CT (computed tomography) guidance-based elderly brain PET (positron emission tomography) image space standardization system, method and device

Abstract

The invention discloses an elderly brain PET image space standardization system, method and device based on CT guidance, and the system comprises a PET / CT image collection and format conversion module, a PET / CT preprocessing module, a PET / CT image coarse standardization module, a PET / CT image fine standardization module and an automatic SUV extraction module. And carrying out senile brain PET molecular image space standardization, and automatically extracting the SUV of a region of interest by adopting brain map partitioning of a standard space. According to the method, a coarse-to-fine two-step strategy is adopted, an accurate, stable and user-friendly space standardization system is provided for the elderly brain PET image through an optimized process, and the method has important clinical application value and scientific research significance.

Description

Technical Field

[0001] The present invention relates to the field of PET image registration, and in particular to a system, method and device for spatial standardization of elderly brain PET images based on CT guidance. Background Art

[0002] Positron emission tomography (PET) is a cutting-edge molecular imaging technology that uses radioactive isotope-labeled tracers to visualize the distribution of specific molecules in vivo and quantitatively reflect physiological and pathological processes. PET technology plays an important role in the scientific research and clinical diagnosis of common brain diseases in the elderly, such as Alzheimer's disease and Parkinson's disease. However, current PET imaging diagnosis mainly relies on the doctor's visual assessment, which is not only highly subjective, but also prone to missed diagnosis and misdiagnosis.

[0003] In order to overcome these limitations, it is crucial to automatically extract the standard uptake value (SUV) or standard uptake value ratio (SUVR) of the brain region. This not only helps doctors make objective diagnoses and reduce reliance on their subjective experience, but also allows for the exploration of physiological and pathological changes in different brain regions in scientific research. The key to achieving this goal is the spatial standardization of PET images, that is, registering individual images to the standard brain template space.

[0004] At present, there are two main methods for spatial standardization of PET images: spatial standardization assisted by brain structural images and spatial standardization based on PET brain templates. Brain structural images include magnetic resonance imaging (MRI) structural images (such as T1-weighted images and T2-weighted images) and computed tomography (CT) structural images. The method assisted by brain structural images is usually divided into three steps: first, rigidly register individual PET images to individual structural brain images; second, nonlinearly register individual structural brain images to standard brain structural templates; finally, apply the nonlinear deformation field generated by the previous step to register PET images to the standard space. The method based on PET brain templates includes constructing a PET standard brain template for a specific group, and then nonlinearly registering individual PET images to the template.

[0005] The precise brain anatomical information provided by high-resolution MRI is considered the gold standard for PET standardization. Although the PET brain template-based method is fast in calculation, it requires the construction of a PET standard brain template for a specific population and may produce large registration errors when processing PET images with great variability. On the other hand, the PET standardization method assisted by CT brain structural imaging usually requires further optimization of the method to approach the effect of MRI due to the resolution limitation of CT images on brain anatomical structures.

[0006] A study has proposed a brain PET standardization method based on low-dose CT (source: Presotto, Luca et al. "Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification." NeuroImage. Clinical vol. 20153-160.19Jul.2018, doi: 10.1016 / j.nicl.2018.07.013). This method follows the idea of ​​structure-based registration methods and achieves spatial normalization of PET images registered with individual CT by finding the optimal transformation of low-dose CT images to standard space. This method improves the standardization effect of low-dose CT images through a "cleaning" procedure and an optimized SPM12 (Statistical Parametric Mapping 12) segmentation algorithm. Specifically, this method first performs a "cleaning" procedure to set all values ​​below -300HU to -1024HU to avoid the low-density structure outside the head affecting the algorithm, and the preprocessed CT image is loaded into the optimized SPM12 segmentation algorithm. The optimized parameters in SPM12 segmentation include disabling bias field correction, using only one Gaussian distribution for the Gaussian mixture model of gray matter and white matter, and using two Gaussian distributions for the other four types of tissue (cerebrospinal fluid, bone, external tissue, and air). The spatially standardized deformation field is generated through CT tissue segmentation, and the deformation field is applied to the PET registered with CT, thus achieving spatial standardization of PET. However, when this method is used to process PET images of elderly patients, due to the use of the tissue probability map (TPM) of healthy young people, the registration accuracy may decrease when facing elderly patients with obvious brain atrophy. In addition, directly performing tissue segmentation on CT in individual space to obtain a standardized deformation field may also produce unstable results.

