Method for acquiring lossless biological sample component distribution

Abstract

The invention relates to the technical field of biological analysis, in particular to a lossless biological sample component distribution acquisition method which comprises the following steps: acquiring an energy spectrum CT image of a biological sample to be detected, and acquiring a first attenuation coefficient matrix of the biological sample to be detected according to the energy spectrum CT image; selecting a first biological sample with the same components as the biological sample to be detected, and 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 acquisition model, and acquiring a 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 a first attenuation coefficient matrix corresponding to the biological sample to be detected into a mass density distribution acquisition model to obtain a mass density distribution matrix of each component in the biological sample to be detected. The mass density of a single component or a compound in a biological sample can be measured, and the whole measurement process is simple and convenient.

Description

Technical Field

[0001] The present invention relates to the technical field of biological analysis, and in particular to a method for non-destructively acquiring component distribution of biological samples. Background Art

[0002] Life science research and the medical field need to understand the distribution of biological tissue components in order to perform functional analysis or judge lesions. For example, the vitality and growth and development status of plant seeds can be judged based on the distribution of their components. Evaluating the coronary artery lumen and analyzing plaque components are the main diagnostic basis for coronary artery pathology. Non-destructive methods for component analysis of biological samples include near-infrared spectroscopy, hyperspectral analysis, etc., which can be used to know the moisture content, main nutrient content or lesion status of plant samples. These methods can often only obtain the macroscopic material content of biological samples. Analysis methods with high spatial resolution are often lossy and require the sample to be sliced ​​and analyzed. In the field of life sciences, dynamic development analysis of living organisms and clinical applications require a method for obtaining non-destructive, high-resolution component distribution of tissues or samples.

[0003] Spectral CT technology uses detectors that can distinguish photon energy to detect the number of photons in different energy ranges within the X-ray spectrum, and obtain data from multiple energy ranges in one imaging. Since the absorption of X-rays by a substance is related to the energy of the ray, the collected multi-energy zone attenuation data can be decomposed to obtain material composition information. This method has been applied in the medical field to analyze the distribution of blood vessels, bones, contrast agents, etc.

[0004] The general method of solving the mass fraction of each substance mentioned above by spectral CT is to use pure standard substances, which can be single-component substances or composites composed of multiple single components (such as a certain compound), collect their absorption characteristics of different energies respectively as references for spectral CT data decomposition, and then substitute them into the spectral CT data decomposition model of the mixture to find the spatial distribution of the content of different substances in the mixture. However, since the absorption differences of X-rays by the constituent substances of biological samples are relatively small and the composition is relatively complex, it is difficult to select calibration materials, and it is also difficult to obtain high-purity single components or composites for mass attenuation coefficient and mass density measurement.

[0005] In the prior art, similar substances with similar density and effective mass coefficient are generally used as calibration objects. It is difficult to find calibration objects with both quantities close to the target substance. Often, only such substances or a calibration object with a quantity close to the target substance can be found as reference components for spectral CT decomposition. In this case, the results obtained have large errors. In addition, the current decomposition of multi-substance spectral CT images often relies on the use of contrast agents, which are prone to damage the biological sample itself.

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

[0007] 1. Technical issues to be resolved

[0008] In view of the above-mentioned shortcomings and deficiencies in the prior art, the present invention provides a non-destructive method for acquiring the distribution of biological sample components, which solves the problem in the prior art that it is difficult to perform spectral CT image separation of complex components of biological samples, and also solves the technical problem that it is difficult to obtain high-purity single components or complexes for measuring mass attenuation coefficient and mass density.

[0009] (II) Technical solution

[0010] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0011] In a first aspect, an embodiment of the present invention provides a method for non-destructively obtaining a biological sample component distribution, comprising:

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

[0013] S200, selecting a first biological sample having the same components as the biological sample to be tested, 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 density matrix of each component into a pre-set mass attenuation coefficient acquisition model to obtain 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 tested into a mass density distribution acquisition model to obtain the mass density distribution matrix of each component in the biological sample to be tested.

