Microscale microscopic imaging method of tissue structure and function based on magnetic resonance imaging

Abstract

A kind of microscale microscopic imaging method based on tissue structure and function of magnetic resonance imaging, comprising: establishing database 1 containing microscopic image and water molecule diffusion movement information on microscopic scale;Millimeter scale magnetic resonance imaging image based on magnetic resonance diffusion principle is established, and the database 2 of the diffusion movement law and characteristics of all water molecules in the corresponding voxel of each pixel in each image;And the correlation mapping algorithm between database 1 and database 2 is established, the microscopic image information corresponding to the macroscopic scale, diffusion principle-based, magnetic resonance macroscopic imaging is obtained through the bridge of water molecule diffusion movement characteristics on micro level.The present application realizes the non-invasive acquisition of microscopic image showing cell and subcellular scale cell microstructure and function using magnetic resonance imaging technology with millimeter scale spatial resolution.

Description

Technical Field

[0001] This invention relates to the field of magnetic resonance imaging technology, and in particular to a micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging. Background Technology

[0002] As the basic structural and functional unit of human and mammalian tissues at the micrometer scale, the dynamic understanding of cellular structural and functional changes is of paramount importance for comprehending the physiological functions of human and mammalian tissues, the mechanisms of disease development, and the mechanisms of action and efficacy evaluation of interventions such as drug therapy.

[0003] Currently, optical microscopy is the primary method for studying and exploring the microstructure and function of cells at the cellular and subcellular scale. However, a prerequisite for optical cell imaging is that the object being imaged must be optically transparent, allowing light to penetrate it. Most human and mammalian tissues are optically opaque, preventing light from penetrating them and thus rendering the method unsuitable for optically opaque human or other biological tissues. Currently, there is no non-invasive imaging method capable of achieving microscopic imaging of tissue microstructure and function at the micrometer scale under optically opaque conditions.

[0004] Diffusion magnetic resonance imaging (DMI) is an imaging method that indirectly reflects information related to the diffusion and movement of water molecules within tissues. It is a macroscopic, non-invasive imaging technique that can penetrate light-opaque human bodies or light-opaque biological tissues.

[0005] Diffusion motion refers to the dispersion displacement of water molecules at the micrometer scale. It reflects the dynamic changes in the structure and function of cells within a living organism. During the tens of milliseconds of diffusion motion in a diffusion magnetic resonance imaging (DMRI) sequence, the dispersion displacement of water molecules is distributed within the micrometer scale, which falls precisely at the cellular and subcellular scale. At the cellular and subcellular scale, water molecule diffusion motion is restricted by the cell membrane and also by biomolecules such as proteins, DNA, and RNA. Furthermore, various cellular functions performed during processes such as activation, metabolism, proliferation, apoptosis, or necrosis also influence the diffusion motion of water molecules in the microenvironment.

[0006] Although diffusion motion itself reflects information about tissue microstructure and function at the cellular and subcellular scales, it's important to note that current diffusion-weighted magnetic resonance imaging (DMRI) spatial resolution is still limited to the millimeter scale, making it a macroscopic imaging method. This means that millions of cells are distributed within each pixel (representing a corresponding voxel) in a magnetic resonance image. Therefore, DMRI currently cannot acquire microscopic images at the cellular and subcellular scales.

[0007] Therefore, in view of the shortcomings of existing technologies, it is necessary to provide a microscopic microscopic imaging method based on magnetic resonance imaging of tissue structure and function that is based on the principle of water molecule diffusion motion magnetic resonance imaging and can provide micrometer-scale information on tissue microstructure and function. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a micron-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging, which can dynamically reflect changes in the microstructure and function of cells.

[0009] The objective of this invention is achieved through the following technical measures.

[0010] A microscopic imaging method for tissue structure and function based on magnetic resonance imaging is provided, including:

[0011] Establish a database containing microscopic images and information on the dispersion and motion of water molecules at the microscopic scale;

[0012] A database was established for millimeter-scale magnetic resonance imaging images obtained based on the principle of magnetic resonance dispersion, as well as the dispersion motion laws and characteristics of all water molecules within the corresponding voxel of each pixel in each image; and

[0013] An association mapping algorithm is established between database 1 and database 2. By using the diffusion motion characteristics of water molecules at the microscopic level as a bridge, microscopic image information corresponding to magnetic resonance macroscopic imaging based on the diffusion principle is obtained at the macroscopic scale.

[0014] Preferably, the above-mentioned micron-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging includes a database 1 containing microscopic images of various tissue types, disease types, and cell types at the micron scale, as well as characterization information reflecting the diffusion motion patterns and characteristics of water in the microscopic images.

