weighted value of voxel v_1.

[0123] In the above manner, the detection probability weighted value of voxel v_2, the detection probability weighted value of voxel v_3, the detection probability weighted value of voxel v_4 and the detection probability weighted value of voxel v_5 can also be obtained.

[0124] The axial slice can be obtained by adding the detection probability weighted value of voxel v_1, the detection probability weighted value of voxel v_2, the detection probability weighted value of voxel v_3, the detection probability weighted value of voxel v_4 and the detection probability weighted value of voxel v_5. z_i The relaxation factor.

[0125] Reference Figure 4 , Figure 4 The curve shown represents: the relaxation factor of each axial layer is obtained in the above manner; wherein, for Figure 4 For any axial layer shown, the closer the axial layer is to the central axial layer, the greater the relaxation factor of the axial layer. Correspondingly, the greater the weight assigned to the weighted PET image of the axial layer in each iteration, and the smaller the weight assigned to the initial PET image of the axial layer in each iteration. This can suppress the image noise level in the axial edge area during the iterative reconstruction process.

[0126] In this embodiment, for a certain voxel, the attenuation coefficient of the response line is used as the weight of the detection probability of the response line for the voxel, and the detection probability of each response line for the voxel is weighted and summed to obtain the weighted value of the detection probability of the voxel, thereby obtaining the relaxation factor of the corresponding axial layer. In the iterative reconstruction process, the image noise level in the axial edge area is better suppressed.

[0127] In one embodiment, according to the relaxation factor of the axial layer, the first weight and the second weight of the axial layer are obtained, including:

[0128] According to the relaxation factor of the axial layer, the first weight of the axial layer is obtained; according to the difference between the set value and the relaxation factor of the axial layer, the second weight of the axial layer is obtained.

[0129] The axial layer has two weights. The sum of the two weights must meet certain requirements. The value that meets the requirements can be used as the set value. In some scenarios, the sum of the two weights must be equal to 1, so 1 can be used as the set value.

[0130] Slice in axial layers z_i Take this as an example to introduce. You can get the axial layer slice z_i The relaxation factor is used as the axial layer slice z_i The first weight of the axial layer slice. z_i The relaxation factor is subtracted, and the difference is used as the axial slice z_i The second weight of . In this processing method, the axial layer slice can be z_i The first weight is assigned to the axial layer slice z_i The weighted PET images obtained at each iteration; the axial slice z_i The second weight is assigned to the axial layer slice z_i Initial PET images at each iteration.

[0131] In one embodiment, the initial PET image of each axial layer is updated according to the PET projection data to obtain an updated PET image of each axial layer, including:

[0132] According to the initial PET images of each axial layer, first PET projection prediction data are obtained; according to the difference between the first PET projection prediction data and the PET projection data, the initial PET images of each axial layer are updated to obtain updated PET images of each axial layer.

[0133] According to the initial PET images of each axial layer, PET projection prediction data can be obtained; the PET projection prediction data is obtained by prediction, and for the purpose of distinction, the PET projection prediction data is called the first PET projection prediction data. If the initial PET images of each axial layer are close to the real image, the first PET projection prediction data is closer to the PET projection data acquired by the scan. Therefore, according to the difference between the first PET projection prediction data and the PET projection data, the initial PET images of each axial layer can be updated to obtain the updated PET images of each axial layer.

[0134] For example, if the current iteration is the first iteration, the initial PET image of each axial layer at the first iteration can be randomly generated. According to the initial PET image of each axial layer at the first iteration and the PET projection data acquired and scanned, the first iteration image update information can be obtained, which can be recorded as . First iteration image update information It reflects the difference between the first PET projection prediction data and the PET projection data.

[0135] The specific formula for obtaining the first iteration image update information is as follows: , where P is the system response matrix, Y is the PET projection data acquired and scanned, and I 1 Including the initial PET images of each axial layer at the first iteration, P T is the transposed matrix of the system response matrix, and 1 is the all-1 matrix.

[0136] Slice in axial layers z_i Take this as an example to introduce that after getting the first iteration image update information After that, the information can be updated according to the first iteration image , for axial slice z_i Initial PET image at the first iteration Update to get the axial layer slice z_i Updated PET image at the first iteration .

