A microstructure analysis method, system, electronic device, storage medium, and computer program product

Abstract

This disclosure relates to a microstructure analysis method, system, electronic device, storage medium, and computer program product. The method includes: scanning and imaging a target sample using an X-ray multi-characteristic grating imaging device to determine the target feature signal of the target sample; performing single-energy spectral structural decomposition on the target sample according to a preset continuous mapping relationship and the target feature signal to determine the equivalent thickness distribution of the target sample relative to a preset reference material; and determining the corresponding microstructure quantitative analysis result of the target sample according to a preset quantitative relationship model and the equivalent thickness distribution to reflect the microstructure size characteristics of the target sample. This disclosure does not require increasing the X-ray radiation dose of the X-ray multi-characteristic grating imaging device and can output microstructure quantitative analysis results with clear physical meaning in scenarios with low radiation dose and low spatial resolution. It is applicable to various fields requiring quantitative identification of microstructures and has high accuracy and versatility.

Description

Technical Field

[0001] This disclosure relates to the field of X-ray multi-characteristic imaging technology, and in particular to a microstructure analysis method, system, electronic device, storage medium, and computer program product. Background Technology

[0002] With the rapid development of radiological diagnostics and advanced materials science, the demand for refined tomographic imaging of biological tissues and novel materials at the microscopic and mesoscopic scales is increasingly prominent. However, current technologies, such as X-ray intensity attenuation-based computed tomography (CT), cannot extract microscopic / mesoscopic structural information of samples under low-resolution conditions. While high-resolution CT (HRCT) can directly extract microscopic / mesoscopic structural information, it requires a significantly increased X-ray radiation dose, posing a serious safety risk to the samples, especially biological tissues. Therefore, extracting microscopic / mesoscopic structural information of samples under conventional X-ray imaging conditions is crucial for research fields such as radiological diagnostics and advanced materials science. Summary of the Invention

[0003] In view of this, this disclosure proposes a technical solution for a microstructure analysis method, system, electronic device, storage medium, and computer program product.

[0004] According to one aspect of this disclosure, a microstructure analysis method is provided, comprising: scanning and imaging a target sample using an X-ray multi-characteristic grating imaging device to determine the target feature signal of the target sample; performing single-energy spectral structural decomposition on the target sample according to a preset continuous mapping relationship and the target feature signal to determine the equivalent thickness distribution of the target sample relative to a preset reference material, wherein the preset reference material is composed of a combination of first and second reference microspheres of different sizes, and the preset continuous mapping relationship represents the relationship between the feature signal of the preset reference material and its material thickness; and determining the microstructure quantitative analysis result corresponding to the target sample according to a preset quantitative relationship model and the equivalent thickness distribution, wherein the preset quantitative relationship model is used to indicate the size relationship between the target sample and the first and second reference microspheres, and the microstructure quantitative analysis result is used to reflect the microstructure size characteristics of the target sample.

[0005] In one possible implementation, the X-ray multi-feature grating imaging device is an imaging device based on the Talbot-Lau interferometer structure, or a grating imaging device based on the geometric projection method.

[0006] In one possible implementation, the step of performing single-energy spectral structure decomposition on the target sample according to a preset continuous mapping relationship and the target feature signal to determine the equivalent thickness distribution of the target sample relative to a preset reference material includes: constructing an equivalent thickness optimization model corresponding to the target sample according to the preset continuous mapping relationship and the target feature signal; optimizing and solving the equivalent thickness optimization model to determine the equivalent thickness distribution.

[0007] In one possible implementation, determining the quantitative analysis result of the microstructure corresponding to the target sample based on the preset quantitative relationship model and the equivalent thickness distribution includes: determining the weight distribution of the first reference microsphere and the weight distribution of the second reference microsphere based on the equivalent thickness distribution; and determining the quantitative analysis result of the microstructure based on the preset quantitative relationship model, the size and weight distribution of the first reference microsphere, and the size and weight distribution of the second reference microsphere.

[0008] In one possible implementation, the method further includes: based on the X-ray multi-feature grating imaging device, scanning and imaging the preset reference material with multiple preset material thicknesses to determine a discrete sample dataset, wherein the discrete sample dataset includes sample feature signals corresponding to the preset reference material with each preset material thickness; and using a weighted polynomial fitting method to determine the preset continuous mapping relationship based on the discrete sample dataset.

[0009] In one possible implementation, the step of using the X-ray multi-characteristic grating imaging device to scan and image the preset reference material with multiple preset material thicknesses to determine a discrete sample dataset includes: for any preset reference material with a preset material thickness, using the X-ray multi-characteristic grating imaging device to scan and image the preset reference material with that preset material thickness, and determining an initial feature signal corresponding to the preset reference material with that preset material thickness; using the X-ray multi-characteristic grating imaging device to scan and image a reference material with that preset material thickness, and determining a reference feature signal corresponding to the reference material with that preset material thickness; and correcting the feature signal corresponding to the preset reference material with that preset material thickness based on the reference feature signal, and determining a sample feature signal corresponding to the preset reference material with that preset material thickness.

[0010] According to another aspect of this disclosure, a microstructure analysis system is provided, comprising: an imaging module for scanning and imaging a target sample based on an X-ray multi-feature grating imaging device to determine the target feature signal of the target sample; a single-energy spectral structure decomposition module for performing single-energy spectral structure decomposition on the target sample according to a preset continuous mapping relationship and the target feature signal to determine the equivalent thickness distribution of the target sample relative to a preset reference material, wherein the preset reference material is composed of a combination of first and second reference microspheres of different sizes, and the preset continuous mapping relationship represents the relationship between the feature signal of the preset reference material and its material thickness; and a quantitative structure analysis module for determining the microstructure quantitative analysis result corresponding to the target sample according to a preset quantitative relationship model and the equivalent thickness distribution, wherein the preset quantitative relationship model is used to indicate the size relationship between the target sample and the first and second reference microspheres, and the microstructure quantitative analysis result is used to reflect the microstructure size characteristics of the target sample.

[0011] According to another aspect of this disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method.

[0012] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method.