[0016] Optionally, the S200 specifically includes:

[0017] S21 0a. Acquire a spectral CT image of a first biological sample, and acquire an 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 to obtain a first density matrix;

[0019] S230a, aligning the data and the matrix dimensions of the first density matrix to obtain the density matrix of each component of the first biological sample.

[0020] Optionally, or, the S200 specifically includes:

[0021] S21 0b, acquiring a spectral CT image of the first biological sample;

[0022] S220b, obtaining a slice and an optical image of the slice of a first biological sample, performing component detection on the slice of the first biological sample, and obtaining 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;

[0023] S230b, aligning the data and the matrix dimensions of the first density matrix to obtain the density matrix of each component in the first biological sample.

[0024] Optionally, the S220b includes:

[0025] S221b, obtaining a slice and an optical image of the slice of a first biological sample, performing fluorescent staining, spatial proteomics analysis or spatial metabolomics analysis on the slice, and obtaining a first density matrix corresponding to the optical image of the slice of the first biological sample;

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

[0027] Optionally, the S222b specifically includes:

[0028] Acquire characteristic points on the slice optical image, and acquire corresponding identical characteristic points on the energy spectrum CT image according to the characteristic points on the slice optical image;

[0029] According to the same characteristic points corresponding to the energy spectrum CT image, the two-dimensional cross-sectional image where the same characteristic points are located is obtained as the energy spectrum CT image of the slice, and according to the energy spectrum CT image of the slice, the corresponding attenuation coefficient matrix is ​​obtained as the attenuation coefficient matrix of each component of the first biological sample.

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

[0031] The number of the characteristic points is greater than 3.

[0032] Optionally, performing data alignment and matrix dimension alignment on the first density matrix to obtain the density matrix of each component of the first biological sample includes:

[0033] Obtaining the mass points of the first density matrix and the attenuation coefficient matrix corresponding to the first biological sample, and obtaining the corresponding translation matrix and rotation transformation matrix according to the mass points; obtaining the coordinate transformation matrix 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 density matrix after data alignment.

[0035] Optionally, the transformation matrix is:

[0036]

[0037] Among them, F represents the coordinate transformation matrix, R represents the rotation change 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] Among them, a j is a constant that does not change with spatial position, ρ rj is a density matrix, corresponding to the density of substance j in the first biological sample obtained in step S200, α j (E k ) is the energy of substance j in the first biological sample, E k The X-ray mass attenuation coefficient, μ a For the first biological sample at energy E k The attenuation coefficient matrix under X-ray.

[0042] Optionally, the S400 includes:

[0043] The mass attenuation coefficients of the components of the first biological sample and the first attenuation coefficient matrix corresponding to the biological sample to be tested are input into the following formula to obtain the mass density distribution matrix of the components in the biological sample to be tested:

[0044]

[0045] Among them, α j (E k ) is the energy E of the substance j solved in step S300 kThe X-ray mass attenuation coefficient, B j is the mass density distribution matrix corresponding to each component of the biological sample to be tested, μ b The energy of the biological sample to be tested is E k The first attenuation coefficient matrix under X-rays.

[0046] (III) Beneficial effects

[0047] The beneficial effects of the present invention are as follows: a non-destructive method for acquiring the distribution of biological sample components of the present invention first acquires the energy spectrum CT image of the biological sample to be tested, and acquires the first attenuation coefficient matrix corresponding to the biological sample to be tested according to the energy spectrum CT image; a first biological sample having the same components as the biological sample to be tested is selected, and a model is acquired according to the mass attenuation coefficient to obtain the mass attenuation coefficient corresponding to each component in the biological sample to be tested, and finally a model is acquired according to the mass density to obtain the mass density distribution matrix of each component of the biological sample to be tested. Compared with the prior art, the present invention does not need to find a calibration object, and after measuring the first biological sample, the mass density of the biological sample to be tested with the same components can be obtained, and multiple calibrations are not required, which reduces the experimental workload and experimental operation process, making the entire measurement process more convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The figure is a flow chart of a method for non-destructively acquiring component distribution of a biological sample according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0050] Among the methods for solving material composition information in spectral CT, the most commonly used method in practical applications is material recognition based on the image domain, which is achieved by material decomposition of the reconstructed data in multiple energy zones, and is also called a post-processing material decomposition algorithm. The attenuation of X-rays passing through matter is related to the energy of the photons, and is also related to the atomic number and density of the material. The larger the atomic number and the more electrons there are, 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 and energy of the material. It is reflected in the reconstructed image, and μ(r, E) varies with the spatial position r and the distribution of energy E.

[0051] Modern spectral CT generally adopts a single radiation source and a photon counting detector with energy partitioning function, which can collect projections of multiple energy zones at the same time.

[0052] An embodiment of the present invention proposes a non-destructive method for acquiring component distribution of a biological sample. In order to solve the technical problem in the prior art that it is difficult to measure the mass density of a single component or a complex with high purity due to the complexity of the components and the difficulty in selecting calibration objects when performing component separation, the present invention obtains a first attenuation coefficient matrix of the biological sample to be tested, and obtains the mass attenuation coefficients of each component of the first biological sample with the same composition as the biological sample to be tested, and inputs the first attenuation coefficient matrix and the corresponding mass attenuation coefficients of each component into a mass density acquisition model to obtain the mass density of each component in the biological sample to be tested; the present invention does not need to find a calibration object. After measuring the first biological sample, the mass density of the biological sample to be tested with the same component can be obtained, and multiple calibrations are not required, which reduces the experimental workload and experimental operation process, making the entire measurement process more convenient.

[0053] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0054] Specific embodiment description part

[0055] Example 1

[0056] See also Figure 1 , a non-destructive method for obtaining the distribution of components in a biological sample in this embodiment includes:

[0057] S100, obtaining a spectral CT image of a biological sample to be tested, and obtaining a first attenuation coefficient matrix corresponding to the biological sample to be tested according to the spectral CT image of the biological sample to be tested;

[0058] S200, selecting a first biological sample having the same components as the biological sample to be tested, and obtaining an attenuation coefficient matrix and a density matrix of each component of the first biological sample;

[0059] S300, inputting the attenuation coefficient matrix and density matrix of each component into a pre-set mass attenuation coefficient acquisition model to obtain the mass attenuation coefficient of each component of the first biological sample;

[0060] 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 tested into a mass density distribution acquisition model to obtain the mass density distribution matrix of each component in the biological sample to be tested.

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

[0062] The method of this embodiment does not require the search for calibration objects. After measuring the first biological sample, the mass density of the biological sample to be measured with the same components can be obtained without multiple calibrations, which reduces the experimental workload and experimental operation procedures, making the entire measurement process more convenient.

[0063] Example 2

[0064] A non-destructive method for obtaining the distribution of biological sample components in this embodiment includes:

[0065] Step S100, obtaining a spectral CT image of the biological sample to be tested, and obtaining a first attenuation coefficient matrix corresponding to the biological sample to be tested according to the spectral CT image of the biological sample to be tested;

[0066] Step S200, selecting a first biological sample having the same components as the biological sample to be tested, and obtaining an attenuation coefficient matrix and a density matrix of each component of the first biological sample;

[0067] Step S300, inputting the attenuation coefficient matrix and density matrix of each component into a pre-set mass attenuation coefficient acquisition model to obtain the mass attenuation coefficient of each component of the first biological sample;

[0068] Step S400: input 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 tested into a mass density distribution acquisition model to obtain the mass density distribution matrix of each component in the biological sample to be tested.

[0069] Before obtaining the first attenuation coefficient matrix, the biological sample to be tested needs to be dried to completely evaporate the water in the biological sample to be tested while keeping other substances such as proteins and lipids unchanged. An energy spectrum CT image of the biological sample to be tested after drying is obtained, and the first attenuation coefficient matrix corresponding to the biological sample to be tested is obtained based on the energy spectrum CT image.

[0070] Similarly, when obtaining the attenuation coefficient matrix and density matrix of each component of the first biological sample, it is also necessary to perform a drying process so that the water in the first biological sample can be completely evaporated while keeping other substances such as proteins and lipids unchanged.