[0015] Preferably, the above-mentioned micron-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging, in constructing database 1, specifically includes the following steps:

[0016] S1: A measurement technique that uses non-magnetic resonance imaging to directly measure the motion laws and related parameters of water molecule dispersion is used, combined with microscopic imaging technology, to construct a database based on microscopic images containing the motion laws and correlation coefficients of water molecules dispersion.

[0017] S2: Employing techniques capable of quantitative analysis of microscopic images, quantitative analysis is performed on various optical microscopic images acquired in S1 to obtain the characteristics and parameters of the microscopic images, which are then integrated into database 1.

[0018] Preferably, in the above-mentioned micron-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging, step S1 employs coherent anti-Stokes Raman scattering microscopic imaging technology to construct a database of water molecule dispersion coefficients based on microscopic images.

[0019] Preferably, in the micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging described above, in S2, a technique capable of quantitative analysis of microscopic images is specifically adopted to quantitatively analyze the various optical microscopic images acquired in S1, obtain a water molecule dispersion anisotropy parameter library based on the microscopic images, and integrate it into database 1.

[0020] Preferably, in the micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging described above, the structural tensor analysis method is used in S2 to perform quantitative analysis of the microscopic images.

[0021] Preferably, the above-mentioned micron-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging includes a database 2 containing magnetic resonance imaging images at the millimeter scale for various tissue types and disease types.

[0022] Preferably, in the aforementioned micron-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging, the diffusion motion laws and characteristics of water molecules within voxels in database 2 are specifically a set of probability density functions of the diffusion motion distribution of water molecules within voxels.

[0023] The set of probability density functions for the diffusion motion distribution of water molecules within a voxel includes the set of probability density functions for the diffusion coefficient distribution of water molecules within a single voxel based on diffusion magnetic resonance images, and the set of probability density functions for the diffusion anisotropy distribution of water molecules within a single voxel based on diffusion magnetic resonance images.

[0024] Preferably, the above-mentioned micron-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging establishes an association mapping algorithm between database 1 and database 2. Specifically, it uses a pattern recognition algorithm or machine learning algorithm to map the probability density function distribution set in database 2 to the corresponding specific set of water diffusion motion characteristics in database 1 corresponding to the microscopic image, thereby realizing micron-scale microscopic imaging technology for tissue microstructure and function based on magnetic resonance.

[0025] When using pattern recognition algorithms, the following steps are included:

[0026] S1: Discretize the probability density function of the intravoxel diffusion characteristics distribution at the millimeter scale for different tissue types and different diseases;

[0027] S2: Using the criterion of minimum mean square error, through continuous iterative processing, we search for subsets from the database of micron-scale tissue microstructure and diffusion function of different tissue types and different diseases that completely match the probability density function values ​​of the voxel diffusion characteristics distribution at the millimeter scale after discretization.

[0028] When using machine learning algorithms, the following steps are included:

[0029] S1: Discretize the probability density function of the intravoxel diffusion characteristics distribution at the millimeter scale for different tissue types and different diseases;

[0030] S2: Using a portion of the data from database 1 as a training set, analyze the training set using feature recognition or clustering algorithms; using diffusion characteristics as a link, combine the diffusion characteristic data in database 2, and use feature recognition algorithms to jointly establish a feature library, and establish a mapping training set between database 2 and database 1;

[0031] S3: Using a mapping algorithm between database 2 and database 1 obtained through machine learning, based on the newly obtained images and diffusion data that conform to the data characteristics of database 2, the corresponding microscopic imaging information in database 1 is obtained, that is, micron-scale microscopic imaging of tissue microstructure and function based on diffusion magnetic resonance imaging is realized.

[0032] Preferably, the above-mentioned micron-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging establishes database 1 first and then database 2, or database 2 first and then database 1, or database 1 and database 2 are established simultaneously.

[0033] This invention presents a microscopic imaging method for tissue structure and function based on magnetic resonance imaging (MRI). The method includes: establishing a database 1 containing microscopic images and information on the diffusion motion of water molecules at the microscopic scale; establishing a database 2 containing millimeter-scale MRI images obtained based on the principle of magnetic resonance diffusion, and the diffusion motion patterns and characteristics of all water molecules within the corresponding voxel for each pixel in each image; and establishing an association mapping algorithm between database 1 and database 2. Through the bridge of the diffusion motion characteristics of water molecules at the microscopic level, the method obtains macroscopic image information corresponding to macroscopic MRI imaging based on the principle of diffusion at the macroscopic scale. This solves the problem of acquiring microscopic images displaying the microstructure and function of cells at the cellular and subcellular scales using MRI technology with millimeter-scale spatial resolution.