[0007] In view of this, in PET / CT scanning systems, for elderly brain patients who only have low-dose CT structural images, it is particularly urgent to develop an accurate and stable spatial normalization method for elderly brain PET images. This method uses low-dose CT images, which not only avoids the need for additional MRI scans, but also improves the accuracy and stability of the standardization process, which is of great significance to both clinical practice and scientific research.

[0008] There are several limitations in existing PET standardization techniques, which are particularly evident in clinical applications. First, the standardization method based on MRI structural brain images requires patients to undergo additional high-quality MRI scans in a short period of time. This not only increases the economic burden on patients, but also because PET and MRI scans are usually not performed on the same device in the absence of PET / MRI equipment, this non-integrated scanning method may reduce the accuracy of the registration of PET images with individual MRI images, thereby affecting the reliability of the standardization results. In addition, PET images without matching high-resolution MRI images are often encountered in clinical practice, which limits the spatial standardization process assisted by MRI structural images.

[0009] The standardization method based on PET brain template requires a specific PET standard brain template. This process requires the collection of a large number of PET images of the same probe to construct a PET brain template for a specific group, which is often difficult to achieve. In addition, since the PET images of the same probe of patients with different disease states are significantly different, there may be large differences between individual PET images and the constructed standard brain template, which limits the accuracy and stability of this method in spatial standardization.

[0010] The low-dose CT-assisted PET registration method proposed in previous studies has proved that low-dose CT can also assist in the spatial standardization of PET, but it still has its limitations. In particular, this method uses a tissue probability map (TPM) template based on healthy young people. This template space is very different from the brain structure of elderly patients with obvious cerebral lobe atrophy, which easily leads to a decrease in registration accuracy and is not suitable for processing images of such elderly patients. In addition, the one-step spatial standardization method that directly performs tissue segmentation and registration on CT images in individual space is prone to fall into a local optimal state when the algorithm is iteratively optimized to find the optimal registration nonlinear deformation field, resulting in the failure of spatial standardization, thereby affecting the consistency and reliability of the PET image standardization process. Summary of the invention

[0011] The purpose of the present invention is to propose a CT-guided spatial standardization system and method for elderly brain PET images in view of the deficiencies of the prior art. The present invention is based on a low-dose CT brain structure map obtained on the same bed in a PET / CT scanning system, and realizes accurate and stable spatial standardization of elderly brain PET images with ventricular enlargement and cerebral lobe atrophy. The technology does not require MRI, so for scanning and obtaining PET images on a PET / CT instrument, there is no need to perform MRI scanning to assist in annotation. The technology also does not require the construction of a population-specific PET brain template, thereby overcoming the problem of insufficient specificity of the PET brain template constructed in the case of a small case volume. The low-dose CT image used in the technology can be completed in the same bed as the PET image in the PET / CT scanning system, ensuring the accuracy of the initial individual space PET and CT image registration. By adopting an improved standardization method, the present invention effectively overcomes the spatial standardization accuracy and stability problems caused by insufficient brain tissue contrast in CT images. Especially for elderly patients, the technology can adapt to the special conditions of ventricular enlargement and cerebral lobe atrophy, providing a more accurate and stable PET image spatial standardization solution.

[0012] In summary, the present invention aims to solve the following technical problems:

[0013] 1. Provide accurate and stable spatial normalization for PET images of the elderly brain with ventriculomegaly and lobar atrophy.

[0014] 2. Use low-dose CT images to guide PET image standardization, avoid the need for additional MRI scans, and reduce the burden on patients.

[0015] 3. To overcome the limitations of the PET standard brain template method in small case volumes and provide a PET spatial standardization system with high universality.

[0016] 4. Solve the problem of insufficient accuracy and stability in the standardization process caused by low brain tissue contrast in CT images, and improve the accuracy and stability of standardization through optimized processes.

