CN111311585A - Magnetic resonance diffusion tensor brain image analysis method and system for neonates - Google Patents
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Abstract
The invention discloses a magnetic resonance diffusion tensor brain image analysis method and system for a newborn, and belongs to the field of medical image processing. The method comprises the following steps: A. preprocessing a neonatal diffusion tensor imaging DTI image; B. the step of calculating the diffusion tensor parameters comprises the following steps: fitting eigenvalues of a diffusion tensor through a DTI tensor model, and calculating a diffusion parameter graph based on an anisotropic fraction FA, an average diffusion coefficient MD, an axial diffusion coefficient AD and a radial diffusion coefficient RD; C. a step of image registration; D. and extracting an anisotropy fraction FA-based framework and projecting the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD of the tested person onto the framework. According to the method, the brain image with clear white matter and grey matter signals is finally obtained by preprocessing the DTI image of the neonate, calculating diffusion tensor parameters, registering the image and extracting an FA skeleton.
Description
Technical Field
The invention relates to a medical image and Magnetic Resonance Image (MRI) image processing technology, in particular to a magnetic resonance diffusion tensor brain image analysis method and a system thereof for a newborn.
Background
Magnetic Resonance Imaging (MRI) Diffusion Tensor Imaging (DTI), also referred to clinically as Diffusion Tensor Imaging. The principle of DTI is to indirectly observe the microstructure characteristics of white matter tissues of brain by measuring the degree and direction of water molecule diffusion in the tissues. The DTI imaging technology is the only technology which can display the trend of the white matter nerve fiber bundle of the brain in a living body nondestructive mode, so that the DTI imaging technology is widely applied to clinical and human brain science research.
The most common and reliable DTI analysis technique currently uses a Tract-Based spatial statistics (TBSS) method. The method comprises the following steps: by adopting a TBSS algorithm, after a tested diffusion tensor parameter image is registered to a standard space through linear and nonlinear transformation, an anisotropic Fraction (FA) -based framework is extracted, and diffusion tensor parameter projection is carried out. In this way, the problem of confounding white and grey brain matter encountered in conventional DTI analysis can be solved.
Due to the adoption of the linear and nonlinear registration algorithm based on FSL by the TBSS method, the method is only suitable for healthy adult population with clear white matter and gray matter boundaries. For the neonatal brain with unclear and confusing white and gray brain matter signals, it is still difficult to apply the magnetic resonance diffusion tensor brain image (DTI) analysis technique to perform image analysis. The FSL is a brain function magnetic resonance imaging software library.
In the field of neonatal brain research, relatively poor MRI image contrast and large neonatal brain shape differences are the most major technical challenges facing current neonatal brain DTI studies. According to the related technical literature, up to 74% of the neonatal brain studies do not use the neonatal dedicated magnetic resonance scanning hardware, and 76% of the neonatal brain imaging studies do not adopt a special neonatal DTI image analysis method.
Disclosure of Invention
In view of this, the main objective of the present invention is to provide a method and a system for analyzing a magnetic resonance diffusion tensor brain image of a neonate, the method and the system perform a series of preprocessing on a collected neonate DTI image and T2 weighted structure image data, reconstruct a DTI diffusion tensor parameter image, perform joint iterative registration on a T2 weighted structure image and a DTI B0 image, select an optimal target image for registration, and finally perform anisotropic (FA) skeleton extraction to obtain a neonate brain image with clear white matter and gray matter signals.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a magnetic resonance diffusion tensor brain image analysis method for a newborn comprises the following steps:
A. preprocessing a neonatal diffusion tensor imaging DTI image; respectively including the processes of data format conversion, image correction and brain tissue extraction;
B. the step of calculating the diffusion tensor parameters comprises the following steps: fitting eigenvalues of a diffusion tensor through a DTI tensor model, and calculating a diffusion parameter graph based on an anisotropic fraction FA, an average diffusion coefficient MD, an axial diffusion coefficient AD and a radial diffusion coefficient RD;
C. a step of image registration;
D. and extracting an anisotropy fraction FA-based framework and projecting the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD of the tested person onto the framework.