[0013] According to another aspect of this disclosure, a computer program product is provided, including a computer program or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0014] In this embodiment, based on an X-ray multi-characteristic grating imaging device, the target sample is scanned and imaged. After determining the target feature signal of the target sample, a pre-defined continuous mapping relationship representing the relationship between the feature signal of the pre-defined reference material and its material thickness, along with the target feature signal, is used to perform single-energy spectral structural decomposition on the target sample. By jointly utilizing the nonlinear relationship between different signals in the target feature signal, the equivalent thickness distribution of the target sample relative to the pre-defined reference material is determined. This directly avoids the information redundancy and decomposition problems caused by the linear correlation characteristics of the linear diffusion coefficient of microspheres in commonly used dual-energy CT technology, thereby avoiding the problem of failure of microstructure analysis using dual-energy CT technology in specific scenarios in the prior art. Based on the pre-defined quantitative relationship model and the equivalent thickness distribution used to indicate the size relationship between the target sample and the first and second reference microspheres, the quantitative analysis results of the microstructure corresponding to the target sample with clear physical meaning can be determined to reflect the microstructure size characteristics of the target sample, rather than being limited to qualitative or semi-quantitative analysis of the microstructure size based on the strength of the target feature signal. This increases the adaptability of microstructure analysis to high-precision analysis scenarios such as accurate disease staging. On the other hand, by utilizing the dark-field signal, which is sensitive to sub-pixel-level structure in the target feature signal, as the basis for inversion micro-size analysis, the micro-structure analysis method disclosed herein does not require increasing the X-ray radiation dose to improve the spatial resolution of the X-ray multi-characteristic grating imaging device. It can achieve quantitative characterization of micro-structures far below pixel size under common X-ray radiation dose and imaging device spatial resolution scenarios, improving the versatility of micro-structure analysis. It can be applied to various fields that require quantitative identification of micro-structures, such as quantitative assessment of alveolar structure in the lungs, bone quality assessment, stone composition analysis, and micro-characterization of heterogeneous materials in materials science, and has high technology transfer capability. At the same time, in the field of disease diagnosis, it can better balance the contradiction between acquiring diagnostic information and patient radiation safety.

[0015] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0016] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0017] Figure 1 A flowchart illustrating a microstructure analysis method according to an embodiment of the present disclosure is shown.

[0018] Figure 2 This diagram illustrates a structural schematic of an imaging device based on a Talbot-Lau interferometer structure according to an embodiment of the present disclosure.

[0019] Figure 3 A schematic diagram illustrating a degree of freedom for adjusting a grating according to an embodiment of the present disclosure is shown;

[0020] Figure 4 A schematic diagram of a preset reference material according to an embodiment of the present disclosure is shown;

[0021] Figure 5 This diagram illustrates a scanning imaging of a preset reference material according to an embodiment of the present disclosure;

[0022] Figure 6 A polynomial-fit contour plot of a dark field signal according to an embodiment of the present disclosure is shown.

[0023] Figure 7 A polynomial-fit contour plot of an absorbed signal according to an embodiment of the present disclosure is shown.

[0024] Figure 8 A schematic diagram illustrating a microstructure analysis process according to an embodiment of the present disclosure is shown.

[0025] Figure 9 A schematic diagram showing the results of a quantitative analysis of microstructure according to an embodiment of the present disclosure is provided.

[0026] Figure 10 A block diagram of a microstructure analysis system according to an embodiment of the present disclosure is shown;

[0027] Figure 11 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0028] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0029] As used herein, the terms “comprising,” “including,” “having,” or variations thereof are open-ended and include one or more of the stated features, integrals, elements, steps, components, or functions, but do not exclude the presence or addition of one or more other features, integrals, elements, steps, components, functions, or groups thereof.

[0030] When an element is referred to as “connected,” “coupled,” “responding,” or a variation thereof relative to another element, it may be directly connected, coupled, or responding to another element, or there may be an intermediate element present.

[0031] Although the terms first, second, third, etc., may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another. Therefore, without departing from the teachings of the inventive concept, a first element / operation in some embodiments may be referred to as a second element / operation in other embodiments.

[0032] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0033] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0034] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant regions.

[0035] With the rapid development of diagnostic radiology and advanced materials science, the demand for refined tomographic imaging of biological tissues and novel materials at the microscopic and mesoscopic scales is increasingly prominent. For example, changes in the microscopic structural dimensions of alveoli in lung imaging are crucial for the early diagnosis of lung diseases such as emphysema. However, current technologies, such as X-ray intensity attenuation-based computed tomography (CT), cannot extract microscopic / mesoscopic structural information of samples under low-resolution conditions. While high-resolution CT (HRCT) can directly extract microscopic / mesoscopic structural information, it requires a significantly increased X-ray radiation dose, posing a serious safety risk to the sample, especially biological tissues. Therefore, extracting microscopic / mesoscopic structural information of samples under conventional X-ray imaging conditions is essential for research fields such as diagnostic radiology and advanced materials science.

[0036] X-ray multi-characteristic imaging (XMCI) overcomes the limitations of traditional X-ray imaging, which relies solely on absorption signals. Specifically, XMCI builds upon traditional X-ray absorption imaging by introducing three precision gratings to modulate X-rays into a structured beam with a period smaller than the detector pixel size. Through a phase-stepping method, the three microscopic effects—absorption, refraction, and small-angle scattering—that occur when the structured beam penetrates the sample (effects that cannot be directly resolved by the detector) are transformed into macroscopic signal changes measurable by each pixel of the detector. These changes are output as average intensity changes, phase shifts, and contrast changes in the phase-stepping curve. Based on this mechanism, XMCI can simultaneously extract three complementary physical information signals from the sample during a single imaging session: absorption imaging (ABI), phase-contrast imaging (PCI), and dark-field imaging (DFI). Phase contrast information can reflect the phase shift of the X-ray wavefront in media with different refractive indices, providing high image contrast for samples with weak absorption (e.g., biological soft tissues) and supporting material identification based on the effective number of atoms. However, due to the physical mechanism of the phase contrast imaging process, phase contrast information cannot provide dimensional information about the microstructure.

[0037] Dark-field signals originate from small-angle scattering of X-rays at the sub-pixel-level microstructure within the sample. Therefore, dark-field signals are highly sensitive to microstructural changes far below the spatial resolution of imaging devices, reflecting microstructural features that other information cannot provide. This gives them a unique advantage in applications such as early diagnosis of lung diseases. Specifically, the dense air-tissue interface in the lungs is an ideal source of small-angle scattering. Using corresponding dark-field signals, changes in the lung's microstructure caused by pathological changes such as alveolar rupture and fibrosis can be sensitively captured, enabling early diagnosis of lung diseases before morphological changes manifest as lesions. Furthermore, with the gradual realization of human-scale imaging through technologies such as dark-field chest X-rays and dark-field CT, the clinical translational potential of technologies for early diagnosis of lung diseases based on dark-field signals has been preliminarily validated. However, existing technologies for early diagnosis of lung diseases based on dark field signals mainly focus on semi-quantitative assessment of the signal intensity of dark field signals, and cannot convert dark field signals into microscopic structural size data with more explicit physical meaning, such as the size of alveoli, which limits the application of these technologies in the accurate staging of lung diseases.

