CN114983389B

CN114983389B - Quantitative evaluation method for human brain axon density based on magnetic resonance diffusion tensor imaging

Info

Publication number: CN114983389B
Application number: CN202210677684.3A
Authority: CN
Inventors: 姚君叶; 何宏建; 周子涵; 钟健晖
Original assignee: Zhejiang University ZJU
Current assignee: Zhejiang University ZJU
Priority date: 2022-06-15
Filing date: 2022-06-15
Publication date: 2023-01-10
Anticipated expiration: 2042-06-15
Also published as: JP2023183408A; CN114983389A

Abstract

The invention discloses a quantitative evaluation method for human brain axon density based on magnetic resonance diffusion tensor imaging. Performing data processing on magnetic resonance diffusion weighted image data obtained by scanning the brain of a human through magnetic resonance diffusion tensor imaging to calculate diffusion tensor images of a plurality of single diffusion sensitive factors and obtain a plurality of partial anisotropy index maps; and then, carrying out human brain axon density quantification processing according to the plurality of partial anisotropy index maps, and calculating the percentage difference of partial anisotropy indexes among different diffusion sensitive factors to obtain a human brain axon density quantification map. The method has good reliability, simple operation process and easy popularization, realizes the parameter quantification of axon density in white matter clinically feasible, and can be used for detecting axon loss related diseases.

Description

Quantitative evaluation method for human brain axon density based on magnetic resonance diffusion tensor imaging

Technical Field

The invention relates to a magnetic resonance imaging data processing method in the technical field of magnetic resonance imaging, in particular to a human brain axon density quantitative evaluation method based on magnetic resonance diffusion tensor imaging.

Background

Magnetic resonance Diffusion-weighted imaging (DWI) is the generation of images from the resulting data using a specific magnetic resonance imaging sequence, using water molecule Diffusion to produce contrast in the magnetic resonance image, the intensity of each voxel reflecting the best estimate of the water diffusivity at that location. Diffusion of water molecules in tissues is not free, but reflects interactions with many obstacles such as macromolecules, fibers and membranes. Thus, the water molecule diffusion pattern may reveal microscopic details of the tissue structure, whether normal or diseased. It allows non-invasive, in vivo mapping of the process of diffusion of molecules (mainly water) in biological tissues.

Diffusion Tensor Imaging (DTI) is a special magnetic resonance diffusion-weighted imaging technique, which reflects the microscopic movement of water molecules, and is a technique for studying the pathophysiological state of diseases from the cellular and molecular level, and the walking direction and the stereomorphology of nerve fibers and functional tracts in the white matter of the brain are obtained by tracking the walking of a neural pathway by using the direction information of anisotropic diffusion in a diffusion tensor field. Fractional anisotropy index (FA), which is the ratio of the anisotropic fraction of diffusion to the total value of the diffusion tensor, is used to estimate the anisotropy of diffusion. Is very important in exploring white matter nerve axons in the brain.

A neurite is an elongated part extending from the cell body of a nerve cell (neuron), and may be divided into a dendrite and an axon, which are used for information transmission between cells. The axon morphology is quantified according to the density and the directional distribution of the axons, and the axon morphology provides an opportunity for understanding the brain functional structure basis of normal people and brain dysfunction people.

At present, a noninvasive quantitative measurement method for human brain axon density has no gold standard. Neurite Orientation Dispersion and Density Imaging (NODDI) is an emerging imaging method based on magnetic resonance diffusion weighted imaging technology, which can be used for evaluating the complexity of neurite and dendrite microstructures so as to reflect the morphological information of nerve fibers, but the method needs many diffusion directions and has long imaging time; the model calculation is complex, which is not beneficial to clinical popularization.

Therefore, there is a need for a quantitative assessment method of axonal density in human brain which is noninvasive, reliable and convenient for clinical popularization.

Disclosure of Invention

The invention aims to provide a quantitative evaluation method for the axonal density of the human brain based on magnetic resonance diffusion tensor imaging, aiming at the defects of the prior art. The invention can carry out three-dimensional high-resolution imaging on the axon density within the clinically allowable time (12 minutes).