The process of converting the data format, correcting the image and extracting the brain tissue in the step A specifically comprises the following steps:
a1, converting the original data generated by the magnetic resonance equipment into an image in a DICOM format, and firstly converting the image into a universal medical image format NIFTI;
a2, performing EDDY current artifact and head movement correction on the NIFTI format image by using an EDDY _ CORRECT instruction;
and A3, removing non-brain tissue parts such as neck, eyeball and skull in the image processed by the step A2 by adopting a BET algorithm.
And step B, calculating a relation based on the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD as follows:
MD＝(λ1+λ2+λ3)/3；
AD＝λ1；
RD＝(λ2+λ3)/2；
wherein: FA is based on anisotropic fraction, MD is average diffusion coefficient, AD is axial diffusion coefficient, and RD is radial diffusion coefficient; lambda [ alpha ]1、λ2、λ3Respectively, representing the eigenvalues of the diffusion tensor.
Step C, the step of performing image registration specifically includes:
c1, carrying out six-freedom-degree linear registration on a B0 image in a single tested DTI image and self T2 weighted imaging, and acquiring a linear registration matrix Mat;
c2, carrying out nonlinear registration on the single tested T2 weighted imaging and a standard neonatal T2 weighted imaging template, and acquiring a nonlinear registration deformation map Warp;
c3, registering the single tested FA image to a newborn T2 weighted imaging template by superposing a linear registration matrix Mat and a nonlinear registration deformation map Warp so as to realize the standardization of the preliminary degree;
c4, performing mutual registration iteration on all FA images, and acquiring a most representative image Target in a data-driven manner;
c5, registering all tested FA images to the most representative image Target to achieve a second degree of normalization thereof;
and C6, further superposing and averaging all the images registered to the most representative image Target to obtain an average FA image, thereby realizing high-degree standardization.
A magnetic resonance diffusion tensor brain image analysis system for a newborn comprises an MRI image preprocessing module, a diffusion tensor parameter calculation module, an image registration module and an FA skeleton extraction and projection module; wherein the content of the first and second substances,
the MRI image preprocessing module comprises a data format conversion sub-module, an image correction sub-module and a brain tissue extraction sub-module; respectively used for carrying out data format conversion, image correction and brain tissue extraction on the DTI image of the neonate;
the diffusion tensor parameter calculating module is used for fitting eigenvalues of the diffusion tensor through the DTI tensor model and calculating a diffusion parameter graph based on anisotropic fraction FA, average diffusion coefficient MD, axial diffusion coefficient AD, radial diffusion coefficient RD and the like;
the image registration module is used for carrying out preliminary registration by introducing a T2 weighted structure image and automatically selecting the image Target matched to the most representative image in a data driving mode through multiple iterations, and further superposing and averaging all the images registered to the most representative image Target to obtain an average FA image;
and the FA framework extraction and projection module is used for extracting an FA framework and projecting the FA framework based on the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD of the testee onto the framework.
Wherein: the MRI image preprocessing module comprises a data format conversion sub-module, an image correction sub-module and a brain tissue extraction sub-module; wherein the content of the first and second substances,
the data format conversion sub-module is used for converting original data generated by the magnetic resonance equipment into an image in a DICOM format into a universal medical image format NIFTI;
the image correction submodule is used for performing EDDY current artifact and head motion correction on the NIFTI format image by using an EDDY _ CORRECT instruction;
the brain tissue extraction submodule removes non-brain tissue parts such as neck, eyeball and skull by adopting a BET algorithm.
Wherein: the diffusion tensor parameter calculation module calculates the following relations based on the anisotropic fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD:
MD＝(λ1+λ2+λ3)/3；
AD＝λ1；
RD＝(λ2+λ3)/2；
wherein: FA is based on anisotropic fraction, MD is average diffusion coefficient, AD is axial diffusion coefficient, and RD is radial diffusion coefficient; lambda [ alpha ]1、λ2、λ3Respectively, representing the eigenvalues of the diffusion tensor.