[0034] This invention constructs a database 1 containing microscopic images of tissue structures at the micrometer scale for different tissue types and diseases, along with corresponding information on the diffusion motion of water within the tissues; and a database 2 containing magnetic resonance imaging (MRI) data at the millimeter scale for different tissue types and diseases, along with a set of probability density functions within voxels reflecting the diffusion motion characteristics of water molecules. A mapping relationship between database 1 and database 2 is established based on the diffusion motion state information of water molecules. When obtaining MRI data conforming to database 2, the corresponding microscopic imaging information from database 1 is obtained, achieving diffusion MRI of tissue microstructure and function at the micrometer scale. This invention enables the acquisition of microscopic images displaying cellular and subcellular scale cellular microstructure and function using MRI technology with millimeter-scale spatial resolution.

[0035] The method of this invention can perform magnetic resonance imaging on objects conforming to Database 2, including obtaining images of tissues or organs in different parts of the human body, or organs and tissues in light-opaque organisms, through magnetic resonance imaging, thereby obtaining microscopic imaging results corresponding to the data characteristics of the imaging object conforming to Database 1. It achieves microscopic imaging of the tissue structure and function of light-opaque objects at the micrometer scale using non-invasive imaging methods. The method of this invention enables dynamic understanding of changes in cell structure and function, which is of extremely important value for understanding the physiological functions of human and mammalian tissues, the mechanisms of disease development, and the mechanisms of action and efficacy evaluation of interventions such as drug treatment. Attached Figure Description

[0036] The invention will be further described with reference to the accompanying drawings, but the contents of the drawings do not constitute any limitation on the invention.

[0037] Figure 1 This is a flowchart of a micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging, according to the present invention. Detailed Implementation

[0038] The present invention will be further described in conjunction with the following embodiments.

[0039] Example 1.

[0040] This invention aims to address the problem of acquiring microscopic images displaying cellular and subcellular microstructure and function using magnetic resonance imaging (MRI) technology with millimeter-scale spatial resolution. To achieve the objectives of this invention and realize diffusion magnetic resonance imaging of tissue microstructure and function at the micrometer scale, the following technical problems need to be overcome:

[0041] (1) How to construct a database 1 containing microscopic images of tissue structures at the micrometer scale for different tissue types and different diseases, as well as the corresponding information on the diffusion motion of water in the tissues; (2) How to construct a database 2 containing magnetic resonance imaging at the millimeter scale for different tissue types and different diseases, as well as the corresponding set of probability density functions within voxels that reflect the diffusion motion characteristics of water molecules; (3) How to establish a mapping relationship between database 1 and database 2 based on the diffusion motion state information of water molecules; so that when obtaining personal magnetic resonance imaging data that conforms to database 2, the corresponding microscopic imaging information in database 1 can be obtained, thereby realizing diffusion magnetic resonance imaging of tissue microstructure and function at the micrometer scale.

[0042] To address the aforementioned problems, this embodiment provides a micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging, comprising:

[0043] Establish a database containing microscopic images and information on the dispersion and motion of water molecules at the microscopic scale;

[0044] A database was established for millimeter-scale magnetic resonance imaging images obtained based on the principle of magnetic resonance dispersion, as well as the dispersion motion laws and characteristics of all water molecules within the corresponding voxel of each pixel in each image; and

[0045] An association mapping algorithm is established between database 1 and database 2. By using the diffusion motion characteristics of water molecules at the microscopic level as a bridge, microscopic image information corresponding to magnetic resonance macroscopic imaging based on the diffusion principle is obtained at the macroscopic scale.

[0046] It should be noted that this micron-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging can establish database 1 first and then database 2, or database 2 first and then database 1, or database 1 and database 2 can be established simultaneously.

[0047] Database 1 contains microscopic images at the micrometer scale representing various tissue types, disease types, and cell types, along with characterization information reflecting the diffusion patterns and characteristics of water within these images. This characterization information includes various curves, graphs, and sets of functions.

[0048] The construction of database 1 includes the following steps:

[0049] S1: A measurement technique using non-magnetic resonance imaging that can directly measure the motion laws and related parameters of water molecule dispersion, combined with microscopic imaging technology, is used to construct a database based on microscopic images containing the motion laws and correlation coefficients of water molecules. For example, a database of water molecule dispersion coefficients based on microscopic images can be constructed using coherent anti-Stokes Raman scattering microscopic imaging technology.

[0050] S2: Employ techniques capable of quantitative analysis of microscopic images, such as structural tensor analysis, to quantitatively analyze various optical microscopic images acquired in S1, obtain the characteristics and parameters of the microscopic images, such as obtaining a library of water molecule dispersion anisotropy parameters based on microscopic images, and integrate it into database 1.