Axons are microscopic structures in human brain tissue, typically ranging in diameter from 0.2 to 20 microns, and it is difficult for magnetic resonance sequences to directly capture the density of axons due to resolution limitations. The method of the invention can solve the problem that axon imaging is difficult to directly carry out quantitative evaluation.

In order to achieve the purpose, the invention adopts the following technical scheme:

s1: performing data processing on magnetic resonance diffusion weighted image data obtained by scanning a human brain through magnetic resonance diffusion weighted imaging to calculate diffusion tensor imaging fitting of a plurality of single diffusion sensitive factors b, and obtaining a plurality of partial Anisotropy index maps (FA);

s2: and (4) carrying out human brain axon density quantification processing according to the plurality of partial anisotropy index maps, and calculating the percentage difference of the partial anisotropy indexes among different diffusion sensitive factors b to obtain a human brain axon density quantification map.

In the S1, when the magnetic resonance diffusion weighted imaging scanning is carried out, diffusion sensitivity factors b are 0 and b respectively _low 、b _high Under three conditions of (1), and the diffusion sensitivity factor b is 0 and b _low 、b _high Respectively collecting 1, A and A non-collinear directions, b _low Indicating a lower diffusion sensitivity factor, b _high Indicating a higher diffusion sensitivity factor and a indicating the acquisition number parameter for the non-collinearity diffusion sensitivity gradient direction.

And applying a diffusion sensitive gradient during magnetic resonance diffusion weighted imaging scanning, wherein the diffusion sensitive gradient is non-collinear, and the direction of the diffusion sensitive gradient is taken as the non-collinear direction.

The human brain is a living or isolated brain tissue.

Lower diffusion sensitivity factor b when it is a living brain tissue _low Set at 1000s/mm ² Higher diffusion sensitivity factor b _high Set as 3000s/mm ² 。

Lower diffusion sensitivity factor b when isolated brain tissue _low Set at 4000s/mm ² Higher diffusion sensitivity factor b _high Set as 8000s/mm ² 。

In the S1, a diffusion tensor fitting tool-dtifit tool in the data processing software FSL is adopted, weighted least square method weighted least square is selected, and a diffusion sensitive factor b is 0 and a diffusion sensitive factor b is b _low Performing diffusion tensor imaging fitting on the obtained magnetic resonance diffusion weighted image data (namely diffusion weighted imaging data) to obtain a partial anisotropy index map, wherein a diffusion sensitive factor b is 0 and a diffusion sensitive factor b is b _high Diffusion tensor imaging fitting is performed on the obtained magnetic resonance diffusion image data (i.e. diffusion weighted imaging data) to obtain another partial anisotropy index map.

The S2 human brain axon density quantitative diagram is obtained according to the following formula:

wherein pdFA represents a human brain axon density quantification map,

and FA _{b_high} Respectively represent lower diffusion sensitivity factor b _low And higher diffusion sensitivity factor b _high Correspondingly obtaining a partial anisotropy index map;

the magnetic resonance diffusion tensor imaging scan is specifically three-dimensional scanning imaging for the whole brain.

The magnetic resonance diffusion weighted image data are derived from three parts, namely axon space water, axon space water and free water, and have different diffusion coefficients and partial anisotropy indexes. For pure white signals, the magnetic resonance signal of the axon space water with a higher diffusion coefficient decays faster with the increase of the diffusion sensitivity factor b, and the weight of the axon space is increased in the diffusion weighted imaging result.

The invention quantitatively reflects the change of the axon density by calculating the percentage difference value of the anisotropy indexes, can directly capture the axon density, accurately obtains the result of the axon density of a human brain, and solves the problem that the axon imaging is difficult to directly carry out quantitative evaluation.

The source of the magnetic resonance diffusion weighted image data is obtained according to the following formula:

f _in +f _en +f _iso ＝1

wherein S and S ₀ Weighting the image domain signal intensity, f, of each voxel in the MR diffusion weighted image data with and without the MR diffusion sensitive gradient applied, respectively _in 、f _en 、f _iso The volume fractions of the axon space, the axon space and the free water component are respectively, and the sum of the volume fractions of the three components is 1,D _in 、D _en 、D _is The diffusion coefficients of the intra-axonal space, the extra-axonal space, and the free water component, respectively, e represents a natural constant, and INVF represents the axon density, i.e., the axon internal volume fraction.

