A non-destructive method for obtaining the distribution of components of a biological sample

Abstract

The present application relates to the technical field of biological analysis, and particularly relates to a non-destructive method for obtaining the distribution of components of a biological sample, comprising: obtaining a spectral CT image of a biological sample to be measured; obtaining a first attenuation coefficient matrix of the biological sample to be measured according to the spectral CT image; selecting a first biological sample having the same components as the biological sample to be measured; obtaining an attenuation coefficient matrix and a density matrix of each component of the first biological sample; inputting the attenuation coefficient matrix and the density matrix of each component into a mass attenuation coefficient obtaining model to obtain the mass attenuation coefficient of each component of the first biological sample; and inputting the mass attenuation coefficient of each component of the first biological sample and the corresponding first attenuation coefficient matrix of the biological sample to be measured into a mass density distribution obtaining model to obtain a mass density distribution matrix of each component in the biological sample to be measured. The present application can obtain the mass density measurement of a single component or a compound in a biological sample, and the entire determination process is simple and convenient.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biological analysis technology, and particularly relates to a non-destructive method for obtaining the component distribution of a biological sample. BACKGROUND

[0002] In the field of life science research and medicine, it is necessary to understand the component distribution of biological tissues for functional analysis or to determine pathological changes. For example, the vitality and growth and development state of a plant seed can be determined according to the component distribution of the plant seed, and the evaluation of the coronary artery lumen and the analysis of the plaque component are the main diagnostic basis for coronary artery pathology. Non-destructive methods for component analysis of biological samples include near-infrared spectroscopy, hyperspectral analysis and other methods, which can determine the water content, main nutrient content or pathological condition of a plant sample. These methods can only obtain the macroscopic material content of a biological sample. High spatial resolution analysis methods are often destructive and require sample sectioning for analysis. In the field of life science, dynamic development analysis of living organisms and clinical applications require a non-destructive, high-resolution method for obtaining the component distribution of tissues or samples.

[0003] Spectral CT technology uses a detector that can distinguish photon energy to detect the number of photons in different energy intervals within the X-ray spectrum range, and obtains data in multiple energy intervals in one imaging. Since the absorption of X-rays by a material is related to the energy of the X-rays, the material component information can be obtained by decomposing the collected multi-energy interval attenuation data. This method has been applied in the medical field to analyze the distribution of blood vessels, bones and contrast agents.

[0004] The general method for solving the mass fraction of each material in spectral CT is to use pure standard materials, which can be single-component materials or compounds composed of multiple single components (such as a certain compound), to collect their absorption characteristics at different energies as a reference for spectral CT data splitting, and then substitute them into the spectral CT data decomposition model of the mixture to solve the spatial distribution of different material contents in the mixture. However, since the absorption of X-rays by the constituent materials of a biological sample is relatively small and the composition is complex, it is difficult to select a calibration material, and it is also difficult to obtain a high-purity single component or compound for measuring the mass attenuation coefficient and mass density.

[0005] In the prior art, similar materials with similar densities and effective mass coefficients are often used as calibration materials. It is difficult to find calibration materials that are close to the target material in both density and effective mass coefficient, and often only calibration materials that are close to the target material in one of the two quantities can be found. Therefore, these materials are used as reference components for spectral CT splitting, and the results obtained have relatively large errors. In addition, current spectral CT image decomposition of multiple materials often relies on the use of contrast agents, which can easily damage the biological sample itself.

[0006] Therefore, there is an urgent need for a non-destructive method for obtaining the component distribution of a biological sample. SUMMARY

[0007] Technical problems to be solved

[0008] In view of the above-mentioned defects and shortcomings in the prior art, the present application provides a non-destructive method for obtaining the component distribution of a biological sample, which solves the problem that it is difficult to perform spectral CT image splitting on complex components of a biological sample in the prior art, and also solves the technical problem that it is difficult to obtain a single component or a complex substance with high purity for measuring the mass attenuation coefficient and the mass density.