[0137] Specifically, the first iteration image update information With axial layer slice z_i Initial PET image at the first iteration Perform multiplication and use the product as the axial slice z_i Updated PET image at the first iteration .

[0138] Similarly, the information can be updated based on the first iteration image , the initial PET images of other axial layers at the first iteration are updated to obtain the updated PET images of other axial layers at the first iteration.

[0139] In another exemplary embodiment, if the current iteration is not the first iteration, the current iteration is recorded as the kth iteration, and for each axial layer, the weighted PET image of the axial layer obtained at the k-1th iteration can be obtained, and the PET image is used as the initial PET image of the axial layer at the kth iteration. According to the initial PET image of each axial layer at the kth iteration and the PET projection data acquired and scanned, the image update information of the kth iteration can be obtained, which can be recorded as . The kth iteration image update information It reflects the difference between the first PET projection prediction data and the PET projection data.

[0140] The specific formula for obtaining the k-th iteration image update information is as follows: , where P is the system response matrix, Y is the PET projection data acquired and scanned, and I k Including the initial PET images of each axial layer at the kth iteration, P T is the transposed matrix of the system response matrix, and 1 is the all-1 matrix.

[0141] Slice in axial layers z_i Take the example to introduce that after getting the kth iteration image update information After that, the information can be updated according to the kth iteration image , for axial slice z_i Initial PET image at iteration k Update to get the axial layer slice z_i Updated PET image at iteration k .

[0142] Specifically, the kth iteration image update information With axial layer slice z_i Initial PET image at iteration k Perform multiplication and use the product as the axial slice z_i Updated PET image at iteration k .

[0143] Similarly, the information can also be updated based on the kth iteration image , the initial PET images of other axial layers at the kth iteration are updated to obtain updated PET images of other axial layers at the kth iteration.

[0144] In one embodiment, the method provided by the present application further includes:

[0145] According to the weighted PET images of each axial layer, second PET projection prediction data is obtained; according to the difference between the second PET projection prediction data and the PET projection data, it is determined whether the iteration end condition is met.

[0146] According to the weighted PET images of each axial layer, PET projection prediction data can be obtained; the PET projection prediction data is obtained by prediction, and for the purpose of distinction, the PET projection prediction data is called second PET projection prediction data. If the weighted PET images of each axial layer are close to the real image, the second PET projection prediction data is closer to the PET projection data acquired by the scan. Therefore, it can be determined whether the iteration end condition is met according to the difference between the second PET projection prediction data and the PET projection data.

[0147] Exemplarily, if the current iteration is the first iteration, after obtaining the weighted images of each axial layer obtained in the first iteration, the second PET projection prediction data can be generated according to the weighted images of each axial layer obtained in the first iteration. The difference between the second PET projection prediction data and the PET projection data acquired by the scan is obtained; if the difference is less than or equal to the difference threshold, it can be determined that the iteration end condition is met, and the weighted images of each axial layer obtained in the first iteration can be determined as the target PET images of each axial layer; if the difference is greater than the difference threshold, it can be determined that the iteration end condition is not met, and the weighted images of each axial layer obtained in the first iteration can be determined as the initial PET images of each axial layer in the second iteration, and the second iteration is entered.

[0148] Exemplarily, if the current iteration is not the first iteration, the current iteration is recorded as the kth iteration, and after obtaining the weighted images of each axial layer obtained at the kth iteration, the second PET projection prediction data can be generated according to the weighted images of each axial layer obtained at the kth iteration. The difference between the second PET projection prediction data and the PET projection data acquired and scanned is obtained; if the difference is less than or equal to the difference threshold, it can be determined that the iteration end condition is met, and the weighted images of each axial layer obtained at the kth iteration can be determined as the target PET images of each axial layer; if the difference is greater than the difference threshold, it can be determined that the iteration end condition is not met, and the weighted images of each axial layer obtained at the kth iteration can be determined as the initial PET images of each axial layer at the k+1th iteration, and the k+1th iteration is entered.