The image registration module comprises a linear registration submodule, a nonlinear registration submodule, a primary registration submodule, a registration iteration submodule, a second registration submodule and a third registration submodule; which are respectively used for:
performing linear registration of six degrees of freedom on a B0 image in a single tested DTI image and the self T2 weighted imaging by using the linear registration submodule to obtain a linear registration matrix Mat;
carrying out nonlinear registration on the single tested T2 weighted imaging and a standard neonatal T2 weighted imaging template by utilizing the nonlinear registration submodule to obtain a nonlinear registration deformation map Warp;
registering a single tested FA image to a weighted imaging template of the newborn T2 by superposing a linear registration matrix Mat and a nonlinear registration deformation map Warp by using the primary registration submodule to realize the standardization of the primary degree;
performing mutual registration iteration on all FA images by using the registration iteration submodule, and acquiring a most representative image Target in a data driving mode;
registering all tested FA images to the most representative image Target by using a second registration submodule to realize the second-degree standardization of the FA images;
and further superposing and averaging all the images registered to the most representative image Target by using a third registration submodule to obtain a final average FA image.
The magnetic resonance diffusion tensor brain image analysis method and the system thereof for the neonate have the following beneficial effects:
1) by adopting the method, the defect that the traditional TBSS method needs to rely on a standard FMRIB58_ FA template of an adult is overcome, and the FA skeleton map can be reconstructed without using a newborn FA template by introducing the combined iterative registration of the T2 weighted structural image and the DTI B0 image.
2) Compared with the conventional TBSS method, the method has the advantages that the T2 weighted structure image is introduced to carry out preliminary registration, the appropriate Target can be automatically selected and matched through a method of data driving through multiple iterations, and the FA framework can be extracted without depending on an FA template in the whole process.
3) Experiments prove that the FA skeleton obtained by using the method for neonatal DTI analysis is more complete and has more clinical and physiological significance.
Drawings
FIG. 1 is a schematic diagram of a conventional process for extracting an anisotropic Fraction (FA) -based skeleton by using a conventional spatial statistics-based (TBSS) method;
FIG. 2 is a schematic flow chart of a magnetic resonance diffusion tensor brain image analysis method for a neonate according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a magnetic resonance diffusion tensor brain image analysis system for a neonate according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings and embodiments thereof.
FIG. 1 is a schematic diagram of a conventional process for extracting an anisotropic Fraction (FA) -based skeleton by using a conventional spatial statistics-based (TBSS) method;
as shown in fig. 1, the process of extracting the anisotropic Fraction (FA) -based skeleton by using the conventional spatial statistics based (TBSS) method mainly includes the following steps:
preprocessing a neonate FA image; for a newborn FMRIB58_ FA template, registering diffusion tensor parameter images of a subject to a standard space of the template through linear and nonlinear transformation by adopting a space-based statistics (TBSS) algorithm; and finally, solving the problem of mixed white brain matter and gray brain matter images in the conventional DTI analysis by extracting a skeleton map based on anisotropic Fraction (FA).
Fig. 2 is a schematic flow chart of a magnetic resonance diffusion tensor brain image analysis method for a neonate according to an embodiment of the present invention.
As shown in fig. 2, the method for analyzing a magnetic resonance diffusion tensor brain image of a newborn includes the following steps:
step 21: and (3) preprocessing the DTI image of the newborn.
Here, the preprocessing processes include data format conversion, image correction, and extraction of brain tissue, respectively. The method specifically comprises the following steps:
step 211: the original data generated by the magnetic resonance equipment is an image in DICOM format, and is firstly converted into a universal medical image format NIFTI.
Step 212: the DTI image, i.e. the NIFTI format image, generates image artifacts due to the presence of EDDY current, and meanwhile, the subject may have head movement during the scanning process, so the EDDY _ CORRECT command is used in this embodiment to perform EDDY current artifacts and head movement correction.
Step 213: and removing non-brain tissue parts such as neck, eyeball, skull and the like by adopting a BET algorithm.