[0051] The constructed "Database 2" is a database based on the principle of magnetic resonance diffusion. This database contains magnetic resonance imaging images at the millimeter scale for different tissue types and disease types, as well as the diffusion motion patterns and characteristics of all water molecules within each voxel corresponding to each pixel in each image, such as the set of probability density functions for the diffusion motion distribution of water molecules. The set of probability density functions for the diffusion characteristics distribution within voxels at the millimeter scale for different tissue types and diseases includes the set of probability density functions for the diffusion coefficient distribution of water molecules within a single voxel based on diffusion magnetic resonance images, and the set of probability density functions for the anisotropic diffusion distribution of water molecules within a single voxel based on diffusion magnetic resonance images.

[0052] The diffusion motion laws and characteristics of water molecules within voxels in Database 2 can be a set of probability density functions of the diffusion motion distribution of water molecules within voxels. The set of probability density functions of the diffusion motion distribution of water molecules within voxels includes the set of probability density functions of the diffusion coefficient distribution of water molecules within a single voxel based on diffusion magnetic resonance images, and the set of probability density functions of the diffusion anisotropy distribution of water molecules within a single voxel based on diffusion magnetic resonance images.

[0053] This embodiment of the micron-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging establishes an association mapping algorithm between database 1 and database 2. Using algorithms such as pattern recognition or machine learning, it maps the probability density function distribution set in database 2 to the corresponding specific set of water dispersion motion characteristics in database 1, corresponding to the microscopic images. This achieves micron-scale microscopic imaging technology for tissue microstructure and function based on magnetic resonance. In other words, by using the dispersion motion characteristics of water molecules at the microscopic level as a bridge, it obtains macroscopic information such as microscopic images corresponding to macroscopic magnetic resonance imaging based on the principle of dispersion, thus realizing micron-scale microscopic imaging technology for tissue microstructure and function based on magnetic resonance.

[0054] When using pattern recognition algorithms, the following steps are included:

[0055] S1: Discretize the probability density function of the intravoxel diffusion characteristics distribution at the millimeter scale for different tissue types and different diseases;

[0056] S2: Using the criterion of minimum mean square error, through continuous iterative processing, we search for subsets from the database of micron-scale tissue microstructure and diffusion function of different tissue types and different diseases that completely match the probability density function values ​​of the voxel diffusion characteristics distribution at the millimeter scale after discretization.

[0057] When using machine learning algorithms, the following steps are included:

[0058] S1: Discretize the probability density function of the intravoxel diffusion characteristics distribution at the millimeter scale for different tissue types and different diseases;

[0059] S2: Using a portion of the data from database 1 as a training set, analyze the training set using feature recognition or clustering algorithms; using diffusion characteristics as a link, combine the diffusion characteristic data in database 2, and use feature recognition algorithms to jointly establish a feature library, and establish a mapping training set between database 2 and database 1;

[0060] S3: Using a mapping algorithm between database 2 and database 1 obtained through machine learning, based on the newly obtained images and diffusion data that conform to the data characteristics of database 2, the corresponding microscopic imaging information in database 1 is obtained, that is, micron-scale microscopic imaging of tissue microstructure and function based on diffusion magnetic resonance imaging is realized.

[0061] This embodiment constructs a database 1 containing microscopic images of tissue structures at the micrometer scale for different tissue types and diseases, along with corresponding information on the diffusion motion of water within the tissues; and a database 2 containing magnetic resonance imaging (MRI) data at the millimeter scale for different tissue types and diseases, along with a set of probability density functions within voxels that reflect the diffusion motion characteristics of water molecules. A mapping relationship between database 1 and database 2 is established based on the diffusion motion state information of water molecules. When obtaining MRI data that conforms to database 2, the corresponding microscopic imaging information from database 1 is obtained, achieving diffusion magnetic resonance imaging of tissue microstructure and function at the micrometer scale. This realizes the acquisition of microscopic images displaying cellular and subcellular scale cellular microstructure and function using MRI technology with millimeter-scale spatial resolution.

[0062] The method of this invention can perform magnetic resonance imaging on objects conforming to Database 2, including obtaining images of tissues or organs in different parts of the human body, or organs and tissues in light-opaque organisms, through magnetic resonance imaging, thereby obtaining microscopic imaging results corresponding to the data characteristics of the imaging object conforming to Database 1. It achieves microscopic imaging of the tissue structure and function of light-opaque objects at the micrometer scale using non-invasive imaging methods. The method of this invention enables dynamic understanding of changes in cell structure and function, which is of extremely important value for understanding the physiological functions of human and mammalian tissues, the mechanisms of disease development, and the mechanisms of action and efficacy evaluation of interventions such as drug treatment.