[0149] The present application also provides a PET image reconstruction method, the method comprising the following steps:

[0150] Acquire PET projection data; perform at least one iterative reconstruction on the PET projection data to obtain a target PET image of each axial layer; wherein, during the current iterative reconstruction, the initial PET image of each axial layer at the current iteration is updated according to the PET projection data to obtain an updated PET image of each axial layer at the current iteration; perform weighted summation of the updated PET image of each axial layer at the current iteration and the initial PET image of each axial layer at the current iteration according to two weights obtained from the relaxation factor of each axial layer to obtain a weighted PET image of each axial layer at the current iteration; the closer the axial layer within the set range is to the central axial layer, the greater the relaxation factor of the axial layer, the greater the weight assigned to the updated PET image of the axial layer at the current iteration, and the smaller the weight assigned to the initial PET image of the axial layer at the current iteration.

[0151] After performing a PET scan on the target object, the number of events detected by each response line can be obtained, and these event numbers form PET projection data, which belongs to the projection data acquired by the scan. For example, if there are m LORs, the PET projection data includes {y LOR_1 ,y LOR_2 ,...,y LOR_m},y LOR_m represents the number of events detected by the mth LOR; the PET projection data can be written in a matrix form, which can be in the form of m rows and one column, and can be denoted as Y.

[0152] Different axial layers correspond to different axial positions. For example, the axial positions corresponding to two adjacent axial layers are z_i and z_i+1 respectively, and these two axial layers can be recorded as slices. z_i and slice z_i+1 Among the plurality of axial layers, the axial layer that is consistent with or nearly consistent with the axial position of the central detection ring is the central axial layer; the central detection ring may be: among the plurality of detection rings arranged in parallel, the detection ring that is in the middle of the arrangement.

[0153] The present application sets corresponding relaxation factors for different axial layers, which can control the convergence degree of different axial layers and avoid image edge noise enhancement due to excessive convergence. Specifically, for any axial layer within the set range, the closer the axial layer is to the central axial layer, the greater the relaxation factor of the axial layer, and accordingly, the greater the weight assigned to the weighted PET image of the axial layer at each iteration, and the smaller the weight assigned to the initial PET image of the axial layer at each iteration, so that the image noise level in the axial edge area can be suppressed during the iterative reconstruction process.

[0154] Slice in axial layers z_i Take this as an example:

[0155] (1) If the current iteration is the first iteration, the axial layer slice can be randomly generated z_i The initial PET image at the first iteration (which can be denoted as ); Based on the PET projection data Update to get the axial layer slice z_i The updated PET image at the first iteration (which can be recorded as ).

[0156] According to the axial layer slice z_i The relaxation factor can be obtained by two weights; according to these two weights, the axial slice z_i Updated PET image at the first iteration With axial layer slice z_i Initial PET image at the first iteration Perform weighted summation to obtain the axial layer slice z_i The weighted PET image at the first iteration. If the axial slice z_i The axial layer is within the set range, and the axial layer slice z_i The closer to the central axial layer, the smaller the axial layer slice z_i The larger the relaxation factor, the more z_i Updated PET image at the first iteration The larger the weight, the higher the initial PET image of the axial layer at the first iteration. The smaller the weight.

[0157] In the above manner, PET images of other axial layers after weighting in the first iteration can also be obtained.

[0158] If it is determined that the iteration end condition is satisfied according to the weighted PET image of each axial layer in the first iteration, the weighted PET image of each axial layer in the first iteration may be determined as the target PET image of each axial layer.

[0159] If it is determined that the iteration end condition is not met based on the weighted PET image of each axial layer in the first iteration, the weighted PET image of each axial layer in the first iteration can be determined as the initial PET image of each axial layer in the next iteration, and the next iteration is performed based on the initial PET image of each axial layer in the next iteration.

[0160] (2) If the current iteration is not the first iteration, the current iteration is recorded as the kth iteration, where k is a positive integer greater than or equal to 1, and the axial layer slice can be obtained. z_iThe weighted PET image at the k-1th iteration is used as the axial slice z_i The initial PET image at the kth iteration (which can be recorded as ); Based on the PET projection data Update to get the axial layer slice z_i The updated PET image at the kth iteration (which can be recorded as ).

