CN114646913B - Non-invasive measurement method for microstructure of biological tissue - Google Patents
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Abstract
A method for non-invasive measurement of biological tissue microstructure comprising: I. performing magnetic resonance diffusion weighted imaging on biological tissues; II. Measuring the actual image signal intensity of each voxel point in each magnetic resonance image; III, substituting the actual image signal intensity of the integral pixel into a formula to solve; IV, judging whether the voxel point is of a single-chamber structure, a double-chamber structure or a three-chamber structure according to the solving result; v, judging the tissue type in the voxel point; and VI, repeating the steps III to V to analyze other voxel points until all voxel points are analyzed. The method is capable of measuring the microstructure of biological tissue.
Description
Technical Field
The invention relates to a method for measuring a biological tissue microstructure, in particular to a noninvasive measuring method for measuring the biological tissue microstructure through magnetic resonance imaging.
Background
Magnetic resonance (magnetic resonance imaging, MRI) has been widely used for imaging biological tissue due to its non-ionizing damage, high soft tissue resolution, and sensitivity to chemical components.
Currently, there is a method for solving the volume ratio of cells, the volume ratio of blood vessels, the volume ratio of extracellular space, the diffusion coefficient of cells, the diffusion coefficient of blood vessels, and the diffusion coefficient of extracellular space at each voxel point of a biological tissue from the actual image signal intensity of each voxel point in a magnetic resonance image. In the solving process, each voxel point is assumed to be composed of cells, blood vessels and extracellular spaces, the actual structure of biological tissues is not met, and the problem that some voxel points cannot be solved exists.
Disclosure of Invention
The invention aims to provide a noninvasive measuring method of microstructure of biological tissue, which can measure microstructure of biological tissue.
The invention provides a noninvasive measurement method of a microstructure of biological tissue, which comprises the following steps:
I. performing magnetic resonance diffusion weighted imaging on biological tissues to obtain weights b respectively corresponding to different weights i Wherein i=1, 2, 3..n, n.gtoreq.6;
II. Measuring the actual image signal intensity S (b) of each voxel point in each magnetic resonance image i )；
III, taking any integral pixel point in the magnetic resonance image, and obtaining the actual image signal intensity S (b i ) Substituting the following equations 1 to 3 to solve D, V in the equation 21 、V 22 、D 21 、D 22 、V 31 、V 32 、V 33 、D 31 、D 32 And D 33 ，
Single chamber model:
double-chamber model:
three-chamber model:
wherein: s (b) 0 ) For the signal intensity of the voxel point when no diffusion sensitive gradient pulse is applied, D is the diffusion coefficient of a single-chamber structure, V 21 The volume ratio of the first chamber of the double-chamber structure, V 22 The volume ratio of the second chamber of the double-chamber structure, D 21 Diffusion coefficient of the first chamber of the double-chamber structure, D 22 Diffusion coefficient of the second chamber of the double-chamber structure, V 31 The volume ratio of the first chamber of the three-chamber structure, V 32 The volume ratio of the second chamber of the three-chamber structure, V 33 The volume ratio of the third chamber of the three-chamber structure, D 31 Diffusion coefficient of the first chamber of the three-chamber structure, D 32 Diffusion coefficient of the second chamber of the three-chamber structure, D 33 The diffusion coefficient of the third chamber, which is a three-chamber structure;
IV, judging whether the voxel point is of a single-chamber structure, a double-chamber structure or a three-chamber structure according to the solving result of the step III;
v, if the voxel point is judged to be of a single-chamber structure in the step IV, judging the tissue type of the single-chamber structure according to the step D; the tissue types include cells, blood vessels, and extracellular spaces; the volume ratio and diffusion coefficient of the tissue type not contained in the single-chamber structure are set to 0. If the voxel point is judged to be of a dual-chamber structure in step IV, then the method is performed according to D 21 And D 22 Judging the tissue types of the first chamber and the second chamber of the double-chamber structure; the tissue types include cells, blood vessels, and extracellular spaces; the volume ratio and diffusion coefficient of the tissue type not contained in the dual chamber structure are set to 0. If the voxel point is judged to be of a three-compartment structure in step IV, then the method is performed according to D 31 、D 32 And D 33 Judging the tissue types of the first chamber, the second chamber and the third chamber of the three-chamber structure; the tissue types include cells, blood vessels, and extracellular spaces.