The invention has the beneficial effects that:

the method adopts the conventional clinical magnetic resonance diffusion weighting sequence to realize the quantitative evaluation of the three-dimensional axon density, and the acquisition and reconstruction of the image are based on the magnetic resonance diffusion tensor imaging, so that the three-dimensional quantitative evaluation of the axon density in the white matter of the brain can be quickly and accurately carried out within the clinical feasible time.

The method has good reliability, simple operation process and easy popularization, realizes the parameter quantification of axon density in white matter clinically feasible, and can be used for detecting axon loss related diseases.

Drawings

FIG. 1 shows an example of the application of pdFA (partial anisotropy index percent difference) in 6 regions of Interest (ROI) of white matter of healthy human brain to be tested, which is an axon density quantitative evaluation index of the invention. The ordinate is the human brain axon Density quantification plot obtained using scheme 3, pdFA, and the abscissa is the axon Density Index (neuron Density Index, NDI) NDI-NODDI obtained using Neurite direction dispersion and Density imaging (NODDI). In the figure, the light lines and scattered points are data results with an average frequency of 1, and the black lines and scattered points are data results with an average frequency of 2, which represent lower and higher Signal-to-Noise Ratio (SNR) conditions, respectively. The 6 ROIs are the corpus callosum knee (GCC), corpus callosum middle (body of the corpus callosum, BCC), corpus callosum compression (spelium of the corpus callosum, SCC), inner bursal limb (ALIC), inner bursal limb (PLIC), superior longitudinal bundle (SLF), respectively.

FIG. 2 is an axon density quantitative evaluation index of the invention, an application example of a human brain axon density quantitative graph pdFA in vitro human brain callus tissue. FIG. 2 (a) is a graph with the ordinate of pdFA obtained from different sections of a corpus callosum of ex vivo human brain tissue obtained by the present invention and the abscissa of axon density index obtained from analysis using paraffin section neurofibrillary-Vermilisky (Bielschowsky) silver staining technique; FIG. 2 (b) shows the corresponding position of the slice on the MRI image; panel (c) is a microscopic image of the staining results of the sections, the stained parts are axons, and the ratio of the area of the stained parts to the total area is defined as NDI-Histology as a gold standard for quantitative assessment of the axon density of the ex vivo human brain.

FIG. 3 is a Pearson correlation analysis of the pdFA and INVF in the axon density quantitative graph of human brain under different SNR conditions in the simulation experiment. The ordinate is a human brain axon density quantitative graph pdFA calculated using a magnetic resonance signal obtained according to a simulation signal formula, and the abscissa is an axon internal volume fraction set in a simulation experiment.

Table 1 is an in-vivo human brain data acquisition sequence parameter table;

table 2 is a parameter table of different diffusion sensitivity factor b combination schemes;

table 3 is a table of statistical parameters of implementation results of different combinations of diffusion sensitive factors b;

table 4 is an in vitro human brain data acquisition sequence parameter table;

table 5 is a simulation experiment parameter setting table.

Detailed Description

The embodiments of the present invention will be further explained with reference to the drawings.

The embodiments of the invention and the specific implementation thereof are as follows:

in the first embodiment of the invention, 61 healthy subjects were subjected to mr diffusion weighted imaging using a 3.0T mr apparatus to obtain mr diffusion weighted image data. The scan duration of the sequence was 19 minutes, and the other parameters of the sequence are shown in table 1.

TABLE 1

And (3) selecting a weighted least square method by using a diffusion tensor fitting tool in the FSL, and performing diffusion tensor imaging fitting on the magnetic resonance diffusion weighted image data of the single diffusion sensitive factor b with the average times of 1 (lower signal-to-noise ratio) and 2 (higher signal-to-noise ratio) respectively to obtain a partial anisotropy index diagram corresponding to 3 diffusion sensitive factors b.

As an index for quantitative evaluation of human brain axonal density based on diffusion tensor imaging, a corresponding human brain axonal density quantitative map was calculated using the obtained partial anisotropy index map following the following formula:

as shown in Table 2, there are 3 combinations (schemes 1-3).