[0161] According to the axial layer slice z_i The relaxation factor can be obtained by two weights; according to these two weights, the axial layer slice z_i Updated PET image at iteration k With axial layer slice z_i Initial PET image at iteration k Perform weighted summation to obtain the axial layer slice z_i The weighted PET image at the kth iteration. If the axial slice z_i The axial layer is within the set range, and the axial layer slice z_i The closer to the central axial layer, the smaller the axial layer slice z_i The larger the relaxation factor, the more z_i Updated PET image at iteration k The larger the weight, the more it is assigned to the axial layer slice. z_i Initial PET image at iteration k The smaller the weight.

[0162] In the above manner, weighted PET images of other axial layers at the kth iteration can also be obtained.

[0163] If it is determined that the iteration end condition is satisfied according to the weighted PET images of each axial layer at the kth iteration, the weighted PET images of each axial layer at the kth iteration may be determined as the target PET images of each axial layer.

[0164] If it is determined based on the weighted PET images of each axial layer at the kth iteration that the iteration termination condition is not met, the weighted PET images of each axial layer at the kth iteration can be determined as the initial PET images of each axial layer at the next iteration, and the next iteration can be performed based on the initial PET images of each axial layer at the next iteration.

[0165] In the above-mentioned PET image reconstruction method, PET projection data is acquired; the PET projection data is iteratively reconstructed at least once to obtain a target PET image of each axial layer; wherein, in the current iterative reconstruction, the initial PET image of each axial layer at the current iteration is updated according to the PET projection data to obtain an updated PET image of each axial layer at the current iteration; two weights obtained according to the relaxation factors of each axial layer are weighted summed up for the updated PET image of each axial layer at the current iteration and the initial PET image of each axial layer at the current iteration to obtain a weighted PET image of each axial layer at the current iteration; for any axial layer within a set range, the closer the axial layer is to the central axial layer, the greater the relaxation factor of the axial layer, and accordingly, the greater the weight assigned to the weighted PET image of the axial layer at each iteration, and the smaller the weight assigned to the initial PET image of the axial layer at each iteration, thereby suppressing the image noise level in the axial edge area during the iterative reconstruction process.

[0166] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0167] Based on the same inventive concept, the embodiment of the present application also provides a PET image reconstruction device for implementing the PET image reconstruction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations of the one or more PET image reconstruction device embodiments provided below can refer to the limitations of the PET image reconstruction method above, and will not be repeated here.

[0168] In one embodiment, Figure 5 As shown, a PET image reconstruction device is provided, comprising:

[0169] An updating module 501 is used to update the initial PET image of each axial layer according to the PET projection data to obtain an updated PET image of each axial layer;

[0170] A relaxation factor acquisition module 502 is used to acquire the relaxation factor of each axial layer;

[0171] A weighted sum processing module 503 is used for obtaining, for each of the axial layers, a first weight and a second weight of the axial layer according to the relaxation factor of the axial layer, and performing weighted summation on the updated PET image of the axial layer and the initial PET image of the axial layer according to the first weight and the second weight to obtain a weighted PET image of the axial layer;

[0172] A target image acquisition module 504 is configured to obtain a target PET image of each axial layer according to the weighted PET image of each axial layer if it is determined that an iteration end condition is met according to the weighted PET image of each axial layer;

[0173] Return to the execution module 505, which is used to determine the weighted PET image of each axial layer as the initial PET image of each axial layer if it is determined that the iteration end condition is not met according to the weighted PET image of each axial layer, and return to the update module, the relaxation factor acquisition module and the weighted summation processing module.

[0174] In one embodiment, the relaxation factor acquisition module 502 is further used to:

[0175] For each of the axial layers, the layer number difference between the axial layer and the central axial layer is obtained; if the layer number difference is greater than a layer number difference threshold, the relaxation factor of the axial layer is determined according to the layer number difference and a negative correlation function; the relaxation factor of the axial layer is less than the relaxation factor of the central axial layer.

[0176] In one embodiment, the relaxation factor acquisition module 502 is further used to:

[0177] If the layer number difference is less than or equal to the layer number difference threshold, the relaxation factor of the central axial layer is determined as the relaxation factor of the axial layer.

[0178] In one embodiment, the relaxation factor acquisition module 502 is further used to:

[0179] For each of the axial layers, a layer number difference between the axial layer and the central axial layer is obtained; and a relaxation factor of the axial layer is determined according to the layer number difference and a negative correlation function.

[0180] In one embodiment, the relaxation factor acquisition module 502 is further used to:

[0181] For each voxel, the detection probability of each response line for the voxel is obtained; the attenuation coefficient of each response line is obtained; for each axial layer, the relaxation factor of the axial layer is obtained according to the detection probability of each response line of each voxel on the axial layer and the attenuation coefficient of each response line.

[0182] In one embodiment, the relaxation factor acquisition module 502 is further used to:

[0183] For each voxel on the axial layer, the detection probability of each response line of the voxel and the attenuation coefficient of each response line are weighted and summed to obtain a weighted value of the detection probability of the voxel; and according to the weighted value of the detection probability of each voxel on the axial layer, the relaxation factor of the axial layer is obtained.

[0184] In one embodiment, the weighted sum processing module 503 is further used to:

[0185] According to the relaxation factor of the axial layer, the first weight of the axial layer is obtained; according to the difference between the set value and the relaxation factor of the axial layer, the second weight of the axial layer is obtained.

[0186] In one embodiment, the update module 501 is further configured to:

[0187] According to the initial PET image of each axial layer, first PET projection prediction data is obtained; according to the difference between the first PET projection prediction data and the PET projection data, the initial PET image of each axial layer is updated to obtain an updated PET image of each axial layer.

[0188] In one embodiment, the device further includes an iteration end judgment module, configured to:

[0189] According to the weighted PET images of each axial layer, second PET projection prediction data is obtained; and according to the difference between the second PET projection prediction data and the PET projection data, it is determined whether the iteration end condition is met.

[0190] Each module in the above-mentioned PET image reconstruction device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0191] Based on the same inventive concept, the embodiment of the present application also provides a PET image reconstruction device for implementing the PET image reconstruction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations of the one or more PET image reconstruction device embodiments provided below can refer to the limitations of the PET image reconstruction method above, and will not be repeated here.

[0192] In one embodiment, a PET image reconstruction device is provided, comprising:

[0193] A projection data acquisition module, used for acquiring PET projection data;

[0194] An iterative reconstruction module, used for iteratively reconstructing the PET projection data at least once to obtain a target PET image of each axial layer;

[0195] The iterative reconstruction module is also used to update the initial PET image of each axial layer at the current iteration according to the PET projection data during the current iterative reconstruction, so as to obtain the updated PET image of each axial layer at the current iteration; perform weighted summation on the updated PET image of each axial layer at the current iteration and the initial PET image of each axial layer at the current iteration according to two weights obtained from the relaxation factors of each axial layer, so as to obtain the weighted PET image of each axial layer at the current iteration; the closer the axial layer within the set range is to the central axial layer, the greater the relaxation factor of the axial layer, the greater the weight assigned to the updated PET image of the axial layer at the current iteration, and the smaller the weight assigned to the initial PET image of the axial layer at the current iteration.

[0196] Each module in the above-mentioned PET image reconstruction device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0197] In an exemplary embodiment, a computer device is provided, the internal structure of which can be shown as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data involved in the above method. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a PET image reconstruction method is implemented.

[0198] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0199] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned various method embodiments when executing the computer program.

[0200] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0201] In one embodiment, a computer program product is provided, on which a computer program is stored, and the computer program is used by a processor to execute the steps in the above-mentioned various method embodiments.

[0202] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0203] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0204] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0205] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for reconstructing a PET image, characterized in that: The method comprises: Step S1, updating the initial PET image of each axial layer according to the PET projection data to obtain an updated PET image of each axial layer; Step S2, obtaining the relaxation factor of each axial layer; Step S3, for each of the axial layers, obtaining a first weight and a second weight of the axial layer according to the relaxation factor of the axial layer, and performing weighted summation on the updated PET image of the axial layer and the initial PET image of the axial layer according to the first weight and the second weight to obtain a weighted PET image of the axial layer; Step S4, if it is determined that the iteration end condition is met according to the weighted PET image of each axial layer, a target PET image of each axial layer is obtained according to the weighted PET image of each axial layer; Step S5, if it is determined according to the weighted PET image of each axial layer that the iteration end condition is not satisfied, the weighted PET image of each axial layer is determined as the initial PET image of each axial layer, and the execution of steps S1 to S3 is returned.

2. The method according to claim 1, characterized in that Get the relaxation factor of each axial layer, including: For each of the axial layers, obtaining a layer number difference between the axial layer and the central axial layer; If the layer number difference is greater than the layer number difference threshold, the relaxation factor of the axial layer is determined according to the layer number difference and a negative correlation function; the relaxation factor of the axial layer is smaller than the relaxation factor of the central axial layer.

3. The method according to claim 2, characterized in that The method further comprises: If the layer number difference is less than or equal to the layer number difference threshold, the relaxation factor of the central axial layer is determined as the relaxation factor of the axial layer.

4. The method according to claim 1, characterized in that Get the relaxation factor of each axial layer, including: For each of the axial layers, obtaining a layer number difference between the axial layer and the central axial layer; The relaxation factor of the axial layer is determined according to the layer number difference and the negative correlation function.

5. The method according to claim 1, characterized in that: Get the relaxation factor of each axial layer, including: For each voxel, obtain the detection probability of each response line for the voxel; Get the attenuation coefficient of each response line; For each of the axial layers, the relaxation factor of the axial layer is obtained according to the detection probability of each response line of each voxel on the axial layer and the attenuation coefficient of each response line.

6. The method according to claim 5, characterized in that The relaxation factor of the axial layer is obtained according to the detection probability of each response line of each voxel on the axial layer and the attenuation coefficient of each response line, including: For each voxel on the axial layer, weighted summing the detection probability of each response line of the voxel and the attenuation coefficient of each response line is performed to obtain a weighted value of the detection probability of the voxel; The relaxation factor of the axial layer is obtained according to the detection probability weighted value of each voxel on the axial layer.

7. The method according to any one of claims 2 to 6, characterized in that: According to the relaxation factor of the axial layer, a first weight and a second weight of the axial layer are obtained, including: Obtaining a first weight of the axial layer according to a relaxation factor of the axial layer; The second weight of the axial layer is obtained according to the difference between the set value and the relaxation factor of the axial layer.

8. The method according to claim 1, characterized in that According to the PET projection data, the initial PET image of each axial layer is updated to obtain an updated PET image of each axial layer, including: Obtaining first PET projection prediction data according to the initial PET images of each axial layer; According to the difference between the first PET projection prediction data and the PET projection data, the initial PET image of each axial layer is updated to obtain an updated PET image of each axial layer.

9. The method according to claim 1, characterized in that: The method further comprises: Obtaining second PET projection prediction data according to the weighted PET images of each axial layer; Whether the iteration end condition is satisfied is determined according to the difference between the second PET projection prediction data and the PET projection data.

10. A method for reconstructing a PET image, characterized in that: The method comprises: Acquire PET projection data; Iteratively reconstruct the PET projection data at least once to obtain a target PET image of each axial layer; Among them, during the current iterative reconstruction, the initial PET image of each axial layer at the current iteration is updated according to the PET projection data to obtain the updated PET image of each axial layer at the current iteration; the updated PET image of each axial layer at the current iteration and the initial PET image of each axial layer at the current iteration are weighted summed according to the two weights obtained from the relaxation factors of each axial layer to obtain the weighted PET image of each axial layer at the current iteration; the closer the axial layer within the set range is to the central axial layer, the greater the relaxation factor of the axial layer, the greater the weight assigned to the updated PET image of the axial layer at the current iteration, and the smaller the weight assigned to the initial PET image of the axial layer at the current iteration.

Images