[0017] The object of the present invention is achieved through the following technical solutions: In the first aspect, the present invention proposes a CT-guided elderly brain PET image spatial standardization system, the system comprising:

[0018] PET / CT image acquisition and format conversion module, used to acquire paired PET images and CT images of elderly patients and perform format conversion processing;

[0019] PET / CT preprocessing module, used for rigid transformation preprocessing of PET images and CT images after format conversion;

[0020] PET / CT image coarse normalization module, used to optimize the nonlinear deformation field through nonlinear registration, apply it to the rigidly transformed PET image and CT image, and generate coarse normalized PET and CT images;

[0021] The PET / CT image fine normalization module is used to classify the coarsely registered CT images and align them with the tissue probability map of the standard space of the elderly brain, update the voxel classification probability, and then optimize the iterative nonlinear deformation field, which is applied to the coarsely registered CT images and PET images to generate finely standardized CT and PET images;

[0022] The automated SUV extraction module is used to extract the SUV values ​​of the cortical and subcortical ROIs from the fine-scale standardized PET images using the brain atlas in the MIITRA space for subsequent SUVR calculation and disease diagnosis analysis.

[0023] Furthermore, the PET / CT images were converted into NIFTI format after acquisition, and the standardized uptake value SUV was calculated for each voxel of the PET data.

[0024] Furthermore, the preprocessing process includes resampling the PET image to match the voxel resolution of the CT image and performing a rigid transformation on the CT image and the resampled PET image.

[0025] Furthermore, the PET / CT image coarse normalization module uses the Old Normalize method to normalize the rigidly transformed CT image to the CT brain template image, and further performs nonlinear registration to obtain the nonlinear deformation field through optimization and regularization.

[0026] Furthermore, the PET / CT image fine normalization module uses a mixed Gaussian model to classify tissues in the coarsely registered CT images, updates the voxel classification probability based on spatial prior information and the segmentation probability of the CT images, and iterates the nonlinear deformation field through optimization and regularization.

[0027] Furthermore, the automated SUV extraction module uses the brain atlas in MIITRA space to segment the fine-scale normalized PET images into multiple brain regions and calculates the average SUV value for each brain region.

[0028] In a second aspect, the present invention also provides a method for spatial standardization of elderly brain PET images based on CT guidance, the method comprising the following steps:

[0029] (1) Acquire paired PET and CT images of elderly patients and perform format conversion;

[0030] (2) Performing rigid transformation preprocessing on the format-converted PET and CT images;

[0031] (3) The nonlinear deformation field is optimized by nonlinear registration and applied to the rigidly transformed PET and CT images to generate coarsely standardized PET and CT images;

[0032] (4) Perform tissue classification on the coarsely registered CT images and align them with the tissue probability map of the standard space of the elderly brain, update the voxel classification probability, and then optimize the iterative nonlinear deformation field, apply it to the coarsely registered CT images and PET images, and generate finely standardized CT and PET images;

[0033] (5) For the fine-scale standardized PET images, the brain atlas in MIITRA space was used to extract the SUV values ​​of the cortical and subcortical ROIs for subsequent SUVR calculation and disease diagnosis analysis.

[0034] In a third aspect, the present invention also provides a device for spatial standardization of elderly brain PET images based on CT guidance, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, the method for spatial standardization of elderly brain PET images based on CT guidance is implemented.

[0035] In a fourth aspect, the present invention further provides a computer-readable storage medium having a program stored thereon, and when the program is executed by a processor, the method for spatial standardization of elderly brain PET images based on CT guidance is implemented.

[0036] In a fifth aspect, the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the CT-guided spatial standardization method for elderly brain PET images.

[0037] Beneficial effects of the present invention:

[0038] 1) Simplify the scanning process and reduce the burden on patients: The standardization of PET images guided by low-dose CT images avoids additional MRI scans, simplifies the scanning process, and reduces the economic and physical burden on patients. 2) Improve the stability and accuracy of the standardization process: The present invention uses an optimized standardization method to improve the registration accuracy through low-dose CT image guidance, overcomes the registration accuracy problem caused by PET and MRI scanning on different beds, and effectively solves the accuracy and stability problems in the standardization process caused by low brain tissue contrast in CT images, especially in the case of ventriculomegaly and cerebral lobe atrophy in elderly patients.

[0039] 3) Solving the problem of small sample size: The present invention avoids the problem of insufficient specificity of the PET standard brain template due to a small case volume, and provides a standardized method that is still effective under conditions of limited sample size.

[0040] 4) Strong adaptability, suitable for elderly patients: The present invention is specially designed for the characteristics of brain PET images of elderly patients. It can be applied to all types of brain PET probes, rather than being limited to a certain type of probe. It can adapt to the special situations of ventricular enlargement and cerebral lobe atrophy, and provide a more accurate and stable spatial standardization solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 This is a structural framework diagram of a low-dose CT-based elderly brain PET image spatial standardization system according to an embodiment of the present invention.