TABLE 2

Neurite outgrowth direction dispersion and Density imaging (NODDI) model fitting was performed using the complete data set collected with the sequences of Table 1 and the resulting axonal Density Index (NDI-NODDI) was calculated as the gold standard for the evaluation of axonal Density in human brain in vivo. In the selected white matter region of interest, the human brain axon density quantitative map pdFA obtained above and the axon density index NDI-NODDI were analyzed for Pearson Correlation Coefficient (CC), and the results are shown in FIG. 1. The statistical parameter values corrected by the multiple test were calculated as corrected P values, and the results are shown in table 3, with the gray cells representing the data set with an average number of 2. Both FIG. 1 and Table 3 show that the quantitative map of human brain axon density obtained using the data set with an average number of 2 (higher SNR) has a higher correlation between pdFA and the axon density index NDI-NODDI, which more accurately reflects the human brain axon density.

As shown in table 3, in the present invention, the diffusion sensitive factor b combination of scheme 3 is the most preferred, and the correlation between the human brain axon density quantification map pdFA and the axon density index NDI-NODDI is the highest. The time required for this protocol was 12 minutes.

TABLE 3

**adjusted P<0.01,***adjusted P<0.001

In a second embodiment of the present invention, a 3.0T magnetic resonance apparatus is used to perform a magnetic resonance diffusion-weighted imaging scan on 1 isolated brain fixed in formalin for 4 weeks to obtain magnetic resonance diffusion-weighted image data. Other parameters of the sequence are shown in table 4.

TABLE 4

Parameter(s)	Value/range
		TR	9620ms
TE	96/118ms
		Diffusion time (. DELTA.)	46.3ms
Gradient duration (delta)	35.7ms
		Diffusion factor (b)	0/4000/8000s/mm ²
Number of directions	30/30
		Resolution ratio	1.8×1.8×1.8mm ³
Number of times of averaging	2
		Time of scan	48 minutes

Using the "diffusion tensor fitting" tool in FSL, selecting weighted least squares with sensitivity factor b to diffusion of 4000 and 8000s/mm ² Performing diffusion tensor imaging fitting on the magnetic resonance diffusion weighted image data to obtain a partial anisotropy index map FA _b4000 And FA _b8000 。

As an index for quantitative evaluation of ex vivo human brain axon density based on diffusion tensor imaging, FA obtained was used following the following formula _b4000 And FA _b8000 Calculating a human brain axon density quantitative graph (pdFA):

after completion of the magnetic resonance scan, the body half brain specimen was cut into 5mm thick coronal sections. This procedure produces 6 slices whose positions correspond in the sagittal magnetic resonance image, as shown in figure 2 (b). The callus tissue blocks were removed and paraffin embedding was performed to obtain 6 corresponding sections with a thickness of 6 μm. Neurite histochemical staining was performed on the tissue sections using paraffin section neurofibraucholski (Bielschowsky) silver staining technique, as shown in fig. 2 (c). Axonal density indicators (NDI-Histology) of the callus were quantified on stained sections using Image-J software.

Pearson correlation analysis was performed on the human axon density quantitative map of ex-vivo human brain sections, pdFA, and NDI-Histology, an axon density quantitative evaluation index obtained by histochemical staining, and the results are shown in FIG. 2 (a), which show a high linear correlation. The invention also has good performance on the quantitative evaluation of the axon density of the isolated human brain specimen.

In order to study the principle of quantitative evaluation of axon density by pdFA, which is a quantitative evaluation index of axon density in human brain. The invention carries out simulation experiments based on white matter 'standard model'.

The intra-axonal space, the extra-axonal space having water with diffusion anisotropy, the intra-axonal space having an axial diffusion coefficient of

Expressed as radial diffusion coefficient

And (4) showing. Similarly, the axial diffusion coefficient of the space outside the axon

Express, radial diffusion coefficient to express

The diffusion of free water has diffusion isotropy and no directionality, and is represented by D _iso And (4) showing.

The range of the signal to noise ratio is the range of the in-vivo experimental data.

Table 5 lists the specific parametric characteristics involved in the simulation experiments.

TABLE 5

As shown in FIG. 3, in the signal-to-noise ratio range (20-40) of the in-vivo experimental data, the value of the pdFA of the axon density quantitative evaluation index human brain axon density quantitative map linearly increases with the increase of the volume fraction INVF in the axon representing the axon density quantitative evaluation index, and the two have high linear correlation. Simulation experiments show that the quantitative evaluation index of axon density, pdFA, can sensitively detect the change of the axon density of human brain under the data quality which can be achieved under clinical conditions.