[0038] In view of this, this disclosure provides a microstructure analysis method that, by jointly utilizing the nonlinear relationship between different signals in the target feature signal, determines the equivalent thickness distribution of the target sample relative to a preset reference material. This directly avoids the information redundancy and decomposition problems caused by the linear correlation characteristics of the linear diffusion coefficient of microspheres in commonly used dual-energy CT technology. This avoids the failure of microstructure analysis using dual-energy CT technology in specific scenarios in existing technologies, and outputs quantitative microstructure analysis results with clear physical meaning to reflect the microstructure size characteristics of the target sample, rather than being limited to qualitative or semi-quantitative analysis of microstructure size based on the strength of the target feature signal. This increases the adaptability of microstructure analysis to high-precision analysis scenarios such as accurate disease staging. The microstructure analysis method provided in this disclosure is described in detail below.

[0039] Figure 1 A flowchart illustrating a microstructure analysis method according to an embodiment of this disclosure is shown. This microstructure analysis method can be executed by an electronic device such as a terminal device or a server. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, wearable device, etc. The microstructure analysis method can be implemented by a processor calling computer-readable instructions stored in memory. Alternatively, the microstructure analysis method can be executed by a server. Figure 1 As shown, this microstructure analysis method includes:

[0040] In step S101, the target sample is scanned and imaged using an X-ray multi-feature grating imaging device to determine the target feature signal of the target sample.

[0041] The target sample can refer to a sample composed of at least one type of microsphere. Its specific form can be flexibly set according to actual usage requirements. For example, it can be a container filled with any type of microsphere, or a lung, bone, stone, composite material, porous medium, etc. This disclosure does not specifically limit its application. The microsphere can refer to any spherical or near-spherical entity that meets preset size conditions. Its specific form can be flexibly set according to actual usage requirements. For example, it can include alveoli, cells, polymers, etc. This disclosure does not specifically limit its application. The specific content of the preset size conditions can be flexibly set according to actual usage requirements. This disclosure does not specifically limit its application.

[0042] The target feature signals of the target sample may include the dark field signal and the absorption signal of the target sample; the dark field signal can reflect the small-angle scattering effect corresponding to the sub-pixel microstructure inside the target sample; the absorption signal can reflect the linear attenuation characteristics of the target sample. The specific forms of the dark field signal and the absorption signal can be flexibly set according to actual usage requirements. For example, the dark field signal may include the contrast change of the phase step curve (usually the degree of contrast decrease) and the dark field signal projection image, etc., and the absorption signal may include the average intensity change of the phase step curve (usually the average intensity attenuation) and the absorption signal projection image, etc. This disclosure does not specifically limit them.

[0043] An X-ray multi-feature grating imaging device can refer to any X-ray imaging device capable of acquiring multiple different characteristic signals of a target sample during the imaging process. Its specific form can be referred to in the implementation methods in related technologies. It should be able to acquire the dark field signal and absorption signal of the target object during the scanning process. This disclosure does not make specific limitations in this regard.

[0044] In one possible implementation, the X-ray multi-feature grating imaging device is an imaging device based on the Talbot-Lau interferometer structure, or a grating imaging device based on the geometric projection method.

[0045] Specifically, the imaging device based on the Talbot-Lau interference structure can achieve interferometric imaging by utilizing the Talbot effect and Lau effect in wave optics. For its specific form, please refer to the implementation methods in related technologies. This disclosure does not make any specific limitations on it.

[0046] Figure 2 A schematic diagram of an imaging device based on a Talbot-Lau interferometer structure according to an embodiment of the present disclosure is shown. Figure 2 As shown, the device 200 includes: an X-ray source 201, a source grating 202, a phase grating 203, an analysis grating 204, and a detector 205; the target sample 206 is placed between the phase grating 203 and the analysis grating 204.

[0047] The X-ray source 201 is used to emit initial X-rays. Its specific form can be flexibly set according to actual usage requirements, and this disclosure does not impose specific limitations on it.

[0048] The source grating 202 is used to modulate the initial X-rays into target X-rays that meet the spatial coherence requirements. Its specific form can be flexibly set according to actual application needs. For example, the source grating 202 can be set as an absorption grating, etc., and this disclosure does not specifically limit it. During the scanning imaging of the target sample using the phase-stepping method, the source grating 202 moves along the phase-stepping direction, and the target sample 206 rotates in a clockwise direction.

[0049] The specific form of the absorbing material used in the absorption grating can be flexibly set according to actual usage requirements. For example, it can be set to materials with high absorption properties such as gold (Au) and tungsten (Wu). This disclosure does not make specific limitations in this regard.

[0050] The phase grating 203 is used to modulate the phase of the target X-rays to obtain periodic intensity stripes. Its specific form can be referred to the implementation in related technologies. For example, it can be set as a grating with silicon (Si) as the phase shift material. This disclosure does not specifically limit it. The target sample 206 will interact with the intensity stripes, causing the stripes to be distorted and obtaining a corresponding intensity distribution pattern.

[0051] The analysis grating 204 is used to spatially encode the light intensity distribution pattern, assisting the detector 205 in resolving the light intensity distribution pattern. The specific form of the analysis grating 204 can be flexibly set according to actual usage requirements. For example, the analysis grating 204 can be set as an absorption grating, etc., and this disclosure does not specifically limit it. The specific form of the absorption material used in the absorption grating can be flexibly set according to actual usage requirements. For example, it can be set as a material with high absorption such as gold (Au) or tungsten (Wu), etc., and this disclosure does not specifically limit it. The specific form of the detector 205 can be referred to the implementation methods in related technologies, and this disclosure does not specifically limit it.

[0052] Furthermore, the source grating 202, phase grating 203, and analysis grating 204 can be fixed on a nanometer-precision displacement stage to control the three gratings for multi-degree-of-freedom adjustment. The specific form of the displacement stage can be flexibly set according to actual application requirements; for example, it can be a piezoelectric ceramic displacement stage, etc., and this disclosure does not impose specific limitations on it. The specific form of the multi-degree-of-freedom adjustment can be flexibly set according to actual application requirements, and this disclosure does not impose specific limitations on it.

[0053] Figure 3 A schematic diagram illustrating one degree of freedom for adjusting a grating according to an embodiment of the present disclosure is shown. Figure 3 As shown, a spatial rectangular coordinate system is constructed with the direction of X-ray incident on the grating as the Z-axis. The grating can move in the X-axis and Z-axis directions, and can also rotate around the θ-axis which passes through the geometric center of the grating and is perpendicular to the grating, and around the φ-axis which passes through the geometric center of the grating and is parallel to the X-axis direction, thereby realizing four degrees of freedom adjustment and improving the structural flexibility of the imaging device.