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

[0072] Step S210a, obtaining a spectral CT image of a first biological sample, and obtaining an 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 obtain a first density matrix;

[0074] Step S230a: align the data and the matrix dimensions of the first density matrix to obtain the density matrix of each component of the first biological sample.

[0075] In a 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 obtain a first density matrix.

[0076] Specifically, NMR applies an external magnetic field to polarize the nuclei in the sample, and then applies a radio frequency pulse to flip them. When the radio frequency pulse is turned off, the nuclei release energy and return to their ground state. This 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 then the first density matrix is ​​constructed.

[0077] Spatial metabolomics combines methods such as 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 a precisely controlled laser to detect the metabolite composition at different locations, thereby constructing a density matrix of each component in the sample.

[0078] Spatial proteomics uses mass spectrometry technology to directly obtain information about proteins, determine their spatial distribution, and further process them to generate a density matrix that reflects protein density.

[0079] Alternatively, step S200 specifically includes:

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

[0081] Step S220b, obtaining a slice and an optical image of the slice of the first biological sample, performing component detection on the slice of the first biological sample, and obtaining 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;

[0082] Step S230b: align the data and the matrix dimensions of the first density matrix 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 on a three-dimensional level, and the other is processing on a two-dimensional level.

[0084] In this embodiment, step S220b includes:

[0085] Step S221b, obtaining a slice and an optical image of the slice of the first biological sample, performing fluorescent staining, spatial proteomics analysis or spatial metabolomics analysis on the slice, and obtaining a first density matrix corresponding to the optical image of the slice of the first biological sample;

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

[0087] In this embodiment, a frozen sectioning technique is used to prepare a section of the first biological sample, and an optical microscope is used to image the section to obtain an optical image of the section of the first biological sample.

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

[0089] Step S222b specifically includes:

[0090] Acquire characteristic points on the slice optical image, and acquire the same corresponding characteristic points on the energy spectrum CT image according to the characteristic points on the slice optical image;

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

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

[0093] The number of feature points is greater than 3.

[0094] In the specific implementation process, at least three non-collinear feature points are required to determine a plane; by setting more than three feature points, it can ensure that there is enough information to accurately align the data during the registration process, and it can also improve the robustness and accuracy of the registration.

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

[0096] In a specific implementation process, aligning the data and the matrix dimension of the first density matrix to obtain the density matrix of each component of the first biological sample includes:

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

[0099] In this embodiment, the method also includes: if the resolution of the first density matrix is ​​lower than the resolution of the attenuation coefficient matrix, data interpolation is required to increase the resolution of the first density matrix so that it matches 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 so that it matches the resolution of the attenuation coefficient matrix.

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

[0101] If P<M or Q<N, a bilinear interpolation method is used to increase the resolution of the first density matrix; if P is greater than M or Q>N, the resolution of the first density matrix is ​​reduced by merging adjacent pixels.

[0102] The second density matrix obtained after the adjustment has the same dimension as the second attenuation coefficient matrix, which is M×N.

[0103] Furthermore, the transformation matrix is:

[0104]

[0105] Among them, F represents the coordinate transformation matrix, R represents the rotation change matrix, and T represents the translation matrix.

[0106] In the specific implementation process, for example, assume that a certain feature point of the slice optical image is P(x, y), and the corresponding feature point of the point on the energy spectrum CT image is P′(x′, y′); find the centroid of the feature point is M(x, y) and M′(x′, y′), and construct a translation matrix based on the centroid, so that M′(x, y)=T×M(x′, y′);

[0107] In addition, according to the linear equations composed of multiple points

[0108]

[0109] Get the rotation transformation matrix R.

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

[0114] In this embodiment, step S400 includes:

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

[0116]

[0117] Among them, α j (E k ) is the energy E of the substance j solved in step S300 k The X-ray mass attenuation coefficient, B j is the mass density distribution matrix corresponding to each component of the biological sample to be tested, μ bThe energy of the biological sample to be tested is E k The first attenuation coefficient matrix under X-rays.

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

[0119]

[0120] Among them, B w is the mass density of water, α j (E k ) is the energy E of the substance j solved in step S300 k The X-ray mass attenuation coefficient, α w (E k ) is the mass attenuation coefficient of water, B j is the mass density of other components, μ b The energy of the biological sample to be tested is E k The first attenuation coefficient matrix under X-rays.

[0121] Among them, the mass attenuation coefficient of water for X-rays of different energies can be obtained from the website of the National Institute of Standards and Technology (NIST). Considering that for biological samples, the body can be regarded as normal temperature and pressure, the density of water in this environment is unchanged, and its linear attenuation coefficient only changes with mass.

[0122] To solve the above unknowns, the number of collection energy zones of the photon counting detector must be greater than or equal to four.

[0123] The present embodiment provides a non-destructive method for acquiring the distribution of biological sample components, which avoids the destruction of samples by traditional detection methods, and can accurately acquire the mass density distribution matrix of the internal components of the biological sample to be tested, providing strong support for in-depth understanding of the characteristics of the biological sample.

[0124] Example 3

[0125] A non-destructive method for obtaining the distribution of biological sample components in this embodiment includes:

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

[0127] Step S200, selecting a first biological sample having the same components as the biological sample to be tested, 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 density matrix of each component into a pre-set mass attenuation coefficient acquisition model to obtain the mass attenuation coefficient of each component of the first biological sample;

[0129] Step S400: input 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 tested into a mass density distribution acquisition model to obtain the mass density distribution matrix of each component in the biological sample to be tested.

[0130] Assuming that the mass attenuation coefficient of a biological sample with the same component distribution as the biological sample to be tested has been obtained before the above steps are performed to obtain the component distribution of the biological sample to be tested, steps S100 and S400 can be directly performed to obtain the mass density distribution matrix of each component in the biological sample to be tested.

[0131] For example, the biological sample to be tested is wheat in the grain filling stage. In this case, wheat in the mature stage can be used as the first biological sample, and each step in Example 2 is performed to obtain the mass attenuation coefficient of each component of the wheat in the mature stage, and step S400 is performed to obtain the mass density distribution matrix of the wheat in the grain filling stage;

[0132] At this time, if you want to obtain the component distribution of different varieties of wheat, you can directly use the mass attenuation coefficients of each component of the wheat that have been obtained. At this time, you only need to obtain the first attenuation coefficient matrix of the wheat variety to be tested, and input the first attenuation coefficient matrix and the mass attenuation coefficients of each component of the wheat in the mature stage into the mass density acquisition model to obtain the mass density distribution matrix of each component in the wheat variety to be tested.

[0133] For biological samples of the same component, once the mass attenuation coefficient library of various components is established, subsequent sample analysis can directly call it, which greatly improves the analysis efficiency and data comparability.

[0134] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0135] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0136] In the present invention, unless otherwise clearly specified and limited, when a first feature is “on” or “below” a second feature, it may be that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. Moreover, when a first feature is “above”, “above” or “above” a second feature, it may be that the first feature is directly above or obliquely above the second feature, or it may simply mean that the first feature is higher in level than the second feature. When a first feature is “below”, “below” or “below” a second feature, it may be that the first feature is directly below or obliquely below the second feature, or it may simply mean that the first feature is lower in level than the second feature.

[0137] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" etc. 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 invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0138] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may alter, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A non-destructive method for obtaining the distribution of biological sample components, characterized in that: include: S100, obtaining a spectral CT image of a biological sample to be tested, and obtaining a first attenuation coefficient matrix corresponding to the biological sample to be tested according to the spectral CT image of the biological sample to be tested; S200, selecting a first biological sample having the same components as the biological sample to be tested, and obtaining an attenuation coefficient matrix and a density matrix of each component of the first biological sample; S300, inputting the attenuation coefficient matrix and density matrix of each component into a pre-set mass attenuation coefficient acquisition model to obtain the mass attenuation coefficient of each component of the first biological sample; 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 tested into a mass density distribution acquisition model to obtain the mass density distribution matrix of each component in the biological sample to be tested.

2. The method for non-destructively obtaining the distribution of biological sample components according to claim 1, characterized in that: The S200 specifically includes: S21 0a. Acquire a spectral CT image of a first biological sample, and acquire an attenuation coefficient matrix corresponding to the first biological sample according to the spectral CT image of the first biological sample; S220a, performing three-dimensional spatial component analysis on the first biological sample to obtain a first density matrix; S230a, performing data alignment and matrix dimension alignment on the first density matrix to obtain density matrices of various components of the first biological sample.

3. The method for non-destructively obtaining the distribution of biological sample components according to claim 1, characterized in that: Alternatively, the S200 specifically includes: S21 0b, acquiring a spectral CT image of the first biological sample; S220b, obtaining a slice and an optical image of the slice of a first biological sample, performing component detection on the slice of the first biological sample, and obtaining 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, aligning the data and the matrix dimensions of the first density matrix to obtain the density matrix of each component in the first biological sample.

4. The method for non-destructively obtaining the distribution of biological sample components according to claim 3, characterized in that: The S220b includes: S221b, obtaining a slice and an optical image of the slice of a first biological sample, performing fluorescent staining, spatial proteomics analysis or spatial metabolomics analysis on the slice, and obtaining a first density matrix corresponding to the optical image of the slice of the first biological sample; S222b, based on the slice optical image, obtain a two-dimensional cross-sectional image of the energy spectrum CT image of the first biological sample corresponding to the slice optical image, and obtain an 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 non-destructive biological sample component analysis according to claim 4, characterized in that: The S222b specifically includes: Acquire characteristic points on the slice optical image, and acquire corresponding identical characteristic points on the energy spectrum CT image according to the characteristic points on the slice optical image; According to the same characteristic points corresponding to the energy spectrum CT image, the two-dimensional cross-sectional image where the same characteristic points are located is obtained as the energy spectrum CT image of the slice, and according to the energy spectrum CT image of the slice, the corresponding attenuation coefficient matrix is ​​obtained as the attenuation coefficient matrix of each component of the first biological sample.

6. The method for obtaining non-destructive biological sample component analysis according to claim 5, characterized in that: The feature points are obtained by a feature detection algorithm; the feature detection algorithm is a Harris corner detection algorithm, a scale space extreme value detection algorithm, an edge detection algorithm or a local feature detection algorithm; The number of the characteristic points is greater than 3.

7. The method for non-destructively obtaining the component distribution of a biological sample according to claim 2 or 3, characterized in that: Performing data alignment and matrix dimension alignment on the first density matrix to obtain the density matrix of each component of the first biological sample includes: Obtaining the mass points of the first density matrix and the attenuation coefficient matrix corresponding to the first biological sample, and obtaining the corresponding translation matrix and rotation transformation matrix according to the mass points; obtaining the coordinate transformation matrix according to the translation matrix and the rotation transformation matrix; According to the coordinate transformation matrix, the first density matrix is ​​data aligned to obtain a density matrix after data alignment.

8. The method for non-destructively obtaining the distribution of biological sample components according to claim 7, characterized in that: The transformation matrix is: Among them, F represents the coordinate transformation matrix, R represents the rotation change matrix, and T represents the translation matrix.

9. The method for obtaining non-destructive biological sample component analysis according to claim 1, characterized in that: The S300 specifically includes: 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: Among them, a j is a constant that does not change with spatial position, ρ rj is a density matrix, corresponding to the density of substance j in the first biological sample obtained in step S200, α j (E k ) is the energy of substance j in the first biological sample E k The X-ray mass attenuation coefficient, μ a For the first biological sample at energy E k The attenuation coefficient matrix under X-ray.

10. The method for non-destructive biological sample component analysis according to claim 1, characterized in that: The S400 includes: The mass attenuation coefficients of the components of the first biological sample and the first attenuation coefficient matrix corresponding to the biological sample to be tested are input into the following formula to obtain the mass density distribution matrix of the components in the biological sample to be tested: Among them, α j (E k ) is the energy E of the substance j solved in step S300 k The X-ray mass attenuation coefficient, B j is the mass density distribution matrix corresponding to each component of the biological sample to be tested, μ b The energy of the biological sample to be tested is E k The first attenuation coefficient matrix under X-rays.

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