[0009] Technical solutions

[0010] In order to achieve the above-mentioned purpose, the main technical solutions adopted by the present application include:

[0011] In a first aspect, the present application provides a non-destructive method for obtaining the component distribution of a biological sample, comprising:

[0012] S100, obtaining a spectral CT image of a biological sample to be measured, and obtaining a first attenuation coefficient matrix corresponding to the biological sample to be measured according to the spectral CT image of the biological sample to be measured;

[0013] S200, selecting a first biological sample with the same components as the biological sample to be measured, and obtaining an attenuation coefficient matrix and a density matrix of each component of the first biological sample;

[0014] S300, inputting the attenuation coefficient matrix and the density matrix of each component into a pre-set mass attenuation coefficient obtaining model, and obtaining the mass attenuation coefficient of each component of the first biological sample;

[0015] S400, inputting the mass attenuation coefficient of each component of the first biological sample and the first attenuation coefficient matrix corresponding to the biological sample to be measured into a mass density distribution obtaining model, and obtaining a mass density distribution matrix of each component in the biological sample to be measured.

[0016] Optionally, the S200 specifically comprises:

[0017] S210a, obtaining a spectral CT image of the first biological sample, and obtaining a first attenuation coefficient matrix corresponding to the first biological sample according to the spectral CT image of the first biological sample;

[0018] S220a, performing three-dimensional spatial component analysis on the first biological sample, and obtaining a first density matrix;

[0019] S230a, data alignment and matrix dimension alignment are performed on the first density matrix to obtain the density matrix of each component of the first biological sample.

[0020] Optionally, the S200 specifically includes:

[0021] S21 0b, an energy spectrum CT image of the first biological sample is obtained.

[0022] S220b, a slice and a slice optical image of the first biological sample are obtained, component detection is performed on the slice of the first biological sample, a first density matrix corresponding to the slice optical image of the first biological sample is obtained, and an attenuation coefficient matrix of an energy spectrum CT image corresponding to the slice is obtained.

[0023] S230b, data alignment and matrix dimension alignment are performed on the first density matrix to obtain the density matrix of each component of the first biological sample.

[0024] Optionally, the S220b includes:

[0025] S221b, a slice and a slice optical image of the first biological sample are obtained, fluorescence staining, spatial proteomics analysis or spatial metabolomics analysis are performed on the slice, and a first density matrix corresponding to the slice optical image of the first biological sample is obtained.

[0026] S222b, according to the slice optical image, a two-dimensional cross-sectional image corresponding to the slice optical image of an energy spectrum CT image of the first biological sample is obtained, and an attenuation coefficient matrix of the two-dimensional cross-sectional image is obtained as an attenuation coefficient matrix of each component of the first biological sample.

[0027] Optionally, the S222b specifically includes:

[0028] A feature point on the slice optical image is obtained, and a same feature point corresponding to the feature point on the slice optical image on the energy spectrum CT image is obtained.

[0029] According to the same feature point corresponding to the feature point on the energy spectrum CT image, a two-dimensional cross-sectional image where the same feature point is located is obtained as an energy spectrum CT image of the slice, and an attenuation coefficient matrix corresponding to the energy spectrum CT image of the slice is obtained as an attenuation coefficient matrix of each component of the first biological sample.

[0030] Optionally, the feature point is obtained by a feature detection algorithm; the feature detection algorithm is a Harris corner point detection algorithm, a scale space extreme value detection algorithm, an edge detection algorithm or a local feature detection algorithm.

[0031] The feature point is greater than 3.

[0032] Optionally, the first density matrix is data-aligned and matrix dimension-aligned, and the density matrix of each component of the first biological sample is obtained by:

[0033] The point of the attenuation coefficient matrix corresponding to the first density matrix and the first biological sample is obtained, and a translation matrix and a rotation transformation matrix are obtained according to the point; and a coordinate transformation matrix is obtained according to the translation matrix and the rotation transformation matrix.

[0034] According to the coordinate transformation matrix, the first density matrix is data-aligned to obtain a data-aligned density matrix.

[0035] Optionally, the transformation matrix is:

[0036]

[0037] Wherein, F represents the coordinate transformation matrix, R represents the rotation transformation matrix, and T represents the translation matrix.