[0063] Example 2.

[0064] This embodiment uses the micrometer-scale microscopic imaging of tissue microstructure and function based on diffusion magnetic resonance imaging (DMR) of liver fibrosis tissue as an example to illustrate the micrometer-scale microscopic imaging method of tissue structure and function based on magnetic resonance imaging (DMR) of the present invention.

[0065] (I) Constructing a database of tissue microstructure and diffusion function at the micrometer scale for different tissue types and different diseases, namely "Database 1".

[0066] 1.1 A database of water molecule dispersion coefficients based on coherent anti-Stokes Raman scattering microscopic images was constructed, namely "Database 1".

[0067] This step utilizes coherent anti-Stokes Raman scattering microscopy to measure the diffusion coefficient of water molecules in fresh liver tissue, constructing a microscopic image library of liver tissue and a corresponding database of water molecule diffusion coefficients. Coherent anti-Stokes Raman scattering microscopy generates image contrasts by utilizing the vibrational states of different molecules. No tissue labeling is required during imaging, therefore the sample preparation and imaging processes are almost unaffected.

[0068] First, high-resolution coherent anti-Stokes Raman scattering microscopic images were obtained. To obtain microscopic images of fresh liver tissue, the target object (a slice of fresh liver tissue) was prepared. The slice was fixed on a glass slide and imaged using a coherent anti-Stokes Raman scattering microscope. The microscope is equipped with a laser source that generates two laser pulses with a specific frequency difference, referred to as the pump laser beam and the Stokes laser beam, with the frequency difference tuned to correspond to the characteristic vibrational frequencies of water molecules. When the pump laser beam and the Stokes laser beam simultaneously irradiate the target object (liver) tissue sample, a specific coherent anti-Stokes Raman scattering signal is generated. This signal responds to the vibrational characteristics and distribution of water molecules, and therefore can be used to assess the diffusion behavior of water molecules within the tissue. In the experiment, the laser beam needs to be focused to a specific depth of the sample, and the optical system is adjusted to obtain scattering signals at different depths. By changing the timing of the pump laser beam and the Stokes laser beam, the signal intensity at different time intervals can be obtained, and then the diffusion characteristics of water molecules can be quantitatively analyzed using a diffusion model based on Fick's law. By analyzing the movement of water molecules at different time points, the diffusion coefficient of water molecules in the target object (liver tissue) can be obtained.

[0069] To further improve measurement accuracy, the above measurements can be repeated under different temperature, pH, and osmotic pressure conditions to observe the effects of these factors on water molecule diffusion and establish corresponding calibration models.

[0070] A complete database of water molecule dispersion coefficients is constructed by continuously acquiring dispersion coefficients from coherent anti-Stokes Raman scattering images of a specific size. To construct a sufficiently large database of water molecule dispersion coefficients, it is necessary to process as many liver tissue samples as possible as possible.

[0071] Similarly, microscopic images of other types of healthy and diseased tissues, as well as the diffusion and movement signals of water molecules within them, can be obtained. Microscopic images of different types of cell suspensions, as well as the diffusion and movement signals of water molecules within them, can also be obtained.

[0072] 1.2 Obtaining a dataset of water molecule dispersion anisotropy based on microscopic images

[0073] This step uses three-dimensional structural tensor analysis of confocal microscopy images as an example. A large number of target objects (fresh liver tissue slices) need to be acquired. After confocal microscopy imaging, three-dimensional structural tensor analysis is performed to obtain water molecule dispersion anisotropy parameters, thereby constructing a water molecule dispersion anisotropy database for liver tissue. The advantage of three-dimensional structural tensor analysis is that it does not depend on the orientation of the tissue slice plane.

[0074] First, high-resolution confocal microscopic images are obtained. To obtain confocal microscopic images of the target subject (fresh liver tissue), the target tissue sample, stored in phosphate buffer to maintain its physiological state, needs to be prepared. Next, the liver is sliced ​​to a suitable thickness, typically 5 μm-5 mm, using a vibratory microtome, microscalpel, or tissue cutter. The slices are then fixed onto a glass slide. Finally, imaging is performed using a confocal microscope. The laser intensity, scanning speed, and microscope focal length are adjusted to ensure high-quality images, with the magnification set to 10x. The final planar resolution for image acquisition is set to 0.25 × 0.25 μm. 2 The layer thickness was set to 1μm-5μm. First, a two-dimensional image mosaic of the entire tissue slice was constructed. Then, two-dimensional images obtained from different layers were stitched together to form a three-dimensional image. After converting the images to TIFF format, they were imported into MATLAB for subsequent structural tensor analysis. To analyze the diffuse anisotropy of water molecules, the images were preprocessed, including denoising, image enhancement, and contrast adjustment, to ensure the clarity of the tissue microstructure features.

[0075] Next, a three-dimensional structural tensor analysis method is applied to calculate the gradient information around each pixel in the confocal microscopy image. The structural tensor is a mathematical method for describing the local structural features of an image. It can construct a tensor matrix by calculating pixel gradients, thereby capturing the anisotropic structural features in the tissue. For each pixel, the structural tensor reflects the morphological and orientation features of its surrounding region. Based on the calculated structural tensor, the anisotropic structure in the image is quantified. The principal axis direction and diffusion intensity of the anisotropic diffusion direction are analyzed using the eigenvalues ​​of the tensor, thereby inferring the possible diffusion paths of water molecules in the tissue. The results are quantified into anisotropic indices to describe the diffusion anisotropic characteristics of different regions. By continuously acquiring diffusion anisotropic values ​​on confocal images of specific sizes, a complete dataset of water molecule diffusion anisotropy is constructed and integrated into "Database 1". To construct a sufficiently large dataset of water molecule diffusion anisotropy, it is necessary to process as much fresh liver tissue as possible.

[0076] Similarly, microscopic images of other types of healthy and diseased tissues, as well as the diffusion and movement signals of water molecules within them, can be obtained. Microscopic images of different types of cell suspensions, as well as the diffusion and movement signals of water molecules within them, can also be obtained.

[0077] (II) Construct a database, namely "Database 2", which consists of probability density function data sets of magnetic resonance imaging images and voxel diffusion characteristics distribution at the millimeter scale for different tissue types and different diseases.

[0078] 2.1 Constructing a probability density function library for the dispersion coefficient distribution of water molecules within a single voxel based on diffusion magnetic resonance images.

[0079] For liver fibrosis tissue, within each magnetic resonance voxel, a massive number of water molecules undergo complex diffusion processes, typically requiring statistical methods for quantitative description. We can reasonably assume that within a specific micro-region within a voxel, a single diffusion coefficient D can quantitatively describe the diffusion motion within that micro-region. However, within other voxels, due to differences in the microenvironment, the diffusion coefficient D will inevitably exhibit significant differences. As the volume of the micro-region gradually decreases, the distribution of the diffusion coefficient within a single voxel tends towards a continuous distribution. This can be expressed as:

[0080] S / S0=∫P(D)exp(-b·D)dD (1);

[0081] S and S0 represent the diffusion weighting factors b≠0 and b=0 s / mm, respectively. 2 At that time, the diffusion magnetic resonance signal amplitude of a single voxel is denoted as P(D). The diffusion weighting factor b is a scanning parameter that can be manually set before the magnetic resonance scan. It represents the degree to which the diffusion coefficient is weighted by the diffusion magnetic resonance signal. With the same diffusion coefficient D, the larger the diffusion weighting factor b, the smaller the diffusion magnetic resonance signal amplitude S, and the higher the attenuation S / S0 of the diffusion magnetic resonance signal. P(D) represents the probability density function of the diffusion coefficient distribution within the voxel. To better capture the dynamic changes in the diffusion coefficient distribution within the voxel during complex physiological and pathological processes, a multi-component Gaussian mixture model is used to describe the diffusion coefficient distribution within the voxel. P(D) can be expressed as:

[0082]

[0083] Where N represents the number of Gaussian distributions, w represents the weights of the Gaussian distributions, and k σ The mean is The variance is σ 2 Gaussian distribution function:

[0084]

[0085] To obtain a continuous P(D), the following three steps are required:

[0086] (1) Select a combination of b values ​​for multi-exponential fitting; (2) Sample the diffusion coefficients by continuous multi-exponential fitting; (3) Estimate the distribution function of the continuous diffusion coefficients by kernel density estimation.

[0087] First, we select the b-value combination for multi-exponential fitting. We set the b-value range to three intervals: high, medium, and low, namely [100, 1500], [1600, 3000], and [3100, 4500] s / mm. 2The number of exponential terms is set between 2 and 15, and the number of b-values ​​required for multi-exponential fitting is three times the number of exponential terms. When the number of exponential terms is M, 3M b-values ​​need to be selected, choosing M b-values ​​from three intervals: high, medium, and low. During selection, the highest M-1 b-values ​​in each interval are used as the default initial b-values, and then selections are made from the remaining b-values. Ultimately, for multi-exponential fitting with M exponential terms, (16-N) b-values ​​are generated. 3 Group b value combination.