[0043] Figure 2 The present invention is a flowchart of an implementation of a low-dose CT-based elderly brain PET image spatial standardization system according to an embodiment of the present invention.

[0044] Figure 3 It is a schematic diagram of rigid registration in a PET / CT image preprocessing module according to an embodiment of the present invention.

[0045] Figure 4 FIG. 4 is a schematic diagram of a rough normalization of a PET / CT image according to an embodiment of the present invention.

[0046] Figure 5 FIG. 4 is a schematic diagram of fine normalization of PET / CT images according to an embodiment of the present invention.

[0047] Figure 6 It is a schematic diagram of SUV automatic calculation according to an embodiment of the present invention.

[0048] Figure 7 It is a correlation diagram of the results of an embodiment of the present invention and other different standardization methods with the gold standard. The horizontal axis represents the brain region SUV value obtained by the spatial standardization gold standard method based on high-quality MRI, and the vertical axis represents the SUV value of the same brain region extracted by three PET processes, among which CT represents the process system proposed by the present invention; Template represents the standardization process based on the PET brain template; CT_MNI_TPM represents the replacement of the tissue probability map in the fine standardization module with the MNI space TPM based on this system. The closer the slope of the scatter point fitting line is to 1, and the closer the Pearson correlation coefficient r is to 1, the closer this method is to the gold standard method.

[0049] Figure 8This is a comparison of the result images and accuracy of different spatial normalization methods for a PET image in an embodiment of the present invention. The example image is derived from a PET image of a patient with multiple system atrophy who has a large loss of striatal dopamine. Figure a is a gold standard method for spatial normalization based on high-quality MRI, Figure b is a process system proposed by the present invention, Figure c is a method based on a PET brain template, and Figure d is a replacement of MIITRA space TPM with MNI space TPM in fine spatial normalization. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific implementation examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0051] like Figure 1 and 2 The present invention proposes a spatial standardization method for PET images of the elderly brain based on CT guidance. The method uses a two-step strategy from coarse to fine, using CT images to guide PET image registration to the standard space, thereby achieving fully automatic and accurate extraction of SUVs in the region of interest (ROI). The system includes 5 modules, namely 1. PET / CT image acquisition and format conversion module, 2. PET / CT preprocessing module, 3. PET / CT image coarse standardization module, 4. PET / CT image fine standardization module, and 5. Automated SUV extraction module.

[0052] 1. PET / CT image acquisition and format conversion module

[0053] The image acquisition and format conversion module is used to acquire paired PET and CT images of elderly patients and convert them into NIFTI data format suitable for subsequent processing.

[0054] Implementation steps:

[0055] 1. Use PET / CT scanning equipment to collect 3D head PET molecular images and low-dose CT images of elderly patients. During the acquisition process, ensure that the subjects are in the same bed to ensure the registration accuracy of PET and CT images;

[0056] 2. PET and CT images collected by PET / CT instruments are usually stored in DICOM (Digital Imaging and Communications in Medicine) format. In order to facilitate subsequent processing, DICOM format images are converted to NIFTI format;

[0057] 3. Calculate the standardized uptake value (SUV) for each voxel of the PET data using the following formula:

[0058]

[0059] 2. PET / CT Preprocessing Module

[0060] The PET / CT preprocessing module is used to preprocess the SUV PET image and CT image converted into the NIFTI format.

[0061] Implementation steps:

[0062] 1. Resample the PET image to match the voxel resolution of the CT image. Assume that the voxel resolution of the CT image is Δx CT , the voxel resolution of the PET image is Δx PET , the resampling process ensures that Δx PET =Δx CT ;

[0063] 2. If Figure 3 As shown in the figure, with the CT template image as a reference, the CT image and the resampled PET image are gradually rigidly transformed. The rigid transformation includes rotation, translation and scaling, and its mathematical expression is:

[0064] x′=R·x+t

[0065] Where x is the original coordinate, x′ is the transformed coordinate, R is the rotation matrix, and t is the translation vector.

[0066] The CT standard template image is a brain template from a published study (Rorden, Christopher et al. "Age-specific CT and MRI templates for spatial normalization." NeuroImage vol. 61, 4 (2012): 957-65. doi: 10.1016 / j.neuroimage.2012.03.020).