Here, the BET algorithm is specifically an algorithm for formulating an input image by calling a BET command for brain tissue extraction and according to parameters such as an input set image density threshold f.
The preprocessing link is a standard flow of image preprocessing in the brain image analysis process, and because image operation in brain tissues is not involved, the good effect can be obtained for DTI images of brain images of adults and neonates.
Step 22: and calculating diffusion tensor parameters. The method specifically comprises the following steps: and fitting eigenvalues of the diffusion tensor through the DTI tensor model, and calculating a diffusion parameter graph based on the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD, the radial diffusion coefficient RD and the like.
Here, the calculation is based on the relationships of the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD, and the radial diffusion coefficient RD as follows:
MD＝(λ1+λ2+λ3)/3；
AD＝λ1；
RD＝(λ2+λ3)/2。
wherein: MD is the average diffusion coefficient, AD is the axial diffusion coefficient, and RD is the radial diffusion coefficient; lambda [ alpha ]1、λ2、λ3Respectively, representing the eigenvalues of the diffusion tensor.
Step 23: a step of image registration; the method has the advantages that the T2 weighted structure image is introduced for preliminary registration, the most representative image Target matched to be suitable can be automatically selected in a data driving mode through multiple iterations, all images registered to the most representative image Target are further subjected to superposition averaging, and an average FA image is obtained. The method specifically comprises the following steps:
step 231: performing six-degree-of-freedom linear registration on a B0 image in a single tested DTI image and self T2 weighted imaging, and acquiring a linear registration matrix Mat;
step 232: carrying out nonlinear registration on a single tested T2 weighted imaging and a standard neonatal T2 weighted imaging template, and acquiring a nonlinear registration deformation map Warp;
step 233: registering a single tested FA image to a newborn T2 weighted imaging template by superposing a linear registration matrix Mat and a nonlinear registration deformation map Warp so as to realize the standardization of the preliminary degree;
step 234: performing mutual registration iteration on all FA images, and acquiring a most representative image Target in a data driving mode;
step 235: registering all tested FA images to the most representative image Target to achieve a second degree of normalization thereof;
step 236: all images registered to the most representative image Target are further subjected to superposition averaging to obtain an average FA image, thereby achieving a high degree of standardization.
Step 24: extracting an FA skeleton and projecting the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD of the testee onto the skeleton.
The FA framework can be used for subsequent scientific research and clinical statistical analysis.
Fig. 3 is a schematic diagram of a magnetic resonance diffusion tensor brain image analysis system for a neonate according to an embodiment of the present invention.
As shown in fig. 3, the magnetic resonance diffusion tensor brain image analysis system for the neonate mainly includes an MRI image preprocessing module, a diffusion tensor parameter calculation module, an image registration module, and an FA skeleton extraction and projection module. Wherein:
the MRI image preprocessing module comprises a data format conversion sub-module, an image correction sub-module and a brain tissue extraction sub-module; respectively used for carrying out data format conversion, image correction and brain tissue extraction on the DTI image of the newborn.
And the diffusion tensor parameter calculating module is used for fitting the eigenvalue of the diffusion tensor through the DTI tensor model and calculating a diffusion parameter graph based on the anisotropic fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD, the radial diffusion coefficient RD and the like. The relation for calculating the fraction based on anisotropy FA, average diffusion coefficient MD, axial diffusion coefficient AD, radial diffusion coefficient RD is as described above.
The image registration module is used for carrying out preliminary registration by introducing a T2 weighted structure image and automatically selecting the image Target matched to the most representative image in a data driving mode through multiple iterations, and further superposing and averaging all the images registered to the most representative image Target to obtain an average FA image. The system comprises a linear registration submodule, a nonlinear registration submodule, a primary registration submodule, a registration iteration submodule, a second registration submodule and a third registration submodule;
respectively used for:
1) performing linear registration of six degrees of freedom on a B0 image in a single tested DTI image and the self T2 weighted imaging by using the linear registration submodule to obtain a linear registration matrix Mat;
2) carrying out nonlinear registration on the single tested T2 weighted imaging and a standard neonatal T2 weighted imaging template by utilizing the nonlinear registration submodule to obtain a nonlinear registration deformation map Warp;
3) registering a single tested FA image to a weighted imaging template of the newborn T2 by superposing a linear registration matrix Mat and a nonlinear registration deformation map Warp by using the primary registration submodule to realize the standardization of the primary degree;
4) performing mutual registration iteration on all FA images by using the registration iteration submodule, and acquiring a most representative image Target in a data driving mode;
5) registering all tested FA images to the most representative image Target by using a second registration submodule to realize the second-degree standardization of the FA images;
6) and further superposing and averaging all the images registered to the most representative image Target by using a third registration submodule to obtain a final average FA image.