[0038] Optionally, the S300 specifically includes:

[0039] The attenuation coefficient matrix and the density matrix are input into the following formula to obtain the mass attenuation coefficient of each component of the first biological sample:

[0040]

[0041] Wherein, a j is a constant that does not change with the spatial position, ρ rj is the density matrix, and corresponds to the density of substance j in the first biological sample obtained in step S200, α j (E k ) is the mass attenuation coefficient of substance j in the first biological sample to X-ray with energy E k , μ a is the attenuation coefficient matrix of the first biological sample under X-ray with energy E k .

[0042] Optionally, the S400 includes:

[0043] The mass attenuation coefficient of each component of the first biological sample and the first attenuation coefficient matrix corresponding to the to-be-detected biological sample are input into the following formula to obtain the mass density distribution matrix of each component in the to-be-detected biological sample:

[0044]

[0045] Wherein, α j (E k ) is the mass attenuation coefficient of substance j to X-ray with energy E kThe mass attenuation coefficient of X-rays, B j The mass density distribution matrix corresponding to each component of the biological sample to be measured, μ b The first attenuation coefficient matrix of the biological sample to be measured under X-rays with energy E k .

[0046] (III) Beneficial effects

[0047] The beneficial effects of the present application are: a non-destructive biological sample component distribution acquisition method of the present application first acquires the spectral CT image of the biological sample to be measured, obtains the first attenuation coefficient matrix corresponding to the biological sample to be measured according to the spectral CT image; a first biological sample with the same components as the biological sample to be measured is selected, the mass attenuation coefficient is obtained according to the mass attenuation coefficient acquisition model, and the mass attenuation coefficient corresponding to each component in the biological sample to be measured is obtained, and finally the mass density distribution matrix of each component of the biological sample to be measured is obtained according to the mass density acquisition model. Compared with the prior art, the present application does not need to find a calibration object, and after the determination of the first biological sample, the mass density of the biological sample to be measured with the same components can be obtained, without the need for multiple calibrations, reducing the experimental workload and experimental operation process, and making the entire determination process more convenient. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 It is a flowchart of a non-destructive biological sample component distribution acquisition method of the present application. DETAILED DESCRIPTION

[0049] In order to better explain the present application, the present application is described in detail in combination with the drawings and specific embodiments.

[0050] The method commonly used in practical application in the method for solving material component information in spectral CT is material identification based on image domain, which is realized by material decomposition of multi-energy region reconstruction data, and is also called post-processing material decomposition algorithm. The attenuation of X-rays through a material is related to the energy of the photons, and is also related to the atomic number and density of the material. The greater the atomic number, the more the number of electrons, and the stronger the absorption of X-rays by the material, and the greater the attenuation coefficient. Therefore, the attenuation coefficient μ(r, E) is a physical quantity related to the properties of the material and the energy, which is reflected in the reconstructed image, and μ(r, E) is a distribution varying with spatial position r and energy E.

[0051] Modern spectral CT generally uses a single radiation source and a photon counting detector with energy partition function, which can simultaneously collect projections of multiple energy regions.

[0052] The embodiment of the present application provides a non-destructive biological sample component distribution acquisition method, in order to solve the technical problem that in the prior art, when component splitting is performed, because the component composition is complex, a calibration substance is difficult to select, and therefore it is difficult to perform high-purity single component or compound mass density measurement, the present application obtains a first attenuation coefficient matrix of a to-be-measured biological sample, obtains mass attenuation coefficients of each component of a first biological sample which has the same composition as the to-be-measured biological sample, inputs the first attenuation coefficient matrix and the mass attenuation coefficients of each component into a mass density acquisition model, and obtains the mass density of each component in the to-be-measured biological sample; the present application does not need to find a calibration substance, and after the determination of the first biological sample, the mass density of the to-be-measured biological sample with the same component can be obtained, without the need for multiple calibrations, thereby reducing the experimental workload and experimental operation process, and making the entire determination process more convenient.

[0053] In order to better understand the above technical solutions, the exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present application can be more clearly, thoroughly understood, and the scope of the present application can be completely conveyed to those skilled in the art.