[0067] 3. PET / CT Image Coarse Normalization Module

[0068] like Figure 4As shown, the PET / CT image coarse normalization module is used to perform coarse normalization on the PET and CT images after rigid transformation. The process obtains coarse normalized CT images and PET images, which are used as input for the next step of fine normalization. Compared with the original CT image, the coarse normalized CT image at this time has better similarity with the standard space to be registered, thereby providing a stable iterative starting point for registration optimization for fine standardization, which is conducive to fine standardization to find the global optimal solution in the registration optimization and prevent falling into the local optimal solution during the fine standardization process. This module is implemented based on the Old Normalize algorithm in the MATLAB brain image processing tool SPM12 (Statistical Parametric Mapping 12), and the specific steps and principles are as follows:

[0069] Implementation steps:

[0070] 1. Use the Old Normalize method to normalize the rigidly transformed CT image to the CT brain template image and further perform nonlinear registration:

[0071] x″=x′+φ(x′)

[0072] Among them, φ(x′) is the nonlinear deformation field, which is used to describe more detailed local deformation; x′ is the coordinate after rigid transformation, and x″ is the coordinate after rough normalization.

[0073] 2. Optimization and regularization: The nonlinear transformation parameters are solved by the optimization algorithm Levenberg-Marquardt so that the registered image I(x″) matches the CT standard template image T(x) to the maximum extent. The objective function can be expressed as:

[0074]

[0075] Where R(φ) is a regularization term used to constrain the smoothness of the nonlinear deformation field, and λ is a regularization parameter;

[0076] 3. Apply the generated nonlinear deformation field to the rigidly transformed PET and CT images to generate roughly standardized PET and CT images:

[0077] I PET,norm (x) = I PET (x″)

[0078] I CT,norm (x) = I CT (x″)

[0079] Among them, I PET,norm (x) and I CT,norm (x) are the PET and CT images obtained after rough standardization.PET and I CT They are rigidly transformed PET images and CT images respectively.

[0080] IV. PET / CT Image Standardization Module

[0081] like Figure 5 As shown, the PET / CT image fine standardization module performs more fine standardization on the coarsely registered PET and CT images. The module is implemented based on the New Segment algorithm in the MATLAB brain image processing toolkit SPM12. The input image to be segmented and registered in this step comes from the coarsely standardized CT image in the previous step. Through this step, the output is a finely standardized spatial nonlinear deformation field. The deformation field is applied to the coarsely standardized CT image and the coarsely standardized PET image, thereby generating a finely standardized CT image and PET image registered to the MIITRA standard space of the elderly brain.

[0082] The MIITRA standard space of the elderly brain was used in fine standardization to improve the similarity between the source image and the reference image in the registration. The two-step standardization strategy from coarse to fine avoids falling into local optimization during the iterative optimization process, and the result of coarse standardization provides a stable iterative starting point for registration optimization for fine standardization. All of these have jointly improved the stability of spatial standardization of elderly brain images with brain atrophy.

[0083] Implementation steps:

[0084] 1. Tissue classification is performed on the roughly registered CT images by optimizing the segmentation parameters. Since CT has low contrast for brain tissue, a mixed Gaussian model is used, using one Gaussian distribution for gray matter and white matter, and two Gaussian distributions for cerebrospinal fluid, bone, external tissue, and air:

[0085]

[0086] in, represents Gaussian distribution, μ k and σ kare the mean and standard deviation of brain tissue category k, respectively. Specifically, the selection of Gaussian parameters is based on the following: In CT imaging, gray matter and white matter each have only one average intensity value, so one Gaussian function is used. For cerebrospinal fluid, two Gaussian functions are used because cerebrospinal fluid close to bones may have a higher intensity due to the overflow effect. Since the value range of bones in CT is very wide, two Gaussian functions are also used. External tissue can be regarded as consisting of areas such as low-intensity fat and higher-intensity muscle, so two Gaussian functions are also used. In the air class, one Gaussian function is used to model most of the -1024HU, and another Gaussian function is used to model other low-intensity pixels with intensities between approximately -50 and -300HU;

[0087] 2. Align the coarsely registered CT image with the tissue probability map (TPM) of the Multichannel Illinois Institute of Technology and Rush University Aging (MIITRA) standard space and update the voxel classification probability. This process combines the spatial prior information P k (x) and the segmentation probability of the CT image p(k|I(x));