And the FA framework extraction and projection module is used for extracting an FA framework and projecting the FA framework based on the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD of the testee onto the framework.
In order to verify the reliability of the neonatal magnetic resonance diffusion tensor brain image analysis method, in the embodiment of the invention, the same group of neonatal magnetic resonance diffusion tensor brain images are processed by a conventional method and the method of the invention respectively, and the extracted white matter skeleton map is compared.
The schematic diagram of "extracted FA skeleton map" in fig. 1 and the schematic diagram of "extracted FA skeleton map" in fig. 2 are experimental results of the prior art and the method of the present invention, respectively, and it can be seen intuitively by comparison that, compared with the discontinuous incomplete state of the white matter fiber bundle skeleton in fig. 1, the method of the present invention can obtain an apparently continuous and complete white matter fiber bundle skeleton, and the result can be further applied in subsequent statistical analysis or clinical research.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (8)
1. A magnetic resonance diffusion tensor brain image analysis method for a newborn is characterized by comprising the following steps:
A. preprocessing a neonatal diffusion tensor imaging DTI image; respectively including the processes of data format conversion, image correction and brain tissue extraction;
B. the step of calculating the diffusion tensor parameters comprises the following steps: fitting eigenvalues of a diffusion tensor through a DTI tensor model, and calculating a diffusion parameter graph based on an anisotropic fraction FA, an average diffusion coefficient MD, an axial diffusion coefficient AD and a radial diffusion coefficient RD;
C. a step of image registration;
D. and extracting an anisotropy fraction FA-based framework and projecting the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD of the tested person onto the framework.
2. The method for analyzing the neonatal magnetic resonance diffusion tensor brain image according to claim 1, wherein the step a of performing data format conversion, image correction and brain tissue extraction specifically comprises:
a1, converting the original data generated by the magnetic resonance equipment into an image in a DICOM format, and firstly converting the image into a universal medical image format NIFTI;
a2, performing EDDY current artifact and head movement correction on the NIFTI format image by using an EDDY _ CORRECT instruction;
and A3, removing non-brain tissue parts such as neck, eyeball and skull in the image processed by the step A2 by adopting a BET algorithm.
3. The method for analyzing the neonatal magnetic resonance diffusion tensor brain image according to claim 1, wherein the calculation of step B is based on the relationship among the anisotropy fraction FA, the mean diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD as follows:
MD＝(λ1+λ2+λ3)/3；
AD＝λ1；
RD＝(λ2+λ3)/2；
wherein: FA is based on anisotropic fraction, MD is average diffusion coefficient, AD is axial diffusion coefficient, and RD is radial diffusion coefficient; lambda [ alpha ]1、λ2、λ3Respectively, representing the eigenvalues of the diffusion tensor.
4. The method for analyzing the neonatal magnetic resonance diffusion tensor brain image according to claim 1, wherein the step of performing image registration in step C specifically comprises:
c1, carrying out six-freedom-degree linear registration on a B0 image in a single tested DTI image and self T2 weighted imaging, and acquiring a linear registration matrix Mat;
c2, carrying out nonlinear registration on the single tested T2 weighted imaging and a standard neonatal T2 weighted imaging template, and acquiring a nonlinear registration deformation map Warp;
c3, registering the single tested FA image to a newborn T2 weighted imaging template by superposing a linear registration matrix Mat and a nonlinear registration deformation map Warp so as to realize the standardization of the preliminary degree;
c4, performing mutual registration iteration on all FA images, and acquiring a most representative image Target in a data-driven manner;
c5, registering all tested FA images to the most representative image Target to achieve a second degree of normalization thereof;
and C6, further superposing and averaging all the images registered to the most representative image Target to obtain an average FA image, thereby realizing high-degree standardization.