[0054] The specific method for scanning and imaging a target sample using an X-ray multi-characteristic grating imaging device to determine the target feature signal of the target sample can be flexibly set according to actual usage requirements, and this disclosure does not impose specific limitations on it.

[0055] In one example, the device 200 can be used to scan and image the target sample using a phase-stepping method, acquiring multiple original light intensity distribution images corresponding to the target sample; then, by performing phase-stepping curve analysis on the multiple original light intensity distribution images, the dark field signal projection image and the absorption signal projection image of the target sample can be extracted.

[0056] A grating imaging device based on geometric projection is a device that can image an object by using a grating with a specific configuration under a conventional X-ray source, utilizing the principles of geometric optics. Its specific form can be referred to the implementation methods in related technologies. For example, a grating imaging device based on geometric projection may include an X-ray source, a source grating, a modulation grating, an analysis grating, and a detector, and the source grating, modulation grating, and analysis grating are all absorption gratings, etc. This disclosure does not make specific limitations in this regard.

[0057] Furthermore, the directly extracted dark field signal projection image and absorption signal projection image may contain certain errors and noise signals. Therefore, data preprocessing can be performed on these images to obtain processed dark field signal projection images and absorption signal projection images, thereby improving the accuracy and reliability of the target feature signals. The specific data preprocessing method can be flexibly set according to actual usage requirements. For example, it may include signal smoothing processing, noise suppression processing, and beam hardening effect correction processing, etc., and this disclosure does not specifically limit it in this regard.

[0058] In step S102, the target sample is subjected to single-energy spectral structure decomposition according to the preset continuous mapping relationship and the target feature signal to determine the equivalent thickness distribution of the target sample relative to the preset reference material. The preset reference material is composed of a combination of a first reference microsphere and a second reference microsphere with different sizes. The preset continuous mapping relationship represents the relationship between the feature signal of the preset reference material and its material thickness.

[0059] The preset reference material can represent a material composed of a combination of first and second reference microspheres of different sizes. Its specific form can be flexibly set according to actual usage requirements, and this disclosure does not impose specific limitations on it. The specific materials of the first and second reference microspheres can be flexibly set according to actual usage requirements, and can usually be set to the same material. For example, both the first and second reference microspheres can be set to polymethyl methacrylate (PMMA) microspheres, etc., and this disclosure does not impose specific limitations on it. The specific values ​​of the sizes of the first and second reference microspheres can be flexibly set according to usage requirements. For example, the size of the first reference microsphere can be set to 200 micrometers (μm), and the size of the second reference microsphere can be set to 400 μm, etc., and this disclosure does not impose specific limitations on it.

[0060] In one example, the first and second reference microspheres can be filled into a stepped mold with multiple steps of different thicknesses, respectively; by combining the stepped molds, the thickness of the preset reference material can be flexibly adjusted.

[0061] Figure 4 A schematic diagram of a preset reference material according to an embodiment of the present disclosure is shown. Figure 4 As shown, the preset reference material 400 includes: a first stepped mold 401 filled with a first reference microsphere, and a second stepped mold 402 filled with a second reference microsphere. Both the first stepped mold 401 and the second stepped mold 402 have multiple steps of different thicknesses. By combining these steps, the maximum thickness of the preset reference material 400 can be adjusted.

[0062] The preset continuous mapping relationship can represent the relationship between the characteristic signal of the preset reference material and its material thickness, and can be determined by the dark field signal and absorption signal of the preset reference material with different material thicknesses. The specific content of the preset continuous mapping relationship can be flexibly set according to actual usage requirements. For example, it can include the mapping relationship between the dark field signal of the preset reference material and the thickness of the constituent parts of the first and second reference microspheres in the preset reference material, as well as the mapping relationship between the absorption signal of the preset reference material and the thickness of the constituent parts of the first and second reference microspheres in the preset reference material, etc. This disclosure does not specifically limit it in this regard.

[0063] The following sections will describe in detail the process of constructing the preset continuous mapping relationship in conjunction with the possible implementation methods of this disclosure, which will not be elaborated here.

[0064] Based on the preset continuous mapping relationship and target feature signal, the target sample can be decomposed into a single energy spectrum. Under the single X-ray energy spectrum condition, by utilizing the nonlinear relationship between different feature signals in the target feature signal, the equivalent thickness distribution of the target sample relative to the preset reference material can be determined, thereby directly avoiding the problem of difficulty in accurately modeling complex equivalent energy spectra.

[0065] Specifically, for any position of the target sample, an equivalent analysis can be performed on the dark field signal and absorption signal corresponding to that position in the target feature signal according to a preset continuous mapping relationship. The thickness of the preset reference material that can produce the same dark field signal and absorption signal, as well as the constituent thickness of the first reference microsphere and the second reference microsphere in the preset reference material of that thickness, are determined as the equivalent thickness corresponding to that position. Based on the equivalent thickness corresponding to each position in the target sample, the equivalent thickness distribution of the target sample relative to the preset reference material is determined.

[0066] The following sections will describe in detail the process of determining the equivalent thickness distribution of the target sample relative to the preset reference material by performing single-energy spectral structural decomposition on the target sample based on the preset continuous mapping relationship and the target feature signal, in conjunction with the possible implementation methods of this disclosure. These details will not be elaborated here.

[0067] In step S103, the quantitative analysis results of the microstructure of the target sample are determined according to the preset quantitative relationship model and the equivalent thickness distribution. The preset quantitative relationship model is used to indicate the size relationship between the target sample and the first reference microsphere and the second reference microsphere, and the quantitative analysis results of the microstructure are used to reflect the microstructure size characteristics of the target sample.

[0068] Typically, the size of any microsphere is much larger than its corresponding autocorrelation length. In this case, the linear diffusion coefficient of any microsphere can be expressed as formula (1):

[0069]

[0070] in, This represents the linear diffusion coefficient for any single microsphere. This indicates the size of the microsphere; Indicates autocorrelation length dependency; This represents the autocorrelation length, and its specific value is related to the equipment parameters of the X-ray multi-characteristic grating imaging device.

[0071] In one example, the X-ray multi-feature grating imaging apparatus includes a source grating, a modulation grating (e.g., as shown in the image), and a modulation grating. Figure 2 The imaging device based on the Talbot-Lau interferometer structure shown includes a phase grating 203 (or a modulation grating of a grating imaging device based on geometric projection, etc.) and an analysis grating. When any microsphere is placed between the modulation grating and the analysis grating, the autocorrelation length... This can be expressed as formula (2):

[0072]

[0073] in, Represents Planck's constant; Represents the speed of light; Indicates the energy of X-rays; The grating period represents the analytical grating; This represents the distance between any microsphere and the analytical grating.