[0088] p(k|I(x),x)∝p(I(x)|k)·P k (x)

[0089] Among them, P k (x) is the spatial prior probability of category k. The tissue probability map TPM of the standard spatial MIITRA of the elderly brain is derived from a published study (Niaz, Mohammad Rakeen et al. "Development and evaluation of a high resolution 0.5mm isotropic T1-weighted template of the older adult brain." NeuroImage vol. 248 (2022): 118869. doi: 10.1016 / j.neuroimage.2021.118869);

[0090] 3. Optimize the nonlinear deformation field φ(x) by minimizing the objective function:

[0091]

[0092] Among them, T k(x+φ(x)) is the probability image of category k in the template image, R(φ) is the regularization term used to constrain the smoothness of the deformation field, and λ is the regularization parameter;

[0093] 4. Regularization term R(φ): The regularization term is used to constrain the smoothness and continuity of the deformation field. The regularization form is:

[0094]

[0095] This regularization term ensures the continuity and smoothness of the deformation field by suppressing the magnitude of the deformation field gradient;

[0096] 5. Use optimization algorithm, Levenberg-Marquardt iteration to optimize the deformation field

[0097]

[0098] Among them, α is the learning rate, is the gradient of the objective function with respect to the deformation field;

[0099] 6. Apply the fine-normalized nonlinear deformation field to the coarsely registered CT image and PET image to generate fine-normalized CT and PET images:

[0100] I PET,fine (x) = I PET,norm (x+φ(x))

[0101] I CT,fine (x) = I CT,norm (x+φ(x))

[0102] 5. Automated SUV Extraction Module

[0103] like Figure 6 As shown, the automated SUV extraction module is used to extract the SUV values ​​of the cortex and subcortical ROIs from the fine-scale standardized PET images using the brain atlas in the MIITRA space for subsequent SUVR calculation and disease diagnosis analysis.

[0104] Implementation steps:

[0105] 1. Use the brain atlas in MIITRA space to fine-tune the normalized PET images PET,fine divided into multiple brain regions;

[0106] 2. For each brain region, calculate the average SUV value:

[0107]

[0108] Where N is the number of voxels in the brain region, SUV i is the SUV value of each voxel.

[0109] Example

[0110] In an embodiment of the present system, spatial standardization of dopamine transporter (DAT) PET / CT images of elderly patients with multiple system atrophy is achieved. Specifically, first, in the PET / CT image acquisition and format conversion module, 50 elderly patients with multiple system atrophy are acquired on the PET / CT instrument. 18 FN-(3-fluoropropyl)-2β-carbomethoxy-3β-(4′-iodophenyl) demethyltropane (FP-β-CIT) 18 PET / CT image of F-FP-β-CIT, a tracer for DAT. The PET image was acquired with a size of 400 × 400 × 148 voxels and a resolution of 1 × 1 × 1.5 mm 3 CT images of the same patient were collected in the same bed, with an image size of 512×512×148 voxels and a resolution of 0.586×0.586×1.5mm 3 . After the collection is completed, the original DICOM format CT images are converted into the NIFTI format for subsequent analysis, and the SUV PET image in the NIFTI format is calculated based on the patient scanning parameter information, weight and injection dose in the PET image DICOM header file. In the PET / CT image preprocessing module, we perform linear interpolation on PET and CT images with inconsistent resolutions to ensure that the two images match in matrix size and resolution. Subsequently, the PET and CT images are rigidly transformed to align them with the CT brain template. Enter the PET / CT coarse normalization module and use the OldNormalize tool in SPM12 to nonlinearly normalize the CT image to the CT brain template. Select the annotated image size of 181×217×181 voxels and the resolution of 1×1×1mm 3 , to include the entire brain and reduce unnecessary background parts. The generated nonlinear deformation field is applied to the PET image. Then enter the PET / CT fine standardization module, use the Segment tool of SPM12, and select segmentation parameters suitable for low-dose CT. Gray matter and white matter use a single Gaussian distribution, while cerebrospinal fluid, bones, external tissues and air use double Gaussian distribution. At the same time, the tissue probability map TPM of the elderly brain standard space MIITRA is selected as the reference template for segmentation, and the coarsely registered CT images are segmented, and a forward deformation field is generated and applied to the coarsely standardized PET and CT images to obtain finely standardized PET and CT images with an image size of 181×217×181 voxels and a resolution of 1×1×1mm 3Finally, the automated SUV extraction module is used to extract the 1×1×1mm 3 Brain atlas, analyzes fine-scale standardized PET images and extracts SUV values ​​of specific brain regions (such as striatum and occipital cortex). These SUV values ​​will be used for subsequent SUVR calculation and disease diagnosis analysis.

