CN117059234A - DTI-based three-dimensional reconstruction method for rat brain nerve fiber bundles - Google Patents
DTI-based three-dimensional reconstruction method for rat brain nerve fiber bundles Download PDFInfo
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Abstract
The invention provides a three-dimensional reconstruction method of rat brain nerve fiber bundles based on DTI, which comprises the following steps: collecting a DTI image and a T1 weighted image of the head of the rat by adopting a small animal magnetic resonance instrument; preprocessing the acquired image; using the pretreatment result as input, removing non-brain tissue images to complete extraction of brain tissue images; performing tensor calculation and full brain deterministic fiber tracking by using the result of the step (3); defining a brain specific brain region as a region of interest (ROI) according to a quasi-brain map of a rat, performing fiber screening and index calculation, and constructing a nerve fiber bundle connected with the brain specific brain region in a simulation mode. The invention can carry out three-dimensional reconstruction and quantitative analysis on nerve fiber connection between different brain regions of the rat brain through an algorithm, and can be widely applied to the research of rat brain network construction, brain related disease diagnosis and treatment and the like.
Description
Technical Field
The invention belongs to the field of medical image processing, and particularly relates to a three-dimensional reconstruction method of rat brain nerve fiber bundles based on DTI.
Background
The human brain is a highly integrated and interoperable complex system, and more research is beginning to look at the brain from the perspective of the brain network. In 2005, sporns et al proposed to study various brain neurological diseases from the perspective of brain networks. It is increasingly recognized that many neurological and psychiatric disorders occur in association with abnormalities in the brain network, not limited to a single brain region, and therefore it is important to study interconnections and information interactions between different brain regions.
With the widespread use of imaging techniques such as functional magnetic resonance imaging (fMRI) and positron emission computed tomography (PET), researchers have found a variety of functional networks present in the brain, and as exemplified by default mode networks, a number of clinical studies have found that abnormal activity of DMN exists in the brains of many patients with mental disorders and behavioral disorders. More research is currently being conducted on DMN for functional connectivity, network mode and topology properties, and relatively little research is being conducted on its structural aspects. However, the trend and connection relationship of the nerve fibers of the DMN at present, particularly how to connect the nerve fibers between the nerve node of the cortex (prefrontal cortex) and the deep nucleus of the brain (Hippocampus, etc.), are not completely clear.
Diffusion Weighted Imaging (DWI) is a special magnetic resonance imaging technique that can measure the diffusion of water molecules in tissue, thereby reflecting the microstructure of the tissue. In 1994, researchers calculated the diffusivity of water molecules in different directions by applying a plurality of diffusion-sensitive gradients in different directions. This imaging technique, which applies a plurality of directional diffusion sensitive gradients based on DWI technology, is Diffusion Tensor Imaging (DTI). The application of DTI technology generally involves two aspects. On one hand, the method can reconstruct white matter fiber structure in the brain through fiber tracking technology, helps to study the structural connection of the brain, and can be used for constructing a brain structural network. On the other hand, scalar indexes such as anisotropy fraction (fractional anisotropy, FA), average diffusion coefficient (MD) and the like can be calculated from DTI data, and these indexes can quantitatively describe the anisotropic properties of tissues, and statistical and quantitative analysis of these parameters can help to diagnose diseases.
Disclosure of Invention
In order to solve the technical problems, the invention provides a three-dimensional reconstruction method of rat brain nerve fiber bundles based on DTI, which explores the structural connection between different brain regions by researching nerve fiber structure connection between rat brain regions. The invention utilizes the nerve fiber tracking technology to simulate and construct the rat brain nerve fibers based on the rat head DTI data, is helpful for revealing the connection and projection relations of nerve fiber structures in different brain regions of the rat brain, and provides powerful support for constructing the rat brain network and diagnosing and treating brain related diseases.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a three-dimensional reconstruction method of rat brain nerve fiber bundles based on DTI comprises the following steps:
step (1): collecting a DTI image and a T1 weighted image of the head of the rat;
step (2): preprocessing the acquired DTI image of the head of the rat, wherein the preprocessing comprises data format conversion, vortex correction and gradient direction correction;
step (3): forming a rat brain image mask (mask) and removing non-brain parts in the image by using the result of the preprocessing in the step (2) as an input;
step (4): performing tensor calculation and full brain deterministic fiber tracking by using the result of the step (3) to obtain rat full brain fiber structure connection;
step (5): based on the step (4), different brain regions of the brain are defined as interested regions according to the brain map of the rat, and specific fiber screening and index calculation are carried out.