[0074] In one example, the X-ray multi-feature grating imaging apparatus includes a source grating, a modulation grating (e.g., as shown in the image), and a modulation grating. Figure 2The imaging device based on the Talbot-Lau interferometer structure shown includes a phase grating 203, or a modulation grating of a grating imaging device based on geometric projection (e.g., a geometric projection method) and an analysis grating. When any microsphere is placed between the source grating and the modulation grating, the autocorrelation length can be expressed as formula (3):

[0075]

[0076] in, The grating period represents the source grating; This represents the distance between any microsphere and the source grating.

[0077] Based on this, the linear diffusion coefficient of the microsphere to be tested with unknown size can be expressed as a linear combination of the linear diffusion coefficients of the first reference microsphere with known size and the linear diffusion coefficients of the second reference microsphere, specifically expressed as formula (4):

[0078]

[0079] in, This represents the linear diffusion coefficient of the microsphere under test; This indicates the size of the microsphere being tested; This indicates the weight corresponding to the first benchmark microsphere; This represents the linear diffusion coefficient corresponding to the first reference microsphere; This indicates the size corresponding to the first reference microsphere; This indicates the weight corresponding to the second benchmark microsphere; This represents the linear diffusion coefficient corresponding to the second reference microsphere; This indicates the size corresponding to the first reference microsphere.

[0080] On the other hand, the autocorrelation dependence of microspheres of different sizes Since the linear diffusion coefficients of microspheres of different sizes are all the same, the linear diffusion coefficients of microspheres of different sizes are approximately linear, which is why it is impossible to use techniques such as dual-energy CT to infer the size of the microspheres to be tested. However, in the embodiments of this disclosure, this linear relationship of the linear diffusion coefficients of microspheres of different sizes made of the same material can be used to simplify formula (4), and the size of the microspheres to be tested can be expressed as formula (5):

[0081]

[0082] Based on the above principle, for target samples with unknown microstructure size characteristics, the measured thickness corresponding to any position of the target sample can be represented by a linear combination of a first reference microsphere and a second reference microsphere with known size. This allows for the construction of a preset quantitative relationship model to indicate the size relationship between the target sample and the first and second reference microspheres.

[0083] When the dimension of any location of the target sample is represented by a linear combination of the first and second reference microspheres, the weights of the first and second reference microspheres can be determined based on the equivalent thickness distribution of the target sample relative to a preset reference material. Then, combined with a preset quantitative relationship model, the measured thickness at that location can be determined. Based on the measured thickness at each location, the quantitative analysis result of the microstructure of the target sample can be determined. The specific content of the quantitative analysis result can be flexibly set according to actual usage requirements. For example, it may include a quantitative distribution image (tomographic reconstruction image) of the target sample's microstructure dimensions, key statistical characteristic parameters (e.g., average size, size distribution variance, etc.), and size measurement confidence intervals, etc. This disclosure does not specifically limit this.

[0084] This disclosure, through a deterministic quantitative relationship model between target feature signals, especially dark field signals, and microstructures, enables the microstructure analysis method based on X-ray multi-feature grating imaging to move beyond the relative comparison of dark field signal strength and output quantitative analysis results of microstructures with clear physical meaning. This improves the application prospects of the microstructure analysis method of this disclosure in high-precision analysis scenarios such as accurate disease staging.

[0085] The following sections will describe in detail the process of determining the quantitative analysis results of the microstructure of the target sample based on the preset quantitative relationship model and the equivalent thickness distribution, in conjunction with the possible implementation methods of this disclosure. These details will not be elaborated here.

[0086] In this embodiment, based on an X-ray multi-characteristic grating imaging device, the target sample is scanned and imaged. After determining the target feature signal of the target sample, a single-energy spectral structure decomposition of the target sample can be performed according to a preset continuous mapping relationship representing the relationship between the feature signal of the preset reference material and its material thickness, and the target feature signal. By jointly utilizing the nonlinear relationship between different signals in the target feature signal, the equivalent thickness distribution of the target sample relative to the preset reference material can be determined. This directly avoids the information redundancy and decomposition problems caused by the linear correlation characteristics of the linear diffusion coefficient of microspheres in commonly used dual-energy CT technology, thereby avoiding the problem of failure of microstructure analysis using dual-energy CT technology in specific scenarios in the prior art. According to the preset quantitative relationship model used to indicate the size relationship between the target sample and the first and second reference microspheres and the equivalent thickness distribution, the quantitative analysis result of the microstructure corresponding to the target sample with clear physical meaning can be determined to reflect the microstructure size characteristics of the target sample, rather than being limited to qualitative or semi-quantitative analysis of the microstructure size based on the strength of the target feature signal. This increases the adaptability of microstructure analysis to high-precision analysis scenarios such as accurate disease staging. On the other hand, by utilizing the dark-field signal, which is sensitive to sub-pixel-level structure in the target feature signal, as the basis for inversion micro-size analysis, the micro-structure analysis method disclosed herein does not require increasing the X-ray radiation dose to improve the spatial resolution of the X-ray multi-characteristic grating imaging device. It can achieve quantitative characterization of micro-structures far below pixel size under common X-ray radiation dose and imaging device spatial resolution scenarios, improving the versatility of micro-structure analysis. It can be applied to various fields that require quantitative identification of micro-structures, such as quantitative assessment of alveolar structure in the lungs, bone quality assessment, stone composition analysis, and micro-characterization of heterogeneous materials in materials science, and has high technology transfer capability. At the same time, in the field of disease diagnosis, it can better balance the contradiction between acquiring diagnostic information and patient radiation safety.

[0087] In one possible implementation, the method further includes: using an X-ray multi-feature grating imaging device to scan and image preset reference materials of various preset material thicknesses to determine a discrete sample dataset, wherein the discrete sample dataset includes sample feature signals corresponding to the preset reference materials of each preset material thickness; and using a weighted polynomial fitting method to determine a preset continuous mapping relationship based on the discrete sample dataset.

[0088] Specifically, before performing microstructure analysis on the target sample, an X-ray multi-characteristic grating imaging device can be used to scan and image preset reference materials of various preset material thicknesses, respectively, and determine the sample feature signals corresponding to each preset material thickness of the preset reference material, thereby constructing a discrete sample dataset. The specific content of the sample feature signals can be flexibly set according to actual usage requirements, and is usually the same as the target feature signals corresponding to the target sample. For example, it may include the sample dark field signal and sample absorption signal of the preset reference material, etc., and this disclosure does not specifically limit it in this regard.

[0089] The specific method for scanning and imaging a preset reference material with multiple preset material thicknesses can be flexibly set according to actual usage requirements and is related to the X-ray multi-characteristic grating imaging device and the actual form of the preset reference material. This disclosure does not impose specific limitations on this.