[0111] In order to fully evaluate the beneficial effects of the system of the present invention, the present invention is compared with the system based on 18 A comparative analysis was conducted on the traditional brain template method of F-FP-β-CIT PET. At the same time, the suitability of the MIITRA standard space used in the present invention was evaluated, that is, the tissue probability map was replaced with the tissue probability map of the young Montreal Neurological Institute (MNI) space in the fine standardization module for comparative analysis. The comparative analysis conducted in the present invention uses the spatial normalization method based on high-quality MRI as the gold standard for reference. In the method based on PET brain template, the brain template is constructed by averaging 50 DAT-PET images after MRI-guided spatial standardization. In the comparative analysis, given that DAT-PET image analysis focuses on the putamen and caudate nucleus of the striatum, and the occipital cortex is often used as a reference area to calculate SUVR, we paid special attention to the accuracy of SUV value extraction in these key brain regions. Figure 7 As shown in the figure, the correlation diagram reveals the linear relationship between each method and the gold standard, where the closer the slope of the straight line is to 1, the closer the Pearson correlation coefficient is to 1, indicating that the method is more consistent with the gold standard. The comparison results clearly show that the system proposed in the present invention has the best performance in terms of correlation with the gold standard and the smallest error. In particular, Figure 8 As shown, when treating individuals with significantly decreased striatal dopamine, the method of the present invention can maintain an accuracy of up to 0.95, while the accuracy of the template-based method drops to 0.78. This significant difference can be attributed to the fact that in individuals with a large loss of striatal dopamine, the changes in brain structure reflected by CT images are not significant, while the difference between DAT-PET images and DAT-PET brain templates becomes particularly significant. In the iterative calculation process of the nonlinear deformation field, the template-based method is prone to calculation errors due to the large difference between the source image and the reference image. In addition, after replacing the TPM of MIITRA with the TPM of MNI, the accuracy of SUV extraction also dropped to 0.86, indicating that the use of a standard space suitable for elderly brain images in the fine spatial standardization in this system is conducive to improving the accuracy of spatial standardization.

[0112] Corresponding to the aforementioned embodiment of a CT-guided elderly brain PET image spatial standardization system, the present invention also provides an embodiment of a CT-guided elderly brain PET image spatial standardization method. For the specific implementation of each step of the method, please refer to the specific process of the aforementioned embodiment of a CT-guided elderly brain PET image spatial standardization system. The steps of the method are as follows:

[0113] (1) Acquire paired PET and CT images of elderly patients and perform format conversion;

[0114] (2) Performing rigid transformation preprocessing on the format-converted PET and CT images;

[0115] (3) The nonlinear deformation field is optimized by nonlinear registration and applied to the rigidly transformed PET and CT images to generate coarsely standardized PET and CT images;

[0116] (4) Perform tissue classification on the coarsely registered CT images and align them with the tissue probability map of the standard space of the elderly brain, update the voxel classification probability, and then optimize the iterative nonlinear deformation field, apply it to the coarsely registered CT images and PET images, and generate finely standardized CT and PET images;

[0117] (5) For the fine-scale standardized PET images, the brain atlas in MIITRA space was used to extract the SUV values ​​of the cortical and subcortical ROIs for subsequent SUVR calculation and disease diagnosis analysis.

[0118] Corresponding to the aforementioned embodiment of a method for spatial standardization of elderly brain PET images based on CT guidance, the present invention also provides an embodiment of a device for spatial standardization of elderly brain PET images based on CT guidance.

[0119] A device for spatial standardization of elderly brain PET images based on CT guidance is provided in an embodiment of the present invention, comprising a memory and one or more processors. The memory stores executable code. When the processor executes the executable code, it is used to implement a method for spatial standardization of elderly brain PET images based on CT guidance in the above embodiment.