And VI, repeating the step III to the step V to analyze other voxel points until all voxel point analysis is completed.
The non-invasive measurement method of the microstructure of the biological tissue comprises the steps of judging whether each voxel point of the biological tissue belongs to a single-chamber structure, a double-chamber structure or a three-chamber structure, and determining the tissue type of each voxel point based on a judgment result, so that the microstructure of the biological tissue is measured.
In another exemplary embodiment of the non-invasive measurement method for microstructure of biological tissue, in the step IV, the determination of the voxel point as a single-chamber structure, a double-chamber structure or a three-chamber structure according to the result of the solution in the step III is specifically:
if it is
And is also provided with
Then the voxel point is a single-cell structure.
If it is
And is also provided with
Then the voxel point is a dual-chambered structure. Otherwise, the voxel point is in a three-chamber structure. The judging method is simple.
In yet another exemplary embodiment of the method for non-invasive measurement of a microstructure of a biological tissue, after step VI, further comprises: VII, reconstructing a magnetic resonance image according to any one or more of the volume ratio of cells, the volume ratio of blood vessels, the volume ratio of extracellular space, the diffusion coefficient of cells, the diffusion coefficient of blood vessels and the diffusion coefficient of extracellular space of each voxel point. To facilitate a more visual reflection of the structure of biological tissue.
In yet another exemplary embodiment of the non-invasive measurement method of biological tissue microstructure, the solution method in step III employs constrained nonlinear optimization solution; wherein,,
the optimization formula of formula 1 is:
s.t.D≥0
the optimization formula of formula 2 is:
the optimization formula of formula 3 is:
. Thereby more accurately measuring the microstructure of biological tissue.
In yet another exemplary embodiment of the non-invasive measurement method of biological tissue microstructure, the constrained nonlinear optimization solution employs a Lagrangian multiplier method.
In yet another exemplary embodiment of the method for non-invasive measurement of a microstructure of a biological tissue, after step VI, further comprises: VIII, calculating the cellulose trend distribution of the cells, the blood vessels and the extracellular space according to the diffusion coefficient of the cells, the diffusion coefficient of the blood vessels and the diffusion coefficient of the extracellular space of each voxel point. To facilitate a more visual reflection of the diffuse nature of biological tissue.
In yet another exemplary embodiment of the non-invasive measurement method of the microstructure of the biological tissue, the calculation is performed using a statistical method or a main diffusion direction based tracking method.
Drawings
The following drawings are only illustrative of the invention and do not limit the scope of the invention.
Fig. 1 and 2 are magnetic resonance images of brain tissue.
FIG. 3 is a plot of basal ganglia cell fiber bundle trend.
FIG. 4 is a graph showing the trend of cell fiber bundles on both sides of callus.
FIG. 5 is a plot of tumor and healthy side cell fiber bundle trend profile.
Fig. 6 is a magnetic resonance image of brain tissue.
Fig. 7 is a schematic diagram after reconstitution.
Detailed Description
For a clearer understanding of technical features, objects, and effects of the present invention, a specific embodiment of the present invention will be described with reference to the accompanying drawings.
In this document, "schematic" means "serving as an example, instance, or illustration," and any illustrations, embodiments described herein as "schematic" should not be construed as a more preferred or advantageous solution.
Herein, "first", "second", etc. do not indicate the degree of importance or order thereof, etc., but merely indicate distinction from each other to facilitate description of documents.
Here, the object of each embodiment of the noninvasive measurement method of the microstructure of the biological tissue is brain tissue, but the method is not limited to brain tissue, and may be applied to other biological tissues.
According to the recent studies of the present inventors, it was found that the distribution of the extracellular space in brain tissue is heterogeneous, and in a partial brain region (for example, an inner capsule region), a dense fiber bundle structure makes the volume ratio of the extracellular space in the region 0, and only a cell structure and a blood vessel structure exist in a ventricle region. In the actual structure of biological tissue, for a voxel point, the tissue structure may be one, two or three of cells, blood vessels and extracellular space.
In order to measure the microstructure of biological tissue, the present invention provides a non-invasive measurement method of brain tissue microstructure, which in one exemplary embodiment thereof comprises the following steps.