Further, the step (1) specifically includes:
step (1.1), acquiring a DTI image and a T1 weighted image of the head of the rat by using a small animal magnetic resonance instrument;
the specific scanning parameters of the step (1.2) are as follows: the repetition time is3500ms, an echo time of 17.1ms, a flip angle of 90, the matrix size is 100 x 60 x 110, voxel size of 0.3X0.3X0.3 mm 3 The gradient sensitivity factor b is 850s/mm 2 A total of 33 images were acquired, including 3 b0 images without applied gradient magnetic fields and 30 images with applied non-coplanar diffusion gradient directions; gradient sensitivity factor b=0s/mm of the b0 image 2 Repeated acquisitions were performed 3 times.
Further, the step (2) specifically includes:
step (2.1) using MRIcron software to convert rat head DTI original data directly obtained by a small animal magnetic resonance instrument from a dicom format to a nifti format, and obtaining a bvec file storing a bval file of a gradient sensitive factor b and gradient direction information;
step (2.2) performing eddy current correction, registering the rat head DTI image and the T1 weighted image to a b0 image with a gradient sensitivity factor of b=0 so as to eliminate artifacts caused by head movement and eddy current in the image scanning process;
and (2.3) correcting the gradient direction, and adjusting the gradient direction of the original image according to the eddy current correction result.
Further, the step (3) specifically includes:
step (3.1) extracting a rat brain image mask (mask), introducing a b0 image and modifying threshold parameters, removing non-brain tissue images (skull, fat and the like) from a whole brain image, and only leaving the rat brain tissue images to form the rat brain mask; for the region which is not perfectly segmented by the algorithm, manually sketching the rat brain mask by using software such as MRIcron and the like;
and (3.2) dividing the rat brain image according to the obtained brain mask to obtain a DTI image after separating non-brain tissues.
Further, the step (4) specifically includes:
step (4.1) preprocessing before nerve fiber tracking, and transposing the content of the gradient direction file bvec from row display to column display, wherein the gradient direction file bvec does not contain the gradient direction corresponding to the b0 image;
step (4.2) adopting Diffusion Toolkit software, inputting b values and a gradient direction bvec file, estimating diffusion tensors by a linear least squares fitting method, and calculating corresponding diffusion tensors FA and scalar indexes in each voxel of the whole brain for connecting the whole brain fibers;
step (4.3) rebuilding nerve fiber connection of the whole brain by using Diffusion Toolkit software and a fiber continuous tracking algorithm, wherein the termination condition of fiber tracking is as follows: (1) Setting a fiber tracking range according to the rat brain mask calculated in the step (3); (2) the threshold of the diffusion tensor FA is set to 0.15-1.00; (3) the angle threshold is set to 45 °; (4) The minimum value of the fiber length is 2mm, and the maximum fiber length is 50mm;
and (4.4) realizing full brain nerve fiber tracking visualization and data analysis through TrackVis software.
Further, the step (5) specifically includes:
step (5.1) image registration: firstly, registering the rat T1 weighted image to a rat individual Diffusion space by using FSL software; then registering the rat T1 weighted image registered to the individual Diffusion space onto the standard map space image, and obtaining a transformation matrix; inverting the transformation matrix to obtain a transformation matrix from a standard map space to a rat individual Diffusion space; finally, the brain map in the standard space is converted into a Diffusion space of the rat individual by a nearest neighbor interpolation method, and the brain map converted into the Diffusion space of the rat individual is obtained;
step (5.2) extracting a region of interest: finding out parameters corresponding to the region of interest from Label description files corresponding to the rat standard brain atlas, and extracting the region of interest in the rat brain atlas transferred to the individual Diffusion space by using FSL software; setting the logical relation of the fiber bundle screening conditions between the interested areas as 'and', and screening out the fiber bundles which are simultaneously connected with the interested areas; the Track File is derived from the Track Vis software, from which the coordinate information of the specific nerve fiber is extracted.
The invention has the beneficial effects that:
in order to solve the defects in the prior art, the rat full brain nerve fiber simulation construction is realized based on the rat brain magnetic resonance DTI image and the T1 structure weighted image, and the simulation construction and index calculation of connecting different brain region nerve fibers can be realized.
Drawings
FIG. 1 is a flow chart of a three-dimensional reconstruction method of rat brain nerve fiber bundles based on DTI;
FIG. 2 is a statistical graph of the whole brain fiber information of the present invention;
FIG. 3 is a schematic representation of a nerve fiber bundle extracted from the simulation calculation of the present invention connecting frontal cortex of rat brain to hippocampus.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the three-dimensional reconstruction method of rat brain nerve fiber bundle based on DTI of the present invention comprises the following steps:
step (1): collecting a DTI image and a T1 weighted image of the head of the rat for subsequent image processing;
step (1.1) rat (Sprague-Dawley, SD) head DTI images and T1 weighted images were acquired using a Bruker 9.4T small animal magnetic resonance apparatus.
The specific scanning parameters of the step (1.2) are as follows: the repetition time is 3500ms, the echo time is 17.1ms, the flip angle is 90 degrees, the matrix size is 100 multiplied by 60 multiplied by 110, and the voxel size is 0.3 multiplied by 0.3mm 3 The gradient sensitivity factor b is 850s/mm 2 A total of 33 images were acquired, including 3 images with no gradient magnetic field applied (b=0 s/mm 2 3 repetitions) of b0 image and 30 images with non-coplanar diffusion gradient directions applied.
The step (2) of preprocessing the acquired rat head DTI image specifically comprises the following steps:
and (2.1) converting the rat head DTI original data directly obtained by the Bruker 9.4T small animal magnetic resonance instrument into nifti format from dicom format by using a dcm2niigui tool in MRIcron software, and simultaneously obtaining a bval file storing gradient sensitive factor b value information and a bvec file of gradient direction information.
Step (2.2) registers all images onto the b0 image of b=0 using the eddy_correction command in FSL-FDT when performing eddy current correction.
Step (2.3) uses the fdt_rotate_bvics command in the FSL-FDT to adjust the original gradient direction according to the result of the eddy current correction.
The step (3) is to obtain rat brain mask, which comprises the following steps:
step (3.1) extracting a brain image, importing a b0 image and modifying a threshold parameter, removing a non-brain tissue image from a whole brain image, and only leaving a rat brain part to form a rat brain mask; for the region which is not perfectly segmented by the algorithm, manually sketching the rat brain mask by utilizing software such as MRIcron and the like;
step (3.2) obtaining a DTI image after separating non-brain tissue using a fslmatits command according to the obtained rat brain mask.
Step (4): tensor calculation and full brain deterministic fiber tracking are carried out by utilizing the result of the step (3), and the method specifically comprises the following steps:
step (4.1): the contents of the gradient direction file bvec are transposed from row display to column display, and the gradient direction corresponding to the b0 image is not contained.
Step (4.2): using Diffusion Toolkit software, the b value and gradient direction bvec file are entered to calculate the value of the diffusion tensor FA.
Step (4.3): based on the result of step (4.2), the brain nerve fibers of the SD rats were continuously tracked by using Diffusion Toolkit software. The termination conditions were as follows: (1) Setting a fiber tracking range according to the rat brain mask calculated in the step (3); (2) setting the threshold of the diffusion tensor FA to 0.15-1.00; (3) the angle threshold is set to 45 °; (4) The minimum fiber length was 2mm and the maximum fiber length was 50mm.
Step (4.4): the tracking visualization and data analysis of the whole brain nerve fiber are realized through TrackVis software. The statistics of the total brain fiber information shows that the total brain has the longest fiber length of 34.24mm, and when the minimum fiber length is set to be 2mm, the total brain has 59383 bundles of nerve fibers, and the number of involved voxels is 75773.
Step (5): screening specific fibers and calculating fiber indexes, wherein the method specifically comprises the following steps:
step (5.1): image registration is performed: firstly registering a T1 weighted image of a rat individual to a rat individual Diffusion space by utilizing a flirt command in FSL software; then registering the rat T1 weighted image registered to the individual Diffusion space onto the standard map space image, and obtaining a transformation matrix; inverting the transformation matrix to obtain a transformation matrix from a standard map space to a rat individual Diffusion space; and finally, converting the brain map in the standard space into a rat individual Diffusion space by a nearest neighbor interpolation method to obtain the brain map converted into the rat individual Diffusion space.
Step (5.2): extraction of region of interest (ROI) is performed: and (3) finding out parameters corresponding to the region of interest from a Label description file corresponding to the quasi-map of the large mouse, and extracting the region of interest in the brain map transferred to the individual Diffusion space of the large mouse by utilizing the instruction fsimachs in the FSL, wherein the region of interest comprises a 86 hippocampal structure and a 77 frontal lobe united cortex. Taking rat forehead cortex and Hippocampus as examples, selecting a shallow forehead cortex from brain map as an ROI, introducing the ROI into software, and screening out all fiber bundles passing through the forehead cortex; selecting deep Hippocampus as another ROI, and performing the same steps as the previous steps; setting the logical relation of the fiber bundle screening condition between the two ROIs as 'and', and screening out the fiber bundles simultaneously connecting the prefrontal cortex and the hippocampus.
FIG. 2 shows the rat whole brain fiber distribution; fig. 3 shows the nerve fiber length distribution after whole brain tracking.
The above has been described in detail for the purpose of illustrating the method of the present invention and its core idea, so as to facilitate understanding of the present invention by those skilled in the art. It should be understood that the invention is not limited to the specific embodiments, but is capable of numerous modifications within the spirit and scope of the invention as hereinafter defined and defined by the appended claims as will be apparent to those skilled in the art all falling within the true spirit and scope of the invention as hereinafter claimed.
Claims (6)
1. A three-dimensional reconstruction method of rat brain nerve fiber bundles based on DTI is characterized by comprising the following steps: the method comprises the following steps:
step (1): collecting a DTI image and a T1 weighted image of the head of the rat;
step (2): preprocessing the acquired DTI image of the head of the rat, wherein the preprocessing comprises data format conversion, vortex correction and gradient direction correction;
step (3): forming a rat brain image mask and removing non-brain parts in the image by using the result of the preprocessing in the step (2) as input;
step (4): performing tensor calculation and full brain deterministic fiber tracking by using the result of the step (3) to obtain rat full brain fiber structure connection;
step (5): based on the step (4), different brain regions of the brain are defined as interested regions according to the brain map of the rat, and specific fiber screening and index calculation are carried out.
2. The DTI-based three-dimensional reconstruction method of rat brain nerve fiber bundles according to claim 1, wherein the method comprises the following steps: the step (1) specifically comprises the following steps:
step (1.1), acquiring a DTI image and a T1 weighted image of the head of the rat by using a small animal magnetic resonance instrument;
the specific scanning parameters of the step (1.2) are as follows: the repetition time is 3500ms, the echo time is 17.1ms, the flip angle is 90 degrees, the matrix size is 100 multiplied by 60 multiplied by 110, and the voxel size is 0.3 multiplied by 0.3mm 3 The gradient sensitivity factor b is 850s/mm 2 A total of 33 images were acquired, including 3 b0 images without applied gradient magnetic fields and 30 images with applied non-coplanar diffusion gradient directions; gradient sensitivity factor b=0s/mm of the b0 image 2 Repeated acquisitions were performed 3 times.
3. The DTI-based three-dimensional reconstruction method of rat brain nerve fiber bundles according to claim 2, wherein: the step (2) specifically comprises:
step (2.1) using MRIcron software to convert rat head DTI original data directly obtained by a small animal magnetic resonance instrument from a dicom format to a nifti format, and obtaining a bvec file storing a bval file of a gradient sensitive factor b and gradient direction information;
step (2.2) performing eddy current correction, registering the rat head DTI image and the T1 weighted image to a b0 image with a gradient sensitivity factor of b=0 so as to eliminate artifacts caused by head movement and eddy current in the image scanning process;
and (2.3) correcting the gradient direction, and adjusting the gradient direction of the original image according to the eddy current correction result.
4. A DTI-based three-dimensional reconstruction method of rat brain nerve fiber bundles as recited in claim 3, wherein: the step (3) specifically comprises:
step (3.1) extracting a rat brain image mask (mask), introducing a b0 image and modifying threshold parameters, removing non-brain tissue images (skull, fat and the like) from a whole brain image, and only leaving a rat brain tissue image to form the rat brain tissue mask; for the region which is not perfectly segmented by the algorithm, the brain mask of the rat can be manually sketched by utilizing software such as MRIcron and the like;
and (3.2) dividing the rat brain image according to the obtained rat brain mask to obtain a DTI image after separating non-brain tissues.
5. The DTI-based three-dimensional reconstruction method of rat brain nerve fiber bundles as recited in claim 4, wherein: the step (4) specifically comprises:
step (4.1) preprocessing before nerve fiber tracking, and transposing the content of the gradient direction file bvec from row display to column display, wherein the gradient direction file bvec does not contain the gradient direction corresponding to the b0 image;
step (4.2) adopting Diffusion Toolkit software, inputting b values and a gradient direction bvec file, estimating diffusion tensors by a linear least squares fitting method, and calculating corresponding diffusion tensors FA and scalar indexes in each voxel of the whole brain for connecting the whole brain fibers;
step (4.3) rebuilding nerve fiber connection of the whole brain by using Diffusion Toolkit software and a fiber continuous tracking algorithm, wherein the termination condition of fiber tracking is as follows: (1) Setting a fiber tracking range according to the rat brain mask calculated in the step (3); (2) the threshold of the diffusion tensor FA is set to 0.15-1.00; (3) the angle threshold is set to 45 °; (4) The minimum value of the fiber length is 2mm, and the maximum fiber length is 50mm;
and (4.4) realizing full brain nerve fiber tracking visualization and data analysis through TrackVis software.
6. The DTI-based three-dimensional reconstruction method of rat brain nerve fiber bundles as recited in claim 5, wherein: the step (5) specifically comprises:
step (5.1) image registration: firstly, registering the T1 weighted image to a rat individual diffration space by using FSL software; then registering the rat T1 weighted image registered to the individual Diffusion space to a large mouse quasi-map space image, and obtaining a transformation matrix; inverting the transformation matrix to obtain a transformation matrix from a large mouse quasi-graph space to a rat individual Diffusion space; finally, the brain map in the standard space is converted into a Diffusion space of the rat individual by a nearest neighbor interpolation method, and the brain map converted into the Diffusion space of the rat individual is obtained;
step (5.2) extracting a region of interest: finding out parameters corresponding to the region of interest from Label description files corresponding to the rat standard brain atlas, and extracting the region of interest in the rat brain atlas transferred to the individual Diffusion space by using FSL software; setting the logical relation of the fiber bundle screening conditions between the interested areas as 'and', and screening out the fiber bundles which are simultaneously connected with the interested areas; the TrackFile File is derived from TrackVis software, the coordinate information of specific nerve fibers is extracted from the TrackFile File, and index calculation such as fiber number, fiber length distribution and the like can be performed.
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