[0090] Figure 5 This diagram illustrates a scanning imaging of a preset reference material according to an embodiment of the present disclosure. Figure 5 As shown, an imaging device 200 based on a Talbot-Lau interferometer structure is used to scan and image a preset reference material 400 using a phase-stepping method. The preset reference material 400 includes a first stepped phantom 401 filled with first reference microspheres and a second stepped phantom 402 filled with second reference microspheres. By adjusting the relative positional relationship between the first stepped phantom 401 and the second stepped phantom 402, the material thickness of the preset reference material 400 can be adjusted quickly and flexibly, thereby avoiding repeated replacement of the preset reference material, reducing manual operation during the scanning and imaging process, and efficiently determining the sample feature signal corresponding to each preset material thickness. The specific method for adjusting the relative positional relationship between the first stepped phantom 401 and the second stepped phantom 402 can be flexibly set according to actual usage requirements. For example, the first stepped phantom 401 and the second stepped phantom 402 can be placed on two electric displacement stages respectively to perform multi-degree-of-freedom relative position adjustment of the first stepped phantom 401 and the second stepped phantom 402. This disclosure does not specifically limit this method.

[0091] It is important to note that when scanning and imaging a preset reference material using an X-ray multi-characteristic grating imaging device, and when scanning and imaging a target sample using an X-ray multi-characteristic grating imaging device, the corresponding operating parameters of the X-ray multi-characteristic grating imaging device, such as the voltage and current of the X-ray tube, exposure time, and phase step sequence, should remain consistent.

[0092] The specific method for constructing discrete sample datasets can be flexibly set according to actual usage requirements, and this disclosure does not impose specific limitations on it.

[0093] In one possible implementation, an X-ray multi-characteristic grating imaging device is used to scan and image preset reference materials of various preset material thicknesses to determine a discrete sample dataset. This includes: for any preset reference material of a preset material thickness, scanning and imaging the preset reference material of that preset material thickness using the X-ray multi-characteristic grating imaging device to determine the initial feature signal corresponding to the preset reference material of that preset material thickness; scanning and imaging a reference material of that preset material thickness using the X-ray multi-characteristic grating imaging device to determine the reference feature signal corresponding to the reference material of that preset material thickness; and correcting the feature signal corresponding to the preset reference material of that preset material thickness based on the reference feature signal to determine the sample feature signal corresponding to the preset reference material of that preset material thickness.

[0094] For any preset reference material with a preset material thickness, the preset reference material with the preset material thickness can first be scanned and imaged using an X-ray multi-characteristic grating imaging device to determine the initial feature signal corresponding to the preset reference material with the preset material thickness. Then, while maintaining consistent operating parameters, a reference material with the preset material thickness can be scanned and imaged using the same X-ray multi-characteristic grating imaging device to determine the reference feature signal corresponding to the reference material with the preset material thickness, which serves as the basis for correcting the initial feature signal corresponding to the preset reference material with the preset material thickness. The specific form of the reference material can be flexibly set according to actual usage requirements. It can typically be set as a solid object made of the same material as the first and second reference microspheres; this disclosure does not specifically limit its form.

[0095] Based on the reference feature signal corresponding to any preset material thickness, the initial feature signal corresponding to the preset reference material of the preset material thickness can be corrected to determine the sample feature signal corresponding to the preset reference material of the preset material thickness; then, based on the sample feature signal corresponding to the preset reference material of each preset material thickness, a discrete sample dataset is constructed.

[0096] Figure 6 This diagram illustrates a polynomial-fit contour plot of a dark field signal according to an embodiment of the present disclosure. The preset reference material comprises a first reference microsphere with a size of 200 μm and a second reference microsphere with a size of 400 μm. (Example:) Figure 6 As shown, the vertical axis represents the first thickness of the component based on a first reference microsphere with a size of 200 μm in the preset reference material, and the horizontal axis represents the second thickness of the component based on a second reference microsphere with a size of 400 μm in the preset reference material; the color coordinates represent the dark field signal intensity, with a color closer to yellow (warmer color temperature) indicating a stronger dark field signal intensity, and a color closer to purple (cooler color temperature) indicating a weaker dark field signal intensity. The white rings represent sample data in the discrete sample dataset.

[0097] Figure 7 A polynomial-fit contour plot of an absorption signal according to an embodiment of the present disclosure is shown. The preset reference material consists of a first reference microsphere with a size of 200 μm and a second reference microsphere with a size of 400 μm. (As shown...) Figure 7 As shown, the vertical axis represents the first thickness of the component based on a first reference microsphere with a size of 200 μm in the preset reference material, and the horizontal axis represents the second thickness of the component based on a second reference microsphere with a size of 400 μm in the preset reference material; the color coordinates represent the absorption signal intensity, with a color closer to yellow (warmer color temperature) indicating a greater absorption signal intensity, and a color closer to purple (cooler color temperature) indicating a smaller absorption signal intensity. The white rings represent sample data in the discrete sample dataset.

[0098] according to Figure 6 and Figure 7 It is known that the thickness of the two reference microspheres in the preset reference material has a nonlinear effect on the dark field signal intensity and the absorbed signal intensity of the preset reference material; therefore, the weighted polynomial fitting method can be used to determine the preset continuous mapping relationship based on the discrete sample dataset.

[0099] Specifically, when scanning and imaging a preset reference material based on single-energy X-ray spectroscopy, the absorption signal and dark field signal corresponding to the preset reference material can be expressed as formula (6) and formula (7), respectively:

[0100]

[0101] in, Indicates signal absorption; Indicates a dark field signal; This represents the average intensity of the phase step curve of the scanned background; This indicates the average intensity of the phase step curve of the preset reference material; The contrast of the phase step curve representing the scanned background; This indicates the contrast of the phase step curve of the preset reference material; Indicates the linear attenuation coefficient of the preset reference material; Indicates the linear diffusion coefficient of the preset reference material; This indicates the thickness of the preset reference material.

[0102] Generally, the effect of the thickness of the preset reference material on the autocorrelation length can be considered negligible; that is, the autocorrelation length can be considered to be only related to the energy of the X-rays. Based on the average energy spectrum and contrast energy spectrum of the preset background phase step curve, combined with formulas (6) and (7), the absorption signal A and the hardened dark field signal DF can be expressed as formulas (8) and (9), respectively:

[0103]

[0104] in, This represents the average energy spectrum of the preset background phase step curve; This represents the contrast spectrum of the preset background phase step curve.

[0105] Based on formulas (8) and (9), a weighted polynomial fitting method can be used to perform forward modeling based on discrete sample datasets, thereby avoiding the problem of difficulty in accurately modeling complex equivalent energy spectra. This reduces the hardware stability requirements of X-ray multi-characteristic grating imaging devices and further enhances the versatility of the microstructure analysis method disclosed herein. This process can be expressed as formula (10):

[0106]

[0107] in, This represents the vector of polynomial coefficients to be solved; This refers to the weighting coefficient determined based on the noise level. Its specific value can be flexibly set according to actual usage requirements, and this disclosure does not impose any specific limitations on it. This represents the amount of data in a discrete sample dataset; This represents the sample feature signal corresponding to a preset reference material with any preset material thickness in a discrete sample dataset; The thickness of the component portion of the first reference microsphere in the preset reference material, which represents the thickness of the preset material; The thickness of the component portion of the second reference microsphere in the preset reference material, which represents the thickness of the preset material; This represents the polynomial model to be calibrated, used to reflect the polynomial model based on... and The predicted feature signal obtained from the prediction.

[0108] By optimizing and solving formula (10), the objective polynomial coefficient vector that minimizes the difference between the sample feature signal and the predicted feature signal can be determined. This allows the construction of a preset continuous mapping relationship between the material thickness of the preset reference material and the dark field signal and absorption signal. The specific method for optimizing and solving formula (10) can be flexibly set according to actual usage requirements, and this disclosure does not impose any specific limitations on it.

[0109] In one possible implementation, the target sample is subjected to single-energy spectral structure decomposition based on a preset continuous mapping relationship and target feature signal to determine the equivalent thickness distribution of the target sample relative to a preset reference material. This includes: constructing an equivalent thickness optimization model corresponding to the target sample based on the preset continuous mapping relationship and target feature signal; optimizing and solving the equivalent thickness optimization model to determine the equivalent thickness distribution.

[0110] Specifically, for any location of the target sample, the dark field signal and absorption signal corresponding to that location in the target feature signal can be regarded as a set of two-dimensional targets. With the thickness of the constituent parts of the first reference microsphere and the thickness of the constituent parts of the second reference microsphere in the preset reference material as constraints, the predicted thickness of the constituent parts of the first reference microsphere and the predicted thickness of the constituent parts of the second reference microsphere can be solved to minimize the difference between the predicted dark field signal and the predicted absorption signal determined according to the preset continuous mapping relationship and the dark field signal and absorption signal corresponding to that location. This process can be expressed as formula (11):

[0111]

[0112] in, This represents the dark field signal corresponding to any position in the target sample. This represents the absorption signal corresponding to any position in the target sample. This represents the mapping relationship between the dark field signal and the thickness of the constituent parts of the first reference microsphere and the thickness of the constituent parts of the second reference microsphere in a preset continuous mapping relationship. This represents the mapping relationship between the absorbed signal and the thickness of the constituent parts of the first reference microsphere and the thickness of the constituent parts of the second reference microsphere in a preset continuous mapping relationship.

[0113] By optimizing and solving the equivalent thickness optimization model, the equivalent thickness distribution of the target sample can be determined, indicating the equivalent thickness corresponding to any position in the target sample, that is, the thickness of the preset reference material at that position, and the constituent thickness of the first and second reference microspheres in the preset reference material of that thickness. The specific method for optimizing and solving the equivalent thickness optimization model can be flexibly set according to actual usage requirements, and this disclosure does not impose specific limitations on it.

[0114] In one possible implementation, the quantitative analysis results of the microstructure of the target sample are determined according to a preset quantitative relationship model and an equivalent thickness distribution, including: determining the weight distribution of the first reference microsphere and the weight distribution of the second reference microsphere according to the equivalent thickness distribution; and determining the quantitative analysis results of the microstructure according to the preset quantitative relationship model, the size and weight distribution of the first reference microsphere, and the size and weight distribution of the second reference microsphere.

[0115] For any location of the target sample, tomographic reconstruction can be performed based on the equivalent thickness distribution to determine the weight of the first reference microsphere and the weight of the second reference microsphere at that location. Based on the weight of the first reference microsphere and the weight of the second reference microsphere at each location, the weight distribution of the first reference microsphere and the weight distribution of the second reference microsphere can be determined.

[0116] Furthermore, by substituting the size and weight distribution of the first reference microsphere and the size and weight distribution of the second reference microsphere into the preset quantitative relationship model, the measurement size at each location in the target sample can be determined, thereby determining the quantitative analysis results of the microstructure of the target sample.

[0117] Through the above process, the dark field signal, which is sensitive to sub-pixel-level structure in the target feature signal, can be used as the basis for inversion micro-size analysis. This means that the micro-structure analysis method of this disclosure does not require increasing the X-ray radiation dose to improve the spatial resolution of the X-ray multi-characteristic grating imaging device. It can achieve quantitative characterization of micro-structures far smaller than pixel size under common X-ray radiation dose and imaging device spatial resolution scenarios, improving the versatility of micro-structure analysis. It can be applied to various fields that require quantitative identification of micro-structures, such as quantitative assessment of alveolar structure in the lungs, bone quality assessment, stone composition analysis, and micro-characterization of heterogeneous materials in materials science. It has high technology transfer capability. At the same time, in the field of disease diagnosis, it can better balance the contradiction between acquiring diagnostic information and patient radiation safety.

[0118] Figure 8 A schematic diagram illustrating a microstructure analysis process according to an embodiment of the present disclosure is shown. Figure 8As shown, the microstructure analysis process includes a system calibration process and a structure decomposition process. The system calibration process includes: identifying a first and second reference microsphere with known and different sizes, and constructing a preset reference material; setting the imaging parameters of the X-ray multi-characteristic grating imaging device according to usage requirements, and scanning the preset reference materials with various preset material thicknesses to determine the initial characteristic signal of each preset material thickness; using the X-ray multi-characteristic grating imaging device to scan the reference materials with various preset material thicknesses to determine the reference characteristic signal corresponding to each preset material thickness; correcting the initial characteristic signal of the preset reference material with each preset material thickness based on the reference characteristic signal corresponding to each preset material thickness, and constructing a discrete sample dataset; and using a weighted polynomial fitting method, determining a preset continuous mapping relationship based on the discrete sample dataset.

[0119] The structural decomposition process includes: scanning and imaging the target sample using an X-ray multi-characteristic grating imaging device to determine the target feature signal; correcting the target feature signal based on the reference feature signal corresponding to the reference material with each preset material thickness to determine the corrected target feature signal; performing single-energy spectral structural decomposition on the target sample according to the preset continuous mapping relationship and the target feature signal to determine the equivalent thickness distribution of the target sample relative to the preset reference material; performing tomographic reconstruction based on the equivalent thickness distribution to determine the weight distribution of the first reference microsphere and the weight distribution of the second reference microsphere; and determining the quantitative analysis results of the microstructure based on the preset quantitative relationship model, the size and weight distribution of the first reference microsphere, and the size and weight distribution of the second reference microsphere.

[0120] Figure 9 A schematic diagram illustrating the results of a quantitative analysis of microstructure according to an embodiment of the present disclosure is shown. Figure 9 As shown in (a), the target sample is a microsphere; the size of the first reference microsphere is 400 μm, and the size of the second reference microsphere is 200 μm. Figure 9As shown in (b), this image is a probability density distribution diagram of microsphere size prediction. The horizontal axis represents the microsphere size (μm), and the vertical axis represents the probability density. The blue curve represents the probability density distribution curve of microsphere size prediction when the target sample is a microsphere with a size of 200 μm. According to this curve, the microsphere size prediction result is 197 ± 13.2 μm, indicating high accuracy in the quantitative analysis of the microstructure of the target sample. The green curve represents the probability density distribution curve of microsphere size prediction when the target sample is a microsphere with a size of 300 μm. According to this curve, the microsphere size prediction result is 304.5 ± 14.5 μm, indicating high accuracy in the quantitative analysis of the microstructure of the target sample. The yellow curve represents the probability density distribution curve of microsphere size prediction when the target sample is a microsphere with a size of 400 μm. According to this curve, the microsphere size prediction result is 398.6 ± 16.4 μm, indicating high accuracy in the quantitative analysis of the microstructure of the target sample.

[0121] In this embodiment, based on an X-ray multi-characteristic grating imaging device, the target sample is scanned and imaged. After determining the target feature signal of the target sample, a single-energy spectral structure decomposition of the target sample can be performed according to a preset continuous mapping relationship representing the relationship between the feature signal of the preset reference material and its material thickness, and the target feature signal. By jointly utilizing the nonlinear relationship between different signals in the target feature signal, the equivalent thickness distribution of the target sample relative to the preset reference material can be determined. This directly avoids the information redundancy and decomposition problems caused by the linear correlation characteristics of the linear diffusion coefficient of microspheres in commonly used dual-energy CT technology, thereby avoiding the problem of failure of microstructure analysis using dual-energy CT technology in specific scenarios in the prior art. According to the preset quantitative relationship model used to indicate the size relationship between the target sample and the first and second reference microspheres and the equivalent thickness distribution, the quantitative analysis result of the microstructure corresponding to the target sample with clear physical meaning can be determined to reflect the microstructure size characteristics of the target sample, rather than being limited to qualitative or semi-quantitative analysis of the microstructure size based on the strength of the target feature signal. This increases the adaptability of microstructure analysis to high-precision analysis scenarios such as accurate disease staging. On the other hand, by utilizing the dark-field signal, which is sensitive to sub-pixel-level structure in the target feature signal, as the basis for inversion micro-size analysis, the micro-structure analysis method disclosed herein does not require increasing the X-ray radiation dose to improve the spatial resolution of the X-ray multi-characteristic grating imaging device. It can achieve quantitative characterization of micro-structures far below pixel size under common X-ray radiation dose and imaging device spatial resolution scenarios, improving the versatility of micro-structure analysis. It can be applied to various fields that require quantitative identification of micro-structures, such as quantitative assessment of alveolar structure in the lungs, bone quality assessment, stone composition analysis, and micro-characterization of heterogeneous materials in materials science, and has high technology transfer capability. At the same time, in the field of disease diagnosis, it can better balance the contradiction between acquiring diagnostic information and patient radiation safety.

[0122] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further. Those skilled in the art will understand that in the above methods of specific implementation, the specific execution order of each step should be determined by its function and possible internal logic.

[0123] In addition, this disclosure also provides a microstructure analysis system that can be used to implement any of the microstructure analysis methods provided in this disclosure. The corresponding technical solutions and descriptions can be found in the relevant records in the method section, and will not be repeated here.

[0124] Figure 10 A block diagram of a microstructure analysis system according to an embodiment of the present disclosure is shown. Figure 10 As shown, the device 1000 includes:

[0125] Imaging module 1001 is used to scan and image a target sample based on an X-ray multi-feature grating imaging device to determine the target feature signal of the target sample.

[0126] The single-energy spectrum structure decomposition module 1002 is used to perform single-energy spectrum structure decomposition on the target sample according to the preset continuous mapping relationship and the target feature signal, and determine the equivalent thickness distribution of the target sample relative to the preset reference material. The preset reference material is composed of a combination of a first reference microsphere and a second reference microsphere with different sizes. The preset continuous mapping relationship represents the relationship between the feature signal of the preset reference material and its material thickness.

[0127] The structural quantitative analysis module 1003 is used to determine the microstructure quantitative analysis results of the target sample based on the preset quantitative relationship model and the equivalent thickness distribution. The preset quantitative relationship model is used to indicate the size relationship between the target sample and the first reference microsphere and the second reference microsphere, and the microstructure quantitative analysis results are used to reflect the microstructure size characteristics of the target sample.

[0128] In one possible implementation, the X-ray multi-feature grating imaging device is an imaging device based on the Talbot-Lau interferometer structure, or a grating imaging device based on the geometric projection method.

[0129] In one possible implementation, the single-energy spectral structure decomposition module 1002 is used to: construct an equivalent thickness optimization model corresponding to the target sample based on a preset continuous mapping relationship and target feature signal; optimize and solve the equivalent thickness optimization model to determine the equivalent thickness distribution.

[0130] In one possible implementation, the structural quantitative analysis module 1003 is used to: determine the weight distribution of the first reference microsphere and the weight distribution of the second reference microsphere based on the equivalent thickness distribution; and determine the microstructure quantitative analysis results based on the preset quantitative relationship model, the size and weight distribution of the first reference microsphere, and the size and weight distribution of the second reference microsphere.

[0131] In one possible implementation, system 1000 further includes a system calibration module for: scanning and imaging preset reference materials of various preset material thicknesses based on an X-ray multi-characteristic grating imaging device to determine a discrete sample dataset, wherein the discrete sample dataset includes sample feature signals corresponding to the preset reference materials of each preset material thickness; and determining a preset continuous mapping relationship based on the discrete sample dataset using a weighted polynomial fitting method.

[0132] In one possible implementation, the system calibration module is further configured to: scan and image a preset reference material of any preset material thickness using an X-ray multi-characteristic grating imaging device to determine an initial feature signal corresponding to the preset reference material of the preset material thickness; scan and image a reference material of the preset material thickness using an X-ray multi-characteristic grating imaging device to determine a reference feature signal corresponding to the reference material of the preset material thickness; and correct the feature signal corresponding to the preset reference material of the preset material thickness based on the reference feature signal to determine a sample feