Image processing apparatus and image processing method

Abstract

To provide an image processing device that can perform three-dimensional forward projection calculation from CNN output images to calculated sinograms and that can easily create a three-dimensional tomographic image of a subject by performing CNN learning on the basis of evaluation results of errors between the calculated sinograms and actual measurement sinograms.SOLUTION: A radiation tomographic imaging system 1 comprises a radiation tomographic imaging device 2 and an image processing device 10. The image processing device 10 includes a sinogram creation section 11, a CNN processing section 12, a convolution integration section 13, a forward projection calculation section 14, and a CNN learning section 15. The forward projection calculation section 14 performs forward projection calculation of a three-dimensional output image 23 and creates calculated sinograms 241 to 24K formed of K-number of divided blocks. The CNN learning section 15 evaluates an error, for each of the K-number of blocks, between an actual measurement sinogram 21k and the calculated sinogram 24k and performs CNN learning on the basis of an error evaluation result for each of the K-number of blocks.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an apparatus and method for creating a three-dimensional tomographic image of a subject based on simultaneous counting information collected by a tomography apparatus. [Background technology]

[0002] Examples of radiographic tomography devices that can acquire cross-sectional images of a subject (living organism) include PET (Positron Emission Tomography) devices and SPECT (Single Photon Emission Computed Tomography) devices.

[0003] A PET scanner has a detection unit equipped with numerous small radiation detectors arranged around the measurement space where the subject is placed. The PET scanner detects photon pairs with an energy of 511 keV generated by electron-positron annihilation in a subject administered with a positron-emitting isotope (RI source) using the coincidence counting method, and collects this coincidence counting information. Based on this collected information, a tomographic image representing the spatial distribution of the frequency of photon pair generation in the measurement space (i.e., the spatial distribution of the RI source) can be reconstructed. This PET scanner plays an important role in fields such as nuclear medicine, and can be used to study, for example, biological functions and higher brain functions.

[0004] Various methods are known for reconstructing tomographic images of a subject based on a large amount of collected simultaneous counting information. The image processing method for tomographic image reconstruction described in Non-Patent Literature 1 reconstructs tomographic images using Deep Image Prior technology, which utilizes a convolutional neural network, a type of deep neural network. Hereafter, Convolutional Neural Network will be referred to as "CNN," and Deep Image Prior technology will be referred to as "DIP technology." DIP technology utilizes the property of CNNs that meaningful structures in an image are learned faster than random noise (i.e., random noise is difficult to learn). DIP technology makes it possible to obtain tomographic images with reduced noise.

[0005] The image processing method described in Non-Patent Document 1 is specifically as follows: A sinogram (hereinafter referred to as "measured sinogram") is created based on a large amount of simultaneous counting information collected for the subject. In addition, when an input image (e.g., an MRI image) is input to a CNN, the image output from the CNN is subjected to forward projection calculation (Radon transform) to create a sinogram (hereinafter referred to as "computed sinogram"). The error between this computed sinogram and the measured sinogram is then evaluated, and the CNN is trained based on this error evaluation result. By repeating the image output from the CNN, the creation of a computed sinogram by forward projection calculation, the error evaluation, and the training of the CNN using DIP technology, the computed sinogram gradually approaches the measured sinogram, and the output image from the CNN approaches the tomographic image of the subject.

[0006] This image processing method includes forward projection from the CNN output image to the computed sinogram, but does not include back projection from the measured sinogram to the tomographic image. Therefore, it is possible to obtain tomographic images with reduced noise.

[0007] A sinogram is a histogram representing the frequency of simultaneous counting information acquisition (frequency of simultaneous counting events) in a space (sinogram space) represented by four variables r, θ, z, and δ. The variable r represents the distance from the central axis to the simultaneous counting line (the line connecting two detectors that simultaneously counted photon pairs). The variable θ represents the azimuth angle of the simultaneous counting line. The variable z represents the position of the midpoint of the simultaneous counting line along the central axis. The variable δ represents the distance along the central axis between two detectors that simultaneously counted photon pairs. [Prior art documents] [Non-patent literature]

[0008] [Non-Patent Document 1] F. Hashimoto, K. Ote and Y. Onishi, "PET Image Reconstruction Incorporating Deep Image Prior and a ForwardProjection Model," IEEE Transactions on Radiation and Plasma Medical Sciences, doi: 10.1109 / TRPMS.2022.3161569. [Overview of the Initiative] [Problems that the invention aims to solve]

[0009] Generally, CNN-based processing utilizes a GPU (Graphics Processing Unit). A GPU is a computational processing unit specialized for image processing, and it has an integrated computing unit and RAM (Random Access Memory) on a single semiconductor chip. Various types of data used during computation by the GPU's computing unit are required to be stored in the GPU's RAM.

[0010] In the image processing method described in Non-Patent Document 1, the data to be stored in the GPU's RAM includes, for example, CNN input images, CNN output images, weight coefficients representing the CNN's learning state, feature maps, measured sinograms, computed sinograms, and parameters necessary for forward projection calculations, requiring a massive amount of memory capacity.

[0011] However, due to the limited capacity of GPU RAM, the image processing method described in Non-Patent Document 1 can perform 2D forward projection calculations, but it cannot perform 3D forward projection calculations.

[0012] The present invention was made to solve the above problems, and aims to provide an image processing device and image processing method that enables 3D forward projection calculation from a CNN output image to a computed sinogram, and allows the CNN to be trained based on the evaluation result of the error between the computed sinogram and the measured sinogram to easily create a 3D tomographic image of a subject. [Means for solving the problem]

[0013] A first aspect of the image processing apparatus of the present invention is an image processing apparatus that creates a three-dimensional tomographic image of a subject based on simultaneous counting information collected by a radiation tomography apparatus having a plurality of detectors arranged to surround a measurement space in which the subject administered with RI rays is placed, comprising: (1) a sinogram creation unit that creates a sinogram divided into a plurality of blocks based on the simultaneous counting information collected by the radiation tomography apparatus; (2) a CNN processing unit that inputs a three-dimensional input image into a convolutional neural network and creates a three-dimensional output image by the convolutional neural network; (3) a forward projection calculation unit that performs a forward projection calculation on the three-dimensional output image to create a sinogram divided into a plurality of blocks; and (4) a CNN learning unit that evaluates an error between the sinogram created by the sinogram creation unit and the sinogram created by the forward projection calculation unit for each of the plurality of blocks, and learns the convolutional neural network based on the error evaluation result for each of the plurality of blocks. Then, the three-dimensional output image after repeatedly performing the processes of the CNN processing unit, the forward projection calculation unit, and the CNN learning unit a plurality of times is used as the three-dimensional tomographic image of the subject.

[0014] The image processing apparatus of the present invention may also be in the following aspects. In a second aspect, in addition to the first aspect, the image processing apparatus further comprises a convolution integration unit that performs a convolution integral of a point spread function on the three-dimensional output image, and it is preferable that the forward projection calculation unit performs a forward projection calculation on the three-dimensional output image after the processing by the convolution integration unit. In a third aspect, in addition to the first or second aspect, it is preferable that the CNN learning unit evaluates an error in a region in the sinogram space where simultaneous counting information collection by the radiation tomography apparatus is possible. In a fourth aspect, in addition to any one of the first to third aspects, the CNN processing unit may input an image representing the morphological information of the subject, an MRI image of the subject, a CT image of the subject ,Ma or a random noise image into the convolutional neural network as the three-dimensional input image.

[0015] The radiation tomography system of the present invention includes a radiation tomography apparatus having a plurality of detectors arranged to surround a measurement space in which a subject administered with a RI source is placed, and which collects coincidence counting information, and the image processing apparatus of the present invention described above that creates a three-dimensional tomographic image of the subject based on the coincidence counting information collected by the radiation tomography apparatus.

[0016] A first aspect of the image processing method of the present invention is an image processing method for creating a three-dimensional tomographic image of a subject based on coincidence counting information collected by a radiation tomography apparatus having a plurality of detectors arranged to surround a measurement space in which a subject administered with a RI source is placed, the method comprising: (1) a sinogram creation step of creating a sinogram divided into a plurality of blocks based on the coincidence counting information collected by the radiation tomography apparatus; (2) a CNN processing step of inputting a three-dimensional input image into a convolutional neural network and creating a three-dimensional output image by the convolutional neural network; (3) a forward projection calculation step of performing a forward projection calculation on the three-dimensional output image to create a sinogram divided into a plurality of blocks; and (4) a CNN learning step of evaluating an error between the sinogram created in the sinogram creation step and the sinogram created in the forward projection calculation step for each of the plurality of blocks, and training the convolutional neural network based on the error evaluation result for each of the plurality of blocks. Then, the three-dimensional output image after repeatedly performing the processes of the CNN processing step, the forward projection calculation step, and the CNN learning step a plurality of times is used as the three-dimensional tomographic image of the subject.

[0017] The image processing method of the present invention may be in the following aspect. In a second aspect, in addition to the first aspect, the image processing method further includes a convolution integration step of performing a convolution integration of a point spread function on the three-dimensional output image, and in the forward projection calculation step, it is preferable to perform a forward projection calculation on the three-dimensional output image after the processing by the convolution integration step. In the third embodiment, in addition to the first or second embodiment, it is preferable to evaluate the error in the region of the sinogram space where simultaneous counting information can be collected by a tomography device during the CNN learning step. In the fourth embodiment, in addition to any of the first to third embodiments, the CNN processing step includes an image representing the morphological information of the subject, an MRI image of the subject, and a CT image of the subject. ,Ma Alternatively, random noise images may be used as 3D input images for the convolutional neural network. [Effects of the Invention]

[0018] According to the present invention, it is possible to perform a three-dimensional forward projection calculation from a CNN output image to a computed sinogram, and to easily create a three-dimensional tomographic image of a subject by training the CNN based on the evaluation result of the error between the computed sinogram and the measured sinogram. [Brief explanation of the drawing]

[0019] [Figure 1] Figure 1 shows the configuration of the radiographic tomography system 1. [Figure 2] Figure 2 shows an example of a CNN configuration. [Figure 3] Figure 3 is a flowchart of the image processing method. [Figure 4] Figure 4 is a diagram comparing the calculation sinogram 24 in the comparative example and the calculation sinograms 241 to 2416 in this embodiment. Figure 4(a) schematically shows the calculation sinogram 24 in the comparative example. Figure 4(b) schematically shows the calculation sinograms 241 to 2416 in this embodiment. [Figure 5] Figure 5 shows cross-sectional images of the brain obtained from the simulation. [Figure 6] Figure 6 shows cross-sectional images of the brain obtained from the simulation. [Figure 7] Figure 7 shows cross-sectional images of the brain obtained from the simulation. [Figure 8] Figure 8 shows cross-sectional images of the brain obtained from the simulation. [Modes for carrying out the invention]

[0020] Hereinafter, embodiments for carrying out the present invention will be described in detail with reference to the attached drawings. In the description of the drawings, the same elements will be denoted by the same reference numerals, and redundant descriptions will be omitted. The present invention is not limited to these examples, but is indicated by the claims, and all modifications within the meaning and scope equivalent to the claims are intended to be included.

[0021] Figure 1 shows the configuration of the radiometric tomography system 1. The radiometric tomography system 1 comprises a radiometric tomography device 2 and an image processing device 10. The image processing device 10 comprises a sinogram creation unit 11, a CNN processing unit 12, a convolution integration unit 13, a forward projection calculation unit 14, and a CNN learning unit 15.

[0022] The tomography system 2 is a device that collects coincidence count information for reconstructing tomographic images of a subject. Examples of tomography systems 2 include PET scanners and SPECT scanners. In the following explanation, we will assume that the tomography system 2 is a PET scanner.

[0023] The tomography apparatus 2 is equipped with a detection unit having a number of small radiation detectors arranged around the measurement space where the subject is placed. The tomography apparatus 2 detects photon pairs with an energy of 511 keV generated by electron-positron annihilation in the subject to which the RI source has been administered, using the coincidence counting method, and collects this coincidence counting information. The tomography apparatus 2 then outputs this collected coincidence counting information to the image processing device 10.

[0024] The image processing device 10 includes a GPU that performs processing using a CNN, an input unit that accepts user input (e.g., a keyboard or mouse), a display unit that displays images (e.g., a liquid crystal display), and a storage unit that stores programs and data for executing various processes. A computer having a CPU, RAM, ROM, and a hard disk drive is used as the image processing device 10.

[0025] The sinogram creation unit 11 creates a measured sinogram 21 based on the synchronization counting information collected by the tomography device 2. At this time, the sinogram creation unit 11 divides the measured sinogram 211 to 21 into multiple (K) blocks. K Create the actual sinogram 21. k This is the measured sinogram of the k-th block out of K blocks. K is an integer greater than or equal to 2, and k is an integer between 1 and K (inclusive). Divided measured sinograms 211-21 K The combined result is the overall measured sinogram 21.

[0026] The CNN processing unit 12 inputs a 3D input image 20 to the CNN and uses the CNN to create a 3D output image 22. The 3D input image 20 may be an image representing the morphological information of a subject, an MRI image, CT image, or static PET image of the subject, or a random noise image.

[0027] The convolution integral unit 13 performs a convolution integral of the point spread function on the 3D output image 22 created by the CNN processing unit 12 to create a new 3D output image 23. The point spread function (PSF) is a function that represents the response (impulse response) of a tomography device to a point source, and is generally represented by a Gaussian function, or an asymmetric Gaussian function modeled from measured data of the point source that exhibits different blurring characteristics depending on its position within the field of view. The inclusion of the convolution integral unit 13 allows for obtaining tomographic images with better image quality and also stabilizes the learning of the CNN.

[0028] The forward projection calculation unit 14 performs forward projection calculation on the three-dimensional output image 23 to create a calculated sinogram 24. At this time, the forward projection calculation unit 14 divides the calculated sinogram 24 into K blocks 241 to 24 K to create. The calculated sinogram 24 k is the calculated sinogram of the k-th block among the K blocks. The combined calculated sinograms 241 to 24 K is the overall calculated sinogram 24.

[0029] The block division of the calculated sinogram 24 is performed in the same manner as the block division of the measured sinogram 21. The calculated sinogram 24 of the k-th block k and the measured sinogram 21 of the k-th block k are sinograms of a common region in the overall sinogram space. The mode of block division is arbitrary, and block division may be performed on any one or two or more of the four variables representing the sinogram space. The sizes of each of the K blocks may be different or the same.

[0030] The CNN learning unit 15 evaluates the error between the measured sinogram 21 k and the calculated sinogram 24 k for each of the K blocks, and learns the CNN based on the error evaluation results for each of the K blocks.

[0031] After repeatedly performing the processes of the CNN processing unit 12, the convolution integration unit 13, the forward projection calculation unit 14, and the CNN learning unit 15 a plurality of times, the three-dimensional output image 22 created by the CNN processing unit 12 is used as the three-dimensional tomographic image of the subject. The three-dimensional output image 23 created by the convolution integration unit 13 may also be used as the three-dimensional tomographic image of the subject. Since the measured sinogram 21 reflects the response function of the radiation tomography apparatus, it is preferable to use the three-dimensional output image 22 before the convolution integration of the point spread function by the convolution integration unit 13 as the three-dimensional tomographic image of the subject.

[0032] The convolution integral unit 13 may be provided as the final layer of the CNN, or it may be provided separately from the CNN. If the convolution integral unit 13 is provided as the final layer of the CNN, the weight coefficients of the convolution integral unit 13 are kept constant during CNN training. Alternatively, the convolution integral unit 13 may not be provided. If the convolution integral unit 13 is not provided, the forward projection calculation unit 14 performs a forward projection calculation on the 3D output image 22 output from the CNN processing unit 12 to create a computational sinogram 24.

[0033] Figure 2 shows an example of a CNN configuration. The CNN shown in this figure has a 3D U-net structure that includes an encoder and a decoder. In this figure, the size of each layer of the CNN is shown, with the number of pixels in the 3D input image 20 input to the CNN being N×N×64.

[0034] Figure 3 is a flowchart of the image processing method. The image processing method comprises a sinogram creation step S1 performed by a sinogram creation unit 11, a CNN processing step S2 performed by a CNN processing unit 12, a convolution integration step S3 performed by a convolution integration unit 13, a forward projection calculation step S4 performed by a forward projection calculation unit 14, and a CNN learning step S5 performed by a CNN learning unit 15.

[0035] In the sinogram creation step S1, based on the synchronization counting information collected by the tomography device 2, the measured sinograms 211-21 are divided into K blocks. K The CNN is created. In the CNN processing step S2, the CNN is input with the 3D input image 20, and the CNN creates the 3D output image 22. In the convolution integral step S3, the CNN is subjected to a convolution integral of the point image distribution function on the 3D output image 22 created in the CNN processing step S2 to create a new 3D output image 23.

[0036] In the forward projection calculation step S4, the 3D output image 23 is subjected to forward projection calculation and divided into K blocks, resulting in the computational sinograms 241-24. KCreate the following: In CNN training step S5, for each of the K blocks, the measured sinogram 21 k and calculation sinogram 24 k The error between the given values ​​is evaluated, and the CNN is trained based on the error evaluation results for each of the K blocks.

[0037] After repeating the CNN processing step S2, the convolution and integration step S3, the forward projection calculation step S4, and the CNN learning step S5 multiple times, the 3D output image 22 created in the CNN processing step S2 is used as the 3D tomographic image of the subject. Alternatively, the 3D output image 23 created in the convolution and integration step S3 may be used as the 3D tomographic image of the subject. Note that the convolution and integration step S3 is optional.

[0038] Next, prior to a detailed explanation of the processing content of each step in the image processing method of this embodiment, the processing content of each step in the image processing method of the comparative example will be described. In the image processing method of the comparative example, the measured sinogram and the calculated sinogram are not divided into multiple blocks.

[0039] In the following, let f be the CNN processing, z be the 3D input image 20 input to the CNN, and θ be the weight coefficient parameter representing the CNN's learning state. θ changes as the CNN's learning progresses. Let x be the 3D output image 22 output from the CNN when the 3D input image z is input to a CNN with weight coefficients of θ. The 3D output image x is expressed by (1) below. In the CNN processing step, the 3D output image x is created by performing the processing represented by this equation.

[0040]

number

[0041] In the convolution integral step, a new 3D output image x is created by performing a convolution integral of the point image distribution function on the 3D output image x created in the CNN processing step. In Figure 1, the 3D output image x after the convolution integral is denoted as PSF(f(θ|z)).

[0042] In the forward projection calculation step, the 3D output image x is forward projected to create a computed sinogram 24. Let the computed sinogram 24 be y, and let P be the projection matrix used to perform the forward projection calculation (Radon transform) from the 3D output image x to the computed sinogram y. The projection matrix is ​​also called the system matrix or detection probability matrix. The process performed in the forward projection calculation step is expressed by equation (2) below.

[0043]

number

[0044] In the CNN training step, the measured sinogram 21 is taken as y0, and the error between the measured sinogram y0 and the calculated sinogram y (equation (2) above) is evaluated, and the CNN is trained based on the result of this error evaluation. The processing performed in the CNN training step is expressed by equation (3) below. The constrained optimization problem in this equation is the problem of optimizing the CNN parameters θ so that the value of the error evaluation function E(y;y0) is small, under the constraint that the 3D output image x created by the CNN is a tomographic image of the subject.

[0045]

number

[0046] The constrained optimization problem in equation (3) can be transformed into the unconstrained optimization problem in equation (4) below. The error evaluation function E can be arbitrary, but for example, the L1 norm, the L2 norm, or the negative log-likelihood in the Poisson distribution can be used. If the L2 norm is used as the error evaluation function, equation (4) can be transformed into equation (5) below.

[0047]

number

[0048]

number

[0049] Considering the arrangement of multiple detectors in a tomography system, there may be regions in the sinogram space where it is impossible to collect coincidence information. For this reason, the optimization problem in equation (5) above may be replaced with the optimization problem in equation (6) below. In equation (6), m is a binary mask function, which has a value of 1 in regions of the sinogram space where it is possible to collect coincidence information, and a value of 0 in regions where it is impossible. Equation (6) selectively evaluates the error in regions of the sinogram space where it is possible to collect coincidence information by taking the Hadamard product of the error (y-y0) and the binary mask function m.

[0050]

number

[0051] By repeatedly performing each of the CNN processing steps—the convolution integration step, the forward projection calculation step, and the CNN learning step—and solving this optimization problem for the CNN parameter θ, the computed sinogram y approaches the measured sinogram y0, and the 3D output image x created by the CNN approaches the tomographic image of the subject.

[0052] Next, the processing details of each step of the image processing method of this embodiment will be described in detail. In this embodiment, in the forward projection calculation step, the 3D output image x is subjected to forward projection calculation and divided into K blocks of computational sinograms 241-24 K Create the kth block's computational sinogram 24. k to y k Then, the sinogram y is calculated from the 3D output image x.k The projection matrix P is used to perform a forward projection calculation (Radon transform) onto the matrix. k The process performed in the forward projection calculation step is expressed by equation (7) below.

[0053]

number

[0054] In the CNN training step, the measured sinogram 21 of block k k to y 0k For each of the K blocks, the measured sinogram y 0k and calculate sinogram y k The error between and is evaluated, and the CNN is trained based on the error evaluation results for each of the K blocks. The process performed in the CNN training step is represented by the unconstrained optimization problem in equation (8) below. If the L2 norm is used as the error evaluation function, equation (8) can be transformed into equation (9) below. Furthermore, if the error is selectively evaluated in a region of the sinogram space where coincidence information can be collected, it can be represented by the unconstrained optimization problem in equation (10) below. m k This is the binary mask function in the k-th block.

[0055]

number

[0056]

number

[0057]

number

[0058] By repeatedly performing the CNN processing step, convolution integration step, forward projection calculation step, and CNN learning step multiple times, and solving this optimization problem for the CNN parameter θ, a computed sinogram y is obtained for each of the K blocks. k This is the measured sinogram y 0k As it approaches the subject, the 3D output image x created by the CNN approaches the tomographic image of the subject.

[0059] Next, a comparison between the memory capacity required to store data in the GPU's RAM and this embodiment will be described. Here, the number of pixels in the 3D output image created by the CNN is set to 128 × 128 × 64, and the number of pixels in the sinogram space is set to 128 × 128 × 64 × 19. In the image processing method of this embodiment, with K=16, the 3D output image is forward-projected and the calculated sinograms 241-24 are divided into 16 equally divided blocks. 16 The following will be created. Figure 4 shows the calculated sinogram 24 for the comparative example and the calculated sinograms 241-24 for this embodiment. 16 This figure shows a comparison of each example. Figure 4(a) schematically shows the calculation sinogram 24 in the comparative example. Figure 4(b) shows the calculation sinograms 241-24 in this embodiment. 16 This is illustrated schematically.

[0060] Calculation sinogram 24 of each block in this embodiment k The number of pixels is 128 × 8 × 64 × 19, which is 1 / 16 of the number of pixels of the calculated sinogram 24 in the comparative example. Also, in this embodiment, the calculated sinogram 24 of the kth block is obtained from the 3D output image. k Projection matrix P for performing forward projection calculations. k The number of elements is 1 / 16 of the number of elements in the projection matrix P used to perform the forward projection calculation from the 3D output image to the computed sinogram 24 in the comparative example.

[0061] In this embodiment, the storage capacity required to store the data used in forward projection calculations can be reduced compared to the comparative example, and this data can be stored in the GPU's RAM. Therefore, in this embodiment, it is possible to perform 3D forward projection calculations from CNN output images to computed sinograms, and to easily create 3D tomographic images of the subject by training the CNN based on the evaluation results of the error between the computed sinogram and the measured sinogram.

[0062] Next, simulation data was created using a Monte Carlo simulation of a head PET scanner with digital brain phantom images, and the results of obtaining tomographic images using the image processing method and the ML-EM method of this embodiment will be explained. The phantom images were obtained from BrainWeb (https: / / brainweb.bic.mni.mcgill.ca / brainweb / ). The ML-EM (Maximum Likelihood Expectation Maximization) method is a common image reconstruction method.

[0063] In the image processing method of this embodiment, the 3D input image input to the CNN is a random noise image, the number of pixels in the input image is set to 128 × 128 × 64, the number of pixels in the 3D output image created by the CNN is set to 128 × 128 × 64, the number of pixels in the sinogram space is set to 128 × 128 × 64 × 19, the sinogram space is equally divided into 16 blocks, and the error evaluation function is the negative log-likelihood in a Poisson distribution. In the image processing method of this embodiment, the number of iterations is set to 2000, while in the ML-EM method, the number of iterations is set to 50.

[0064] Figures 5 to 8 show tomographic images of the brain obtained from the simulation. These figures show tomographic images of cross-sections at four different positions in the axial direction of the body within the three-dimensional tomographic image. In these figures, (a) shows a phantom image (ground truth image), (b) shows a tomographic image obtained by the ML-EM method, and (c) shows a tomographic image obtained by the image processing method of this embodiment. The image processing method of this embodiment was able to obtain tomographic images with significantly better image quality compared to the tomographic images obtained by the ML-EM method. Furthermore, this simulation confirmed that in this embodiment, it is possible to perform a three-dimensional forward projection calculation from the CNN output image to a computed sinogram using a GPU, and that it is possible to create a three-dimensional tomographic image of the subject by training the CNN based on the evaluation result of the error between the computed sinogram and the measured sinogram. [Explanation of Symbols]

[0065] 1...Radiation tomography system, 2...Radiation tomography device, 10...Image processing device, 11...Sinogram creation unit, 12...CNN processing unit, 13...Convolution integration unit, 14...Forward projection calculation unit, 15...CNN learning unit.

Claims

1. An image processing apparatus that creates a three-dimensional tomographic image of a subject based on simultaneous counting information collected by a tomography apparatus having multiple detectors arranged around a measurement space in which a subject administered an RI source is placed, A sinogram creation unit that creates a sinogram divided into multiple blocks based on the same-count information collected by the aforementioned tomography device, A CNN processing unit that takes a 3D input image as input to a convolutional neural network and creates a 3D output image using the said convolutional neural network, A forward projection calculation unit that performs forward projection calculations on the three-dimensional output image to create a sinogram divided into multiple blocks, A CNN learning unit evaluates the error between the sinogram created by the sinogram creation unit and the sinogram created by the forward projection calculation unit for each of the plurality of blocks, and trains the convolutional neural network based on the error evaluation results for each of the plurality of blocks. Equipped with, The 3D output image after repeatedly performing the processing of the CNN processing unit, the forward projection calculation unit, and the CNN learning unit is defined as the 3D tomographic image of the subject. Image processing device.

2. The system further includes a convolution integral unit that performs a convolution integral of the point image distribution function on the aforementioned three-dimensional output image. The forward projection calculation unit performs forward projection calculation on the 3D output image after processing by the convolution integration unit. The image processing apparatus according to claim 1.

3. The CNN learning unit evaluates the error in a region of the sinogram space where simultaneous counting information can be collected by the tomography device. The image processing apparatus according to claim 1.

4. The CNN processing unit inputs an image representing the morphological information of the subject as the three-dimensional input image to the convolutional neural network. The image processing apparatus according to claim 1.

5. The CNN processing unit inputs the MRI image of the subject as the three-dimensional input image to the convolutional neural network. The image processing apparatus according to claim 1.

6. The CNN processing unit inputs the CT image of the subject as the three-dimensional input image to the convolutional neural network. The image processing apparatus according to claim 1.

7. The CNN processing unit inputs the random noise image as the three-dimensional input image to the convolutional neural network. The image processing apparatus according to claim 1.

8. A tomography system that collects simultaneous counting information and has multiple detectors arranged around a measurement space in which a subject administered with an RI source is placed, An image processing apparatus according to any one of claims 1 to 7, which creates a three-dimensional tomographic image of the subject based on the same-count information collected by the radiographic tomography apparatus, A radiographic tomography system equipped with [specific features / equipment].

9. An image processing method for creating a three-dimensional tomographic image of a subject based on simultaneous counting information collected by a tomography apparatus having multiple detectors arranged around a measurement space in which a subject administered an RI source is placed, A sinogram creation step in which a sinogram divided into multiple blocks is created based on the same count information collected by the aforementioned tomography device, A CNN processing step involves inputting a 3D input image into a convolutional neural network and creating a 3D output image using the convolutional neural network. A forward projection calculation step involves performing a forward projection calculation on the three-dimensional output image to create a sinogram divided into multiple blocks, A CNN learning step which evaluates the error between the sinogram created in the sinogram creation step and the sinogram created in the forward projection calculation step for each of the plurality of blocks, and trains the convolutional neural network based on the error evaluation results for each of the plurality of blocks, Equipped with, The 3D output image obtained after repeatedly performing each of the above CNN processing steps, the forward projection calculation step, and the CNN learning step is defined as the 3D tomographic image of the subject. Image processing methods.

10. The system further includes a convolution integral step in which a convolution integral of the point image distribution function is performed on the aforementioned three-dimensional output image. In the forward projection calculation step, the 3D output image after processing by the convolution integration step is subjected to forward projection calculation. The image processing method according to claim 9.

11. In the CNN learning step, the error is evaluated in a region of the sinogram space where simultaneous counting information can be collected by the tomography device. The image processing method according to claim 9.

12. In the CNN processing step, an image representing the morphological information of the subject is input to the convolutional neural network as the three-dimensional input image. The image processing method according to claim 9.

13. In the CNN processing step, the MRI image of the subject is input to the convolutional neural network as the three-dimensional input image. The image processing method according to claim 9.

14. In the CNN processing step, the CT image of the subject is input to the convolutional neural network as the three-dimensional input image. The image processing method according to claim 9.

15. In the CNN processing step, a random noise image is input to the convolutional neural network as the three-dimensional input image. The image processing method according to claim 9.

Images