[0088] Then, based on the combination of b values ​​selected in the previous step, the dispersion coefficient is sampled by performing continuous multi-exponential fitting. The discretized equation (1) is fitted using the non-negative regularized least squares method, and the discretized expression is:

[0089]

[0090] M represents the number of discretized dispersion coefficients, and also the number of exponential terms. P d (D m () represents the discretized probability density function. The nonnegative regularized least squares fitting problem becomes:

[0091]

[0092] In expression (5), the first and second terms represent the fitting error term and the regularization term, respectively, and μ represents the regularization factor. G represents the number of diffusion weighting factors b.

[0093] Finally, the probability density function P(D) of the continuous dispersion coefficient distribution is estimated by performing kernel density estimation on the sampled discrete dispersion coefficients.

[0094] The diffusion motion information obtained above is compared with that obtained by magnetic resonance imaging. Figure 1 They correspond to each other and are jointly incorporated into "Database 2".

[0095] 2.2 Obtaining the set of probability density functions for the diffuse anisotropic distribution of water molecules within a single voxel based on diffusion magnetic resonance images

[0096] Obtaining the probability density function of the dispersion anisotropy distribution of water molecules within a single voxel is similar to constructing a set of probability density functions for the dispersion coefficient distribution of water molecules within a single voxel. The dispersion anisotropy of water molecules can be measured by relative anisotropy (RA), which is defined as follows:

[0097]

[0098] Where λ represents the eigenvalues ​​of the diffusion tensor, and Var(λ) represents the variance of the eigenvalues ​​λ of the diffusion tensor.

[0099] The probability density function of the anisotropic distribution of water molecules within a voxel can be represented by P(RA). The steps for calculating P(RA) are as follows: First, a suitable combination of b values ​​needs to be selected. Second, continuous sampling of the discrete relative anisotropy is required. Finally, the probability density function of the continuous anisotropic distribution of water molecules within a voxel is constructed using kernel density estimation. The calculation method is the same as described above for constructing the probability density function of the continuous dispersion coefficient distribution of water molecules within a voxel.

[0100] (III) Constructing the mapping relationship between "Database 1" and "Database 2"

[0101] This embodiment constructs a pattern recognition algorithm to search for subsets of probability density functions that can be combined to form the water molecule dispersion coefficient distribution within a single voxel, from a database of water molecule dispersion coefficients based on microscopic images; and to search for subsets of probability density functions that can be combined to form the water molecule dispersion anisotropy distribution within a single voxel, from a database of water molecule dispersion anisotropy based on microscopic images. Through this pattern recognition algorithm, a set of microscopic images corresponding one-to-one with a single voxel is established.

[0102] First, the continuous probability density function is discretized. The number of discretized intervals depends on the ratio of the size of a single magnetic resonance voxel to the size of a single microscopic image; the interval spacing is determined based on the number of intervals. Euler's method is used to approximate the continuous function, and the discretized function value is solved using a recursive formula. In practice, to improve the accuracy of discretization, the step size can be appropriately reduced, making the discretized probability density function value closer to the true value of the continuous probability density function.

[0103] Then, the values ​​closest to the discretized probability density function are searched from the water molecule dispersion coefficient database and the water molecule dispersion anisotropy database. The value that minimizes the mean square error between the two is selected as the value that meets the condition. By searching each value one by one, subsets of the water molecule dispersion coefficient database and the water molecule dispersion anisotropy database that can form a complete discretized probability density function are determined.

[0104] This embodiment constructs a database 1 containing microscopic images of tissue structures at the micrometer scale for different tissue types and diseases, along with corresponding information on the diffusion motion of water within the tissues; and a database 2 containing magnetic resonance imaging (MRI) data at the millimeter scale for different tissue types and diseases, along with a set of probability density functions within voxels that reflect the diffusion motion characteristics of water molecules. A mapping relationship between database 1 and database 2 is established based on the diffusion motion state information of water molecules. When obtaining MRI data that conforms to database 2, the corresponding microscopic imaging information from database 1 is obtained, achieving diffusion magnetic resonance imaging of tissue microstructure and function at the micrometer scale. This realizes the acquisition of microscopic images displaying cellular and subcellular scale cellular microstructure and function using MRI technology with millimeter-scale spatial resolution.