[0054] Detailed embodiment description

[0055] Embodiment 1

[0056] Reference Figure 1 The non-destructive biological sample component distribution acquisition method of the present embodiment comprises:

[0057] S100, an energy spectrum CT image of a to-be-measured biological sample is obtained, and a first attenuation coefficient matrix corresponding to the to-be-measured biological sample is obtained according to the energy spectrum CT image of the to-be-measured biological sample;

[0058] S200, a first biological sample with the same composition as the to-be-measured biological sample is selected, and an attenuation coefficient matrix and a density matrix of each component of the first biological sample are obtained;

[0059] S300, the attenuation coefficient matrix and the density matrix of each component are input into a pre-set mass attenuation coefficient acquisition model, and the mass attenuation coefficients of each component of the first biological sample are obtained;

[0060] S400, the mass attenuation coefficients of each component of the first biological sample and the first attenuation coefficient matrix corresponding to the to-be-measured biological sample are input into a mass density distribution acquisition model, and a mass density distribution matrix of each component in the to-be-measured biological sample is obtained.

[0061] In the specific implementation process, a group of biological samples with the same components are selected as the first biological sample according to the type of the biological sample to be measured. For example, when the biological sample to be measured is an immature wheat seed, the first biological sample can be set as a mature wheat seed; different crop seeds, different varieties of the same crop seeds, or the same variety of the same crop seeds but in different maturity stages have similar main components, mainly including water, starch, protein and fat, and the proportions of the main components can be different. Here, other trace elements or small molecules with low content are not considered because they account for a very low proportion in the total components.

[0062] The method of the embodiment does not need to find a calibration object, and after the determination of the first biological sample, the mass density of the biological sample to be measured with the same components can be obtained, without multiple calibrations, thereby reducing the experimental workload and the experimental operation process, and making the entire determination process more convenient.

[0063] Embodiment 2

[0064] The non-destructive method for obtaining the component distribution of a biological sample includes the following steps.

[0065] In step S100, a spectral CT image of the biological sample to be measured is obtained, and a first attenuation coefficient matrix corresponding to the biological sample to be measured is obtained according to the spectral CT image of the biological sample to be measured.

[0066] In step S200, a first biological sample with the same components as the biological sample to be measured is selected, and an attenuation coefficient matrix and a density matrix of each component of the first biological sample are obtained.

[0067] In step S300, the attenuation coefficient matrix and the density matrix of each component are input into a pre-set mass attenuation coefficient obtaining model to obtain the mass attenuation coefficient of each component of the first biological sample.

[0068] In step S400, the mass attenuation coefficient of each component of the first biological sample and the first attenuation coefficient matrix corresponding to the biological sample to be measured are input into a mass density distribution obtaining model to obtain a mass density distribution matrix of each component in the biological sample to be measured.

[0069] Before the first attenuation coefficient matrix is obtained, the biological sample to be measured needs to be dried to completely evaporate the water in the biological sample to be measured while keeping other substances such as proteins and lipids unchanged. A spectral CT image of the dried biological sample to be measured is obtained, and a first attenuation coefficient matrix corresponding to the biological sample to be measured is obtained according to the spectral CT image.

[0070] Similarly, the first biological sample also needs to be dried to completely evaporate the water in the first biological sample while keeping other substances such as proteins and lipids unchanged when the attenuation coefficient matrix and the density matrix of each component of the first biological sample are obtained.

[0071] In this embodiment, step S200 specifically includes:

[0072] Step S210a, acquiring the spectral CT image of the first biological sample, and acquiring the attenuation coefficient matrix corresponding to the first biological sample according to the spectral CT image of the first biological sample;

[0073] Step S220a, performing three-dimensional spatial component analysis on the first biological sample to acquire the first density matrix;

[0074] Step S230a, performing data alignment and matrix dimension alignment on the first density matrix to acquire the density matrix of each component of the first biological sample.

[0075] In the specific implementation process, the first biological sample is subjected to three-dimensional spatial component analysis, such as nuclear magnetic resonance, spatial metabolomics, spatial proteomics analysis, etc., to acquire the first density matrix.

[0076] Specifically, nuclear magnetic resonance polarizes atomic nuclei in the sample by applying an external magnetic field, then flips them by applying a radio frequency pulse, and when the radio frequency pulse is turned off, these atomic nuclei release energy and return to their ground state. The process can be captured by a detector and converted into an image related to signal intensity and position. By analyzing the signal intensity at different positions, the distribution of specific molecules in the sample is obtained, and the first density matrix is constructed.