[0120] The embodiment of the spatial standardization device for PET images of the elderly brain based on CT guidance provided by the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capabilities in which it is located to read the corresponding computer program instructions in the non-volatile memory into the memory and run it. From the hardware level, it is a hardware structure diagram of any device with data processing capabilities where the spatial standardization device for PET images of the elderly brain based on CT guidance provided by the present invention is located. In addition to the processor, memory, network interface, and non-volatile memory, any device with data processing capabilities where the device in the embodiment is located can also include other hardware according to the actual function of the device with data processing capabilities, which will not be repeated.

[0121] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0122] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0123] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a spatial standardization method of elderly brain PET images based on CT guidance in the above embodiment is implemented.

[0124] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device of any device with data processing capability, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0125] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the CT-guided elderly brain PET image spatial standardization method.

[0126] The above embodiments are used to illustrate the present invention rather than to limit the present invention. Any modification and change made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A CT-guided PET image spatial standardization system for elderly brain, characterized in that: The system includes: PET / CT image acquisition and format conversion module, used to acquire paired PET images and CT images of elderly patients and perform format conversion processing; PET / CT preprocessing module, used for rigid transformation preprocessing of PET images and CT images after format conversion; PET / CT image coarse normalization module, used to optimize the nonlinear deformation field through nonlinear registration, apply it to the rigidly transformed PET image and CT image, and generate coarse normalized PET and CT images; The PET / CT image fine normalization module is used to classify the coarsely registered CT images and align them with the tissue probability map of the standard space of the elderly brain, update the voxel classification probability, and then optimize the iterative nonlinear deformation field, which is applied to the coarsely registered CT images and PET images to generate finely standardized CT and PET images; The automated SUV extraction module is used to extract the SUV values ​​of the cortical and subcortical ROIs from the fine-scale standardized PET images using the brain atlas in the MIITRA space for subsequent SUVR calculation and disease diagnosis analysis.

2. The CT-guided PET image spatial standardization system for elderly brain according to claim 1, characterized in that: PET / CT images were converted into NIFTI format after acquisition, and the standardized uptake value SUV was calculated for each voxel of the PET data.

3. The CT-guided PET image spatial standardization system for elderly brain according to claim 1, characterized in that: The preprocessing process includes resampling the PET images to match the voxel resolution of the CT images and applying a rigid transformation to the CT images and the resampled PET images.

4. The CT-guided PET image spatial standardization system for elderly brain according to claim 1, characterized in that: The PET / CT image coarse normalization module uses the Old Normalize method to normalize the rigidly transformed CT image to the CT brain template image, and further performs nonlinear registration to obtain the nonlinear deformation field through optimization and regularization.

5. The CT-guided PET image spatial standardization system for elderly brain according to claim 1, characterized in that: The PET / CT image fine normalization module uses a mixed Gaussian model to classify tissues in the coarsely registered CT images, updates the voxel classification probability based on spatial prior information and the segmentation probability of the CT images, and iterates the nonlinear deformation field through optimization and regularization.

6. The CT-guided PET image spatial standardization system for elderly brain according to claim 1, characterized in that: The automated SUV extraction module uses the brain atlas in MIITRA space to segment the fine-scale normalized PET images into multiple brain regions and calculates the mean SUV value for each brain region.

7. A method for spatial standardization of elderly brain PET images based on the CT-guided elderly brain PET image spatial standardization system according to any one of claims 1 to 6, characterized in that: The method comprises the following steps: (1) Acquire paired PET and CT images of elderly patients and perform format conversion; (2) Performing rigid transformation preprocessing on the format-converted PET and CT images; (3) The nonlinear deformation field is optimized by nonlinear registration and applied to the rigidly transformed PET and CT images to generate coarsely standardized PET and CT images; (4) Perform tissue classification on the coarsely registered CT images and align them with the tissue probability map of the standard space of the elderly brain, update the voxel classification probability, and then optimize the iterative nonlinear deformation field, apply it to the coarsely registered CT images and PET images, and generate finely standardized CT and PET images; (5) For the fine-scale standardized PET images, the brain atlas in MIITRA space was used to extract the SUV values ​​of the cortical and subcortical ROIs for subsequent SUVR calculation and disease diagnosis analysis.

8. A device for spatial standardization of elderly brain PET images based on CT guidance, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, the method for spatial standardization of elderly brain PET images based on CT guidance as described in claim 7 is implemented.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by the processor, the method for spatial standardization of elderly brain PET images based on CT guidance as claimed in claim 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for spatial standardization of elderly brain PET images based on CT guidance as claimed in claim 7 is implemented.

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