[0105] The method of this invention can perform magnetic resonance imaging on objects conforming to Database 2, including obtaining images of tissues or organs in different parts of the human body, or organs and tissues in light-opaque organisms, through magnetic resonance imaging, thereby obtaining microscopic imaging results corresponding to the data characteristics of the imaging object conforming to Database 1. It achieves microscopic imaging of the tissue structure and function of light-opaque objects at the micrometer scale using non-invasive imaging methods. The method of this invention enables dynamic understanding of changes in cell structure and function, which is of extremely important value for understanding the physiological functions of human and mammalian tissues, the mechanisms of disease development, and the mechanisms of action and efficacy evaluation of interventions such as drug treatment.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging, characterized in that, include: Establish a database containing microscopic images and information on the dispersion and motion of water molecules at the microscopic scale; Establish a database of millimeter-scale magnetic resonance imaging images obtained based on the principle of magnetic resonance diffusion, as well as the diffusion motion laws and characteristics of all water molecules in the corresponding voxel of each pixel in each image. as well as An association mapping algorithm is established between database 1 and database 2. By using the diffusion motion characteristics of water molecules at the microscopic level as a bridge, the microscopic image information corresponding to the macroscopic magnetic resonance imaging based on the diffusion principle is obtained at the macroscopic scale. An association mapping algorithm is established between database 1 and database 2. Specifically, through pattern recognition or machine learning algorithms, the probability density function distribution set in database 2 is mapped to the corresponding specific set of water diffusion motion characteristics corresponding to the microscopic images in database 1, thereby realizing micron-scale microscopic imaging technology of tissue microstructure and function based on magnetic resonance. When using pattern recognition algorithms, the following steps are included: S1: Discretize the probability density function of the intravoxel diffusion characteristics distribution at the millimeter scale for different tissue types and different diseases; S2: Using the criterion of minimum mean square error, through continuous iterative processing, we search for subsets from the database of micron-scale tissue microstructure and diffusion function of different tissue types and different diseases that completely match the probability density function values ​​of the voxel diffusion characteristics distribution at the millimeter scale after discretization. When using machine learning algorithms, the following steps are included: S1: Discretize the probability density function of the intravoxel diffusion characteristics distribution at the millimeter scale for different tissue types and different diseases; S2: Using a portion of the data from database 1 as a training set, analyze the training set using feature recognition or clustering algorithms; using diffusion characteristics as a link, combine the diffusion characteristic data in database 2, and use feature recognition algorithms to jointly establish a feature library, and establish a mapping training set between database 2 and database 1; S3: Using a mapping algorithm between database 2 and database 1 obtained through machine learning, based on the newly obtained images and diffusion data that conform to the data characteristics of database 2, the corresponding microscopic imaging information in database 1 is obtained, that is, micron-scale microscopic imaging of tissue microstructure and function based on diffusion magnetic resonance imaging is realized.

2. The micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging according to claim 1, characterized in that, Database 1 contains microscopic images at the micrometer scale of various tissue types, disease types, and cell types, as well as characterization information reflecting the diffusion motion patterns and characteristics of water in the microscopic images.

3. The micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging according to claim 2, characterized in that, The construction of database 1 includes the following steps: S1: A measurement technique that can directly measure the motion laws and related parameters of water molecule dispersion using non-magnetic resonance imaging is adopted, and combined with microscopic imaging technology, a database containing the motion laws and correlation coefficients of water molecules based on microscopic images is constructed. S2: Employing techniques capable of quantitative analysis of microscopic images, quantitative analysis is performed on various optical microscopic images acquired in S1 to obtain the characteristics and parameters of the microscopic images, which are then integrated into database 1.

4. The micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging according to claim 3, characterized in that, Step S1 employs coherent anti-Stokes Raman scattering microscopy to construct a database of water molecule dispersion coefficients based on microscopic images.

5. The micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging according to claim 3, characterized in that, In S2, a technique capable of quantitative analysis of microscopic images is specifically adopted to quantitatively analyze various optical microscopic images acquired in S1, obtain a library of water molecule dispersion anisotropy parameters based on microscopic images, and integrate it into database 1.

6. The micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging according to claim 5, characterized in that, S2 uses structural tensor analysis to perform quantitative analysis on microscopic images.

7. The micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging according to claim 3, characterized in that, Database 2 contains millimeter-scale magnetic resonance imaging images of various tissue types or various disease types.

8. The micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging according to claim 7, characterized in that, The diffusion motion patterns and characteristics of water molecules within voxels in Database 2 are specifically a set of probability density functions for the diffusion motion distribution of water molecules within voxels. The set of probability density functions for the diffusion motion distribution of water molecules within a voxel includes the set of probability density functions for the diffusion coefficient distribution of water molecules within a single voxel based on diffusion magnetic resonance images, or the set of probability density functions for the diffusion anisotropy distribution of water molecules within a single voxel based on diffusion magnetic resonance images.

9. The micrometer-scale microscopic imaging method for tissue structure and function based on magnetic resonance imaging according to any one of claims 1 to 8, characterized in that, You can either create database 1 first and then database 2, or create database 2 first and then database 1, or create database 1 and database 2 simultaneously.

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