5. A magnetic resonance diffusion tensor brain image analysis system for a newborn is characterized by comprising an MRI image preprocessing module, a diffusion tensor parameter calculating module, an image registering module and an FA skeleton extracting and projecting module; wherein the content of the first and second substances,
the MRI image preprocessing module comprises a data format conversion sub-module, an image correction sub-module and a brain tissue extraction sub-module; respectively used for carrying out data format conversion, image correction and brain tissue extraction on the DTI image of the neonate;
the diffusion tensor parameter calculating module is used for fitting eigenvalues of the diffusion tensor through the DTI tensor model and calculating a diffusion parameter graph based on anisotropic fraction FA, average diffusion coefficient MD, axial diffusion coefficient AD, radial diffusion coefficient RD and the like;
the image registration module is used for carrying out preliminary registration by introducing a T2 weighted structure image and automatically selecting the image Target matched to the most representative image in a data driving mode through multiple iterations, and further superposing and averaging all the images registered to the most representative image Target to obtain an average FA image;
and the FA framework extraction and projection module is used for extracting an FA framework and projecting the FA framework based on the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD of the testee onto the framework.
6. The neonatal magnetic resonance diffusion tensor brain image analysis system of claim 5, wherein the MRI image preprocessing module comprises a data format conversion sub-module, an image correction sub-module and a brain tissue extraction sub-module; wherein the content of the first and second substances,
the data format conversion sub-module is used for converting original data generated by the magnetic resonance equipment into an image in a DICOM format into a universal medical image format NIFTI;
the image correction submodule is used for performing EDDY current artifact and head motion correction on the NIFTI format image by using an EDDY _ CORRECT instruction;
the brain tissue extraction submodule removes non-brain tissue parts such as neck, eyeball and skull by adopting a BET algorithm.
7. The neonatal magnetic resonance diffusion tensor brain image analysis system according to claim 5, wherein the diffusion tensor parameters calculation module calculates the following relations based on the anisotropy fraction FA, the average diffusion coefficient MD, the axial diffusion coefficient AD and the radial diffusion coefficient RD:
MD＝(λ1+λ2+λ3)/3；
AD＝λ1；
RD＝(λ2+λ3)/2；
wherein: FA is based on anisotropic fraction, MD is average diffusion coefficient, AD is axial diffusion coefficient, and RD is radial diffusion coefficient; lambda [ alpha ]1、λ2、λ3Respectively, representing the eigenvalues of the diffusion tensor.
8. The neonatal magnetic resonance diffusion tensor brain image analysis system of claim 5, wherein the image registration module comprises a linear registration sub-module, a non-linear registration sub-module, a preliminary registration sub-module, a registration iteration sub-module, a second registration sub-module, and a third registration sub-module; which are respectively used for:
performing linear registration of six degrees of freedom on a B0 image in a single tested DTI image and the self T2 weighted imaging by using the linear registration submodule to obtain a linear registration matrix Mat;
carrying out nonlinear registration on the single tested T2 weighted imaging and a standard neonatal T2 weighted imaging template by utilizing the nonlinear registration submodule to obtain a nonlinear registration deformation map Warp;
registering a single tested FA image to a weighted imaging template of the newborn T2 by superposing a linear registration matrix Mat and a nonlinear registration deformation map Warp by using the primary registration submodule to realize the standardization of the primary degree;
performing mutual registration iteration on all FA images by using the registration iteration submodule, and acquiring a most representative image Target in a data driving mode;
registering all tested FA images to the most representative image Target by using a second registration submodule to realize the second-degree standardization of the FA images;
and further superposing and averaging all the images registered to the most representative image Target by using a third registration submodule to obtain a final average FA image.
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