Compared with the axon density index (NDI-NODDI) obtained by fitting a neurite direction dispersion and density imaging (NODDI) model, the calculation process of the partial anisotropy index percentage difference value (pdFA) based on diffusion tensor imaging needs less time. To be used in a Linux workstation (2 multiplied by 2.80 GHz)

20-core processor, 252GB memory) for data processing, in the case of inputting the same data, it takes about 10 hours to perform quantitative evaluation of human brain whole brain axon density using neurite direction dispersion and density imaging model fitting; quantitative assessment of axonal density using the pdFA of the magnetic resonance diffusion tensor imaging-based fractional anisotropy index percentage difference of the invention requires only about 3 minutes of computation time.

It should be noted that the above description is only an embodiment of the present invention and the technical principles applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments illustrated, and that various changes and modifications may be apparent to those skilled in the art without departing from the scope of the invention. Thus, although by the above embodiments further equivalent embodiments may be included without departing from the inventive concept, the scope of the invention is to be determined by the scope of the appended claims.

Claims

1. A quantitative evaluation method for human brain axon density based on magnetic resonance diffusion tensor imaging is characterized by comprising the following steps:

s1: performing data processing on magnetic resonance diffusion weighted image data obtained by scanning the human brain through magnetic resonance diffusion weighted imaging to calculate diffusion tensor imaging fitting of a plurality of single diffusion sensitive factors b, and obtaining two partial anisotropy index maps;

s2: performing human brain axon density quantification processing according to the two partial anisotropy index maps, and calculating the percentage difference of the partial anisotropy indexes between two different diffusion sensitive factors b to obtain a human brain axon density quantification map;

wherein pdFA represents a human brain axon density quantification map, FA _{b_low} And FA _{b_high} Respectively represent lower diffusion sensitivity factor b _low And higher diffusion sensitivity factor b _high Corresponding to the obtained partial anisotropy index map.

2. The method for quantitatively evaluating the human brain axon density based on the magnetic resonance diffusion tensor imaging according to the claim 1, characterized in that: in S1, when the magnetic resonance diffusion weighted imaging is scanned, diffusion sensitivity factors b are 0 and b respectively _low 、b _high Under three conditions of (1), and the diffusion sensitivity factor b is 0 and b _low 、b _high Respectively collecting 1, A and A non-collinear directions, b _low Indicating a lower diffusion sensitivity factor, b _high Indicating a higher diffusion sensitivity factor, and a indicating the acquisition number parameter in the direction of the non-collinear diffusion sensitivity gradient.

3. The method for quantitatively evaluating the human brain axon density based on the magnetic resonance diffusion tensor imaging according to the claim 1, characterized in that: in the S1, a diffusion tensor fitting tool in data processing software FSL is adopted, a weighted least square method is selected, and a diffusion sensitive factor b is 0 and the diffusion sensitive factor b is b _low Performing diffusion tensor imaging fitting on the obtained magnetic resonance diffusion weighted image data to obtain a partial anisotropy index map, wherein a diffusion sensitive factor b is 0 and a diffusion sensitive factor b is b _high Diffusing the acquired magnetic resonance diffusion image dataTensor imaging fitting obtains another partial anisotropy index map.

4. The method for quantitatively evaluating the axonal density of the human brain based on the magnetic resonance diffusion tensor imaging according to the claim 1, which is characterized in that: the source of the magnetic resonance diffusion weighted image data is obtained according to the following formula:

f _in +f _en +f _iso ＝1

wherein S and S ₀ Weighting the image domain signal intensity, f, of each voxel in the MR diffusion weighted image data with and without the MR diffusion sensitive gradient applied, respectively _in 、f _en 、f _iso The volume fractions of the axon internal space, the axon external space and the free water component are respectively, and the sum of the volume fractions of the three components is 1,D _in 、D _en 、D _iso The diffusion coefficients for the axonal space, and the free water component, respectively, e represents the natural constant, and INVF represents the axonal density, i.e., the axonal internal volume fraction.