[0077] Spatial metabolomics is combined with mass spectrometry imaging or laser capture microdissection. In mass spectrometry imaging, the sample is placed directly under the probe of the mass spectrometer, and the entire sample surface is scanned by precise laser control to detect the composition of metabolites at different positions, thereby constructing the density matrix of each component in the sample.

[0078] Spatial proteomics uses mass spectrometry to directly obtain protein information and obtain its spatial distribution, and further processing generates a density matrix of the reaction protein density.

[0079] Alternatively, step S200 specifically includes:

[0080] Step S210b, acquiring the spectral CT image of the first biological sample;

[0081] Step S220b, acquiring the slice of the first biological sample and the slice optical image, performing component detection on the slice of the first biological sample to acquire the first density matrix corresponding to the slice optical image of the first biological sample and the attenuation coefficient matrix of the spectral CT image corresponding to the slice;

[0082] Step S230b, data alignment and matrix dimension alignment of the first density matrix are performed to obtain the density matrix of each component in the first biological sample.

[0083] In this embodiment, step S200 is described in two cases, one is processing in three-dimensional level, and the other is processing in two-dimensional level.

[0084] In this embodiment, step S220b includes:

[0085] Step S221b, a slice of the first biological sample and a slice optical image are obtained, the slice is subjected to fluorescent staining, spatial proteomics analysis or spatial metabolomics analysis, and a first density matrix corresponding to the slice optical image of the first biological sample is obtained.

[0086] Step S222b, according to the slice optical image, a two-dimensional cross-sectional image corresponding to the slice optical image of the spectral CT image of the first biological sample is obtained, and an attenuation coefficient matrix of the two-dimensional cross-sectional image is obtained as the attenuation coefficient matrix of each component of the first biological sample.

[0087] In this embodiment, a slice of the first biological sample is prepared using a frozen section technique, and the slice is imaged using an optical microscope to obtain a slice optical image of the first biological sample.

[0088] In the specific implementation process, assuming that the first density matrix corresponding to the slice optical image of the first biological sample is obtained using the method of fluorescent staining, the following steps need to be performed: a specific fluorescent dye is used to stain the slice, such as using FITC to stain starch and using a rhodamine aqueous solution to stain protein; a fluorescent microscope is used to set the required spatial resolution to image the stained slice and obtain an optical image; and the fluorescence intensity matrix corresponding to the optical image is calibrated and labeled to obtain the first density matrix.

[0089] Step S222b specifically includes:

[0090] The feature points on the slice optical image are obtained, and the same feature points corresponding to the slice optical image on the spectral CT image are obtained according to the feature points on the slice optical image.

[0091] According to the same feature points corresponding to the slice optical image on the spectral CT image, a two-dimensional cross-sectional image in which the same feature points are located is obtained as the spectral CT image of the slice, and an attenuation coefficient matrix corresponding to the spectral CT image of the slice is obtained as the attenuation coefficient matrix of each component of the first biological sample.

[0092] The feature points are obtained by a feature detection algorithm; the feature detection algorithm is a Harris corner point detection algorithm, a scale space extreme value detection algorithm, an edge detection algorithm or a local feature detection algorithm.

[0093] The feature points are greater than 3.

[0094] In the implementation process, at least 3 non-collinear feature points are needed to determine a plane; by setting more than 3 feature points, it can be ensured that there is enough information to accurately align the data in the registration process, and the robustness and accuracy of the registration can be improved.

[0095] In this embodiment, for example, the Harris corner detection algorithm is used to detect feature points in the image, which can be special structures or obvious boundaries on the sample surface.

[0096] In the implementation process, the first density matrix is data-aligned and matrix dimension-aligned to obtain the density matrix of each component of the first biological sample, including:

[0097] Obtaining the mass points of the first density matrix and the attenuation coefficient matrix corresponding to the first biological sample, obtaining the corresponding translation matrix and rotation transformation matrix according to the mass points, and obtaining the coordinate transformation matrix according to the translation matrix and the rotation transformation matrix;

[0098] According to the coordinate transformation matrix, the first density matrix is data-aligned to obtain the data-aligned density matrix.

[0099] In this embodiment, the method further includes: if the resolution of the first density matrix is lower than the resolution of the attenuation coefficient matrix, data interpolation is needed to increase the resolution of the first density matrix to match the resolution of the attenuation coefficient matrix; if the resolution of the first density matrix is higher than the resolution of the attenuation coefficient matrix, the resolution of the first density matrix is reduced by merging adjacent pixels to match the resolution of the attenuation coefficient matrix.

[0100] For example, assuming in a two-dimensional layer, the dimension of the attenuation coefficient matrix is MxN, where M is the height and N is the width; the dimension of the first density matrix is PxQ. If P≠M or Q≠N, data interpolation or pixel merging is needed.

[0101] If P

[0102] The second density matrix obtained after adjustment and the second attenuation coefficient matrix have the same dimension, both being MxN.

[0103] Further, the transformation matrix is:

[0104]

[0105] Wherein, F represents a coordinate transformation matrix, R represents a rotation change matrix, and T represents a translation matrix.

[0106] In the specific implementation process, for example, it is assumed that a feature point of a slice optical image is P(x, y), and a corresponding feature point on a spectral CT image is P'(x', y'); the centroids of the feature points are found as M(x, y) and M'(x', y'); a translation matrix is constructed according to the centroids, so that M'(x, y) = T x M(x', y');

[0107] In addition, a linear equation group composed of a plurality of points is

[0108]

[0109] A rotation transformation matrix R is obtained.

[0110] In this embodiment, step S300 specifically includes:

[0111] The attenuation coefficient matrix and the density matrix are input into the following formula to obtain the mass attenuation coefficient of each component of the first biological sample:

[0112]

[0113] Wherein, a j is a constant that does not change with the spatial position, ρ rj is a density matrix, and corresponds to the density of the substance j in the first biological sample obtained in step S200, α j (E k ) is the mass attenuation coefficient of the substance j in the first biological sample to X-rays with energy E k , μ a is the attenuation coefficient matrix of the first biological sample under X-rays with energy E k .

[0114] In this embodiment, step S400 includes:

[0115] The mass attenuation coefficient of each component of the first biological sample and the first attenuation coefficient matrix corresponding to the biological sample to be measured are input into the following formula to obtain the mass density distribution matrix of each component in the biological sample to be measured:

[0116]

[0117] Wherein, α j (E k ) is the mass attenuation coefficient of the substance j to X-rays with energy E k solved in step S300, B j is the mass density distribution matrix corresponding to each component of the biological sample to be measured, and μ ba first attenuation coefficient matrix of the biological sample to be measured under X-rays with energy E k .

[0118] Further, assuming that water exists in the biological sample to be measured, the mass attenuation coefficients of each component of the first biological sample and the first attenuation coefficient matrix of the biological sample to be measured are input into the following formula:

[0119]

[0120] where B w is the mass density of water, a j (E k ) is the mass attenuation coefficient of substance j to X-rays with energy E k solved in step S300, a w (E k ) is the mass attenuation coefficient of water, B j is the mass density of each of the other components, and m b is the first attenuation coefficient matrix of the biological sample to be measured under X-rays with energy E k .

[0121] The mass attenuation coefficients of water to X-rays with different energies can be obtained from the website of the National Institute of Standards and Technology (NIST), and for a biological sample, the density of water in the body can be considered to be constant at normal temperature and pressure, and the linear attenuation coefficient of water only changes with the mass.

[0122] Solving the above unknown quantities requires that the number of energy regions collected by the photon counting detector be greater than or equal to four.

[0123] The non-destructive method for obtaining the component distribution of a biological sample according to the embodiment avoids the destruction of the sample by traditional detection methods, and can accurately obtain the mass density distribution matrix of the internal components of the biological sample to be measured, thereby providing strong support for in-depth understanding of the characteristics of the biological sample.

[0124] Embodiment 3

[0125] The non-destructive method for obtaining the component distribution of a biological sample according to the embodiment includes:

[0126] Step S100, obtaining an energy spectrum CT image of the biological sample to be measured, and obtaining a first attenuation coefficient matrix corresponding to the biological sample to be measured according to the energy spectrum CT image of the biological sample to be measured;

[0127] Step S200, selecting a first biological sample with the same components as the biological sample to be measured, and obtaining an attenuation coefficient matrix and a density matrix of each component of the first biological sample;

[0128] Step S300, inputting the attenuation coefficient matrix and the density matrix of each component into the quality attenuation coefficient acquisition model set in advance, to acquire the quality attenuation coefficient of each component of the first biological sample;

[0129] Step S400, inputting the quality attenuation coefficient of each component of the first biological sample and the first attenuation coefficient matrix corresponding to the biological sample to be measured into the quality density distribution acquisition model, to acquire the quality density distribution matrix of each component of the biological sample to be measured.

[0130] Suppose that the quality attenuation coefficient of the biological sample with the same component distribution as the biological sample to be measured has been acquired before the acquisition of the component distribution of the biological sample to be measured, then the step S100 and the step S400 can be directly performed to acquire the quality density distribution matrix of each component of the biological sample to be measured.

[0131] For example, the biological sample to be measured is wheat in the grain filling stage, at this time, the mature wheat can be used as the first biological sample to perform the steps in the embodiment 2, to acquire the quality attenuation coefficient of each component of the mature wheat, and to perform the step S400 to acquire the quality density distribution matrix of the wheat in the grain filling stage.

[0132] At this time, if the component distribution of different varieties of wheat is to be acquired, the quality attenuation coefficient of each component of the wheat that has been acquired can be directly used, at this time, only the first attenuation coefficient matrix of the wheat to be measured needs to be acquired, and the first attenuation coefficient matrix and the quality attenuation coefficient of each component of the mature wheat are input into the quality density acquisition model to obtain the quality density distribution matrix of each component of the wheat to be measured.

[0133] The method of the embodiment can directly call the quality attenuation coefficient library of each component for the biological sample of the same component once the library is established, which greatly improves the analysis efficiency and the comparability of data.

[0134] In the description of the present application, it should be understood that the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0135] In the present application, unless specifically defined otherwise, the terms "mounting", "connected", "connection", "fixed", "unfixed", and the like should be construed broadly, for example, can be fixed connection, can be detachable connection, or integral; can be mechanical connection, can be electrical connection; can be direct connection, can be indirect connection through an intermediate medium; can be internal communication of two elements or interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0136] In the present application, unless specifically defined otherwise, the first feature is "on" or "under" the second feature, which can be direct contact between the first and second features, or indirect contact between the first and second features through an intermediate medium. Moreover, the first feature is "above", "over" and "on" the second feature, which can be directly above or obliquely above the first feature, or only indicates that the first feature is higher than the second feature in horizontal height. The first feature is "below", "under" and "under" the second feature, which can be directly below or obliquely below the first feature, or only indicates that the first feature is lower than the second feature in horizontal height.

[0137] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in the present application and the features of the different embodiments or examples without contradiction.

[0138] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can modify, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for obtaining the component distribution of a non-destructive biological sample, characterized in that, include: S100. Obtain the energy spectrum CT image of the biological sample to be tested, and obtain the first attenuation coefficient matrix corresponding to the biological sample to be tested based on the energy spectrum CT image of the biological sample to be tested. S200. Select a first biological sample with the same composition as the biological sample to be tested, and obtain the attenuation coefficient matrix and density matrix of each component of the first biological sample. S300. Input the attenuation coefficient matrix and density matrix of each component into the pre-set mass attenuation coefficient acquisition model to obtain the mass attenuation coefficient of each component of the first biological sample. S400: Input the mass decay coefficients of each component of the first biological sample and the first decay coefficient matrix corresponding to the biological sample to be tested into the mass density distribution acquisition model to obtain the mass density distribution matrix of each component of the biological sample to be tested. The S400 includes: Input the mass decay coefficients of each component of the first biological sample and the first decay coefficient matrix corresponding to the biological sample to be tested into the following formula to obtain the mass density distribution matrix of each component of the biological sample to be tested: Where, α j (E k The energy of substance j obtained in step S300 is E. k The mass attenuation coefficient of X-rays, B j Let μ be the mass density distribution matrix corresponding to each component of the biological sample to be tested. b For the biological sample to be tested at an energy of E k The first attenuation coefficient matrix under X-rays.

2. The method for obtaining the non-destructive compositional distribution of biological samples according to claim 1, characterized in that, Specifically, S200 includes: S210a. Obtain the energy spectrum CT image of the first biological sample, and obtain the attenuation coefficient matrix corresponding to the first biological sample based on the energy spectrum CT image of the first biological sample. S220a. Perform three-dimensional spatial component analysis on the first biological sample to obtain the first density matrix; S230a. Align the first density matrix with data and matrix dimensions to obtain the density matrix of each component of the first biological sample.

3. The method for obtaining the non-destructive compositional distribution of biological samples according to claim 1, characterized in that, Alternatively, S200 specifically includes: S210b: Acquire the energy-dispersive CT image of the first biological sample; S220b: Obtain a slice of the first biological sample and an optical image of the slice; perform component detection on the slice of the first biological sample; and obtain a first density matrix corresponding to the optical image of the slice of the first biological sample and an attenuation coefficient matrix of the energy spectrum CT image corresponding to the slice. S230b: Align the first density matrix with data and matrix dimensions to obtain the density matrix of each component in the first biological sample.

4. The method for obtaining the non-destructive compositional distribution of biological samples according to claim 3, characterized in that, The S220b includes: S221b: Obtain a slice of the first biological sample and an optical image of the slice; perform fluorescence staining, spatial proteomics analysis, or spatial metabolomics analysis on the slice; and obtain a first density matrix corresponding to the optical image of the slice of the first biological sample. S222b. Based on the optical image of the slice, obtain a two-dimensional cross-sectional image of the energy spectrum CT image of the first biological sample corresponding to the optical image of the slice, and obtain the attenuation coefficient matrix of the two-dimensional cross-sectional image as the attenuation coefficient matrix of each component of the first biological sample.

5. The method for obtaining the non-destructive compositional distribution of biological samples according to claim 4, characterized in that, Specifically, S222b includes: Feature points on the slice optical image are obtained, and corresponding feature points on the energy spectrum CT image are obtained based on the feature points on the slice optical image. Based on the same feature points on the energy spectrum CT image, the two-dimensional cross-sectional image of the same feature points is obtained as the energy spectrum CT image of the slice, and the corresponding attenuation coefficient matrix is ​​obtained as the attenuation coefficient matrix of each component of the first biological sample based on the energy spectrum CT image of the slice.

6. The method for obtaining the non-destructive compositional distribution of biological samples according to claim 5, characterized in that, The feature points are obtained through a feature detection algorithm; the feature detection algorithm is the Harris corner detection algorithm, the scale space extremum detection algorithm, the edge detection algorithm, or the local feature detection algorithm. The number of feature points is greater than 3.

7. The method for obtaining the non-destructive distribution of biological sample components according to claim 2 or 3, characterized in that, The density matrix of the first biological sample is aligned by data alignment and matrix dimension alignment to obtain the density matrix of each component, including: Obtain the particle points of the attenuation coefficient matrix corresponding to the first density matrix and the first biological sample; based on the particle points, obtain the corresponding translation matrix and rotation transformation matrix; based on the translation matrix and rotation transformation matrix, obtain the coordinate transformation matrix. Based on the coordinate transformation matrix, the first density matrix is ​​aligned to obtain the aligned density matrix.

8. The method for obtaining the non-destructive compositional distribution of biological samples according to claim 7, characterized in that, The transformation matrix is: Where F represents the coordinate transformation matrix, R represents the rotation transformation matrix, and T represents the translation matrix.

9. The method for obtaining the non-destructive compositional distribution of biological samples according to claim 1, characterized in that, Specifically, S300 includes: Input the attenuation coefficient matrix and density matrix into the following formula to obtain the mass attenuation coefficients of each component of the first biological sample: Among them, a j ρ is a constant that does not change with spatial position. rj The density matrix corresponds to the density of substance j in the first biological sample obtained in step S200, α j (E k () represents the energy E of substance j in the first biological sample. k The mass attenuation coefficient of X-rays, μ a The first biological sample at energy E k The attenuation coefficient matrix under X-rays.

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