Step I: using a plurality of different weights b i Performing magnetic resonance diffusion weighted imaging on the measurement interest region of the brain tissue to obtain weights b respectively corresponding to different weights i Wherein i=1, 2, 3..n, n.gtoreq.6. In the illustrative embodiment, the weight b i For vectors with direction, multiple different weights b can be obtained by changing the modulus i That is, this step results in at least six magnetic resonance images of the region of interest of the measurement for the same brain tissue, and the weights b of the respective magnetic resonance images i Is the same and the modulus is different. In the illustrative embodiment, the weight b i The direction of (2) is, for example, laboratory [1,0 ]]Direction or laboratory [0.446,0.895,0 ]]Of course, in the exemplary embodiment, other directions may be provided as desired. In the illustrative embodiment, the weight b can also be changed by i Obtain a plurality of different weights b i I.e. the present step yields at least six magnetic resonance images of the region of interest of the measurement for the same brain tissue, and the weights b of the respective magnetic resonance images i For example, when n=6, the weight b i The directions of (1), (0) and (0)]，[0.446，0.895，0]，[0.447，0.275，0.851]，[0.448，-0.723，-0.525]，[0.447，-0.724，0.526]，[-0.449，-0.277，0.850]。
Step II: measuring the actual image signal intensity S (b) of each voxel point in each magnetic resonance image obtained in step I i ) And the actual image signal strength S (b i ) The gray scale of the voxel point in the image is reflected in the magnetic resonance image.
Step III: the magnetic resonance image is integratedA pixel point whose actual image signal intensity S (b i ) Substituting the following formulas 1 to 3 and solving D, V in the formulas 21 、V 22 、D 21 、D 22 、V 31 、V 32 、V 33 、D 31 、D 32 And D 33 。
Single chamber model:
double-chamber model:
three-chamber model:
wherein S (b) 0 ) For the signal intensity of the voxel point when no diffusion sensitive gradient pulse is applied, D is the diffusion coefficient of a single-chamber structure, V 21 The volume ratio of the first chamber of the double-chamber structure, V 22 The volume ratio of the second chamber of the double-chamber structure, D 21 Diffusion coefficient of the first chamber of the double-chamber structure, D 22 Diffusion coefficient of the second chamber of the double-chamber structure, V 31 The volume ratio of the first chamber of the three-chamber structure, V 32 The volume ratio of the second chamber of the three-chamber structure, V 33 The volume ratio of the third chamber of the three-chamber structure, D 31 Diffusion coefficient of the first chamber of the three-chamber structure, D 32 Diffusion coefficient of the second chamber of the three-chamber structure, D 33 Diffusion coefficient of the third chamber, which is a three-chamber structure. The volume ratio refers to the percentage of each chamber to the total volume of the voxel point, and the diffusion coefficient refers to the movement speed of water molecules in each chamber.
In the above solving process, when the number of unknowns is less than the number of effective equations, an overdetermined equation is used to solve the optimal solution. For exampleIf the integral pixel point is larger than one corresponding weight b i Actual image signal intensity S (b) i ) Substituting equation 1 can result in a number of equations greater than one effective; if a voxel point is greater than four corresponding weights b i Actual image signal intensity S (b) i ) Substituting equation 2 can result in a number of equations greater than four valid; if the integral pixel point is greater than six corresponding weights b i Actual image signal intensity S (b) i ) Substituting equation 3 may yield a number of equations greater than six. In this case, an overdetermined equation is used to find the optimal solution. It will be appreciated that the greater the number of equations available, the greater the accuracy of the solution.
In the present exemplary embodiment, the above solution employs a constrained nonlinear optimization solution, wherein,
the optimization formula of formula 1 is:
s.t.D 0
The optimization formula of formula 2 is:
the optimization formula of formula 3 is:
in this step, the voxel point is calculated assuming a single-chamber structure (i.e., including one of a cell, a blood vessel, and an extracellular space), a double-chamber structure (i.e., including two of a cell, a blood vessel, and an extracellular space), and a triple-chamber structure (i.e., including a cell, a blood vessel, and an extracellular space), respectively. In the present exemplary embodiment, the constrained nonlinear optimization solution employs a Lagrangian multiplier method. But is not limited thereto, in other exemplary embodiments, other constrained nonlinear optimization solving methods may be employed, such as conjugate gradient method, steepest descent method, multidimensional newton method, and the like.
Step IV: and D, judging whether the voxel point is of a single-chamber structure, a double-chamber structure or a three-chamber structure according to the solving result of the step III. In the present exemplary embodiment, specifically:
if it is
And is also provided with
Then the voxel point is a single-cell structure.
If it is
And is also provided with
Then the voxel point is a dual-chambered structure. Otherwise, the voxel point is in a three-chamber structure. Where e is an empirical parameter that varies depending on the nuclear magnetic device and the tissue being scanned, for example, scanning brain tissue based on a GE nmr machine, e may be 0.1. The determination of e is based on anatomical results. The actual distribution of cells, blood vessels and extracellular spaces in the biological tissue is determined according to anatomy, the steps I to IV are carried out on the same biological tissue, and the appropriate epsilon is selected so that the judgment result is consistent with the anatomy result, thereby determining the experience value epsilon. In other illustrative embodiments, the e may also be obtained using a machine learning method from a large amount of annotation data. But is not limited thereto. In the exemplary embodiment, the above determination may be instead performed based on a method such as machine learning.
Step V: if the voxel point is judged to be a single-chamber structure in the step IV, judging the tissue type of the single-chamber structure according to the D; the volume ratio and diffusion coefficient of the tissue type not contained in the single-chamber structure are set to 0. If the voxel point is judged to be of a dual-chamber structure in step IV, then the method is performed according to D 21 And D 22 Judging the tissue types of the first chamber and the second chamber of the double-chamber structure; the volume ratio and diffusion coefficient of the tissue type not contained in the dual chamber structure are set to 0. If the voxel point is judged to be of a three-compartment structure in step IV, then the method is performed according to D 31 、D 32 And D 33 The tissue type of the first, second and third chambers of the three-chamber structure is determined. The tissue types described above include cells, blood vessels, and extracellular spaces. In the present exemplary embodiment, for example, based on anatomical results, by D, D 21 、D 22 、D g1 、D 32 And D 33 Is used to determine the tissue type. Specifically, the actual distribution situation of cells, blood vessels and extracellular spaces in biological tissues is determined according to anatomy, the steps I to IV are carried out on the same biological tissues, and D values of voxel points of the single-chamber structure of the cells, the single-chamber structure of the blood vessels and the single-chamber structure of the extracellular spaces determined in the anatomy result are used as the basis for judging the corresponding tissue types respectively.
Step VI: and (3) repeating the steps III to V to analyze other voxel points until all voxel point analysis is completed.
Step VII: reconstructing a magnetic resonance image from any one or more of the volume ratio of cells, the volume ratio of blood vessels, the volume ratio of extracellular space, the diffusion coefficient of cells, the diffusion coefficient of blood vessels, and the diffusion coefficient of extracellular space at each voxel point. By displaying the reconstructed image, the structure of the biological tissue is more intuitively reflected.
Step VIII: the cellulose trend distribution of the cells, the blood vessels and the extracellular space is calculated by the diffusion coefficient of the cells, the diffusion coefficient of the blood vessels and the diffusion coefficient of the extracellular space of each voxel point. The calculation is performed using a statistical method or a main diffusion direction-based tracking method, and in the present exemplary embodiment, a recursive method described in chinese patent application No. CN201510082396.3 is used, for example. However, the present invention is not limited to this, and in other exemplary embodiments, other methods may be used for estimation.
The following are experimental results obtained by the non-invasive measurement method of the microstructure of biological tissue according to the present exemplary embodiment. Wherein the values of N are all 6, and the values of E are all 0.1. Magnetic resonance apparatus produced by GE company, weight b i The directions of (1), (0) and (0)]，[0.446，0.895，0]，[0.447，0.275，0.851]，[0.448，-0.723，-0.525]，[0.447，-0.724，0.526]，[-0.449，-0.277，0.850]。
Fig. 1 is a magnetic resonance image of brain tissue, wherein the rectangular region is the center of a semicircle to be analyzed. Table 1 shows the volume ratios and diffusion coefficients of the cells, blood vessels and extracellular spaces in the center of the semicircle obtained by the non-invasive measurement method of the microstructure of the biological tissue. Wherein V is vas V is the volume ratio of the blood vessels ecs V as the volume ratio of the extracellular space cell Is the volume ratio of cells, D vas Is the diffusion coefficient of blood vessel, D ecs Is the diffusion coefficient of extracellular space, D cell Is the diffusion coefficient of the cell.
TABLE 1 volume ratios and diffusion coefficients of cells, vessels and extracellular spaces in the semi-oval center of brain tissue
| Type(s) | Maximum value | Minimum value | Median of | Standard deviation of | Number of pixels |
| V vas (％) | 18 | 0.67 | 7.3 | 0.091 | 56 |
| V ecs (％) | 40 | 0.75 | 12 | 0.74 | 56 |
| V cell (％) | 94 | 56 | 79 | 0.73 | 56 |
| D vas (*10 -6 mm 2 /s) | 2.2 | 0.28 | 0.49 | 0.23 | 56 |
| D ecs (*10 -6 mm 2 /s) | 0.71 | 0.32 | 0.52 | 0.0084 | 56 |
| D cell (*10 -6 mm 2 /s) | 0.004 | 0.0016 | 0.0023 | 0.00000026 | 56 |
Fig. 2 is a magnetic resonance image of brain tissue, wherein the rectangular region is the callus area to be analyzed. Table 1 shows the volume ratios and diffusion coefficients of cells, blood vessels and extracellular spaces of the callus area obtained by the non-invasive measurement method of the microstructure of biological tissue described above.
TABLE 2 volume ratio and diffusion coefficient of cells, vessels and extracellular spaces of callus areas of brain tissue
| Type(s) | Maximum value | Minimum value | Median of | Standard deviation of | Number of pixels |
| V vas (％) | 20 | 3.5 | 11 | 0.2 | 39 |
| V ecs (％) | 38 | 3.7 | 16 | 0.68 | 39 |
| V cell (％) | 89 | 53 | 70 | 0.87 | 39 |
| D vas (*10 -6 mm 2 /s) | 2.6 | 0.25 | 0.37 | 0.55 | 39 |
| D ecs (*10 -6 mm 2 /s) | 1.3 | 0.21 | 0.43 | 0.035 | 39 |
| D cell (*10 -6 mm 2 /s) | 0.0041 | 0.0012 | 0.0019 | 0.00000047 | 39 |
Fig. 3 to 5 are fiber bundle trend profiles obtained by the non-invasive measurement method of the microstructure of biological tissue described above. Wherein FIG. 3 shows the cellular fiber bundle trend distribution of basal ganglia, including transverse, coronal, sagittal, and magnified views. FIG. 4 shows the distribution of cell fiber bundles on both sides of the corpus callosum, including transection, coronal, sagittal, and magnified views. FIG. 5 shows the distribution of tumor and healthy side cell fiber bundles including transection, coronal, sagittal, and magnified views.
The non-invasive measurement method of the microstructure of the biological tissue according to the present exemplary embodiment obtains the volume ratios of cells, blood vessels and extracellular spaces of brain tissue of a normal population as follows:
frontal cortex: the volume ratio of cells is about 90%;
semi-oval center: the volume ratio of cells is about 80-85%, and the volume ratio of extracellular space to blood vessel is about 1:1;
base section: the volume ratio of the extracellular space is about 15%, which is consistent with the 15-20% ratio reported in the prior literature.
Fig. 6 shows a magnetic resonance image of brain tissue of a patient. As can be seen in fig. 6, the patient has malignant edema in the right hemisphere (i.e., left side of the drawing). Fig. 7 is a schematic representation of a reconstruction of fig. 6 by a non-invasive measurement method of the microstructure of biological tissue as described above. In the upper three images in fig. 7, the diffusion coefficient of cells, the diffusion coefficient of blood vessels, and the diffusion coefficient of extracellular space are shown from left to right, respectively; in the lower three images, the volume ratio of cells, the volume ratio of blood vessels, and the volume ratio of extracellular space are shown from left to right, respectively.
The noninvasive measuring method of the microstructure of the biological tissue is characterized in that each voxel point of the biological tissue is judged to belong to a single-chamber structure, a double-chamber structure or a three-chamber structure, and the tissue type of each voxel point is determined based on the judging result, so that the method is more in line with the actual structural characteristics of the biological tissue, and the microstructure of the biological tissue is measured.
It should be understood that although the present disclosure has been described in terms of various embodiments, not every embodiment is provided with a separate technical solution, and this description is for clarity only, and those skilled in the art should consider the disclosure as a whole, and the technical solutions in the various embodiments may be combined appropriately to form other embodiments that will be understood by those skilled in the art.
The above list of detailed descriptions is only specific to practical examples of the present invention, and they are not intended to limit the scope of the present invention, and all equivalent embodiments or modifications, such as combinations, divisions or repetitions of features, without departing from the technical spirit of the present invention are included in the scope of the present invention.
Claims (7)
1. A method for non-invasive measurement of a microstructure of biological tissue, comprising:
i, performing magnetic resonance diffusion weighted imaging on biological tissues to obtain weights respectively corresponding to the biological tissues
Is defined, wherein +.>
；
II, measuring the actual image signal intensity of each voxel point in each magnetic resonance image
；
III, taking any integral pixel point from the magnetic resonance image, and obtaining the actual image signal intensity of the pixel point
Substituting the following formulas 1 to 3 to solve +.>
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、
And->
，
Single chamber model:
double-chamber model:
three-chamber model:
wherein:
IV, judging whether the voxel point is of a single-chamber structure, a double-chamber structure or a three-chamber structure according to the solving result of the step III;
v, if the voxel point is judged to be of a single-chamber structure in the step IV, the method is based on
Judging the tissue type of the single-chamber structure; the tissue types include cells, blood vessels, and extracellular spaces; the single-chamber structure not comprising groupsThe volume ratio and diffusion coefficient of the weave type are set to 0,
if the voxel point is judged to be of a double-chamber structure in the step IV, the method is based on the following steps
And->
Judging the tissue types of the first chamber and the second chamber of the double-chamber structure; the tissue types include cells, blood vessels, and extracellular spaces; the volume ratio and diffusion coefficient of the tissue type not contained in the dual chamber structure are set to 0,
if the voxel point is judged to be of a three-chamber structure in the step IV, the method is based on
、/>
And->
Judging the tissue types of the first chamber, the second chamber and the third chamber of the three-chamber structure; the tissue types include cells, blood vessels, and extracellular spaces;
and VI, repeating the steps III to V to analyze other voxel points until all voxel points are analyzed.
2. The non-invasive measurement method of microstructure of biological tissue according to claim 1, wherein in the step iv, the voxel point is determined to be a single-chamber structure, a double-chamber structure or a three-chamber structure according to the result of the solving in the step iii, specifically:
if it is
And is also provided with
Then the voxel point is a single-chamber structure;
if it is
And is also provided with
Then the voxel point is a dual-chamber structure;
otherwise, the voxel point is in a three-chamber structure;
wherein the method comprises the steps of
Is an empirical parameter.
3. The method for non-invasive measurement of biological tissue microstructure according to claim 1, wherein after step vi further comprises:
and reconstructing the magnetic resonance image according to any one or more of volume ratio of cells, volume ratio of blood vessels, volume ratio of extracellular space, diffusion coefficient of cells, diffusion coefficient of blood vessels and diffusion coefficient of extracellular space of each voxel point.
4. The non-invasive measurement method of biological tissue microstructure according to claim 1, wherein the solving method in step iii adopts constrained nonlinear optimization solution; wherein,,
the optimization formula of formula 1 is:
the optimization formula of formula 2 is:
the optimization formula of formula 3 is:
5. the non-invasive measurement method of biological tissue microstructure according to claim 4, wherein the constrained nonlinear optimization solution employs lagrangian multiplier method.
6. The method for non-invasive measurement of biological tissue microstructure according to claim 1, wherein after step vi further comprises:
and VIII, calculating the cellulose trend distribution of the cells, the blood vessels and the extracellular space according to the diffusion coefficient of the cells, the diffusion coefficient of the blood vessels and the diffusion coefficient of the extracellular space of each voxel point.
7. The non-invasive measurement method of biological tissue microstructure according to claim 6, wherein the estimation uses a statistical method or a main diffusion direction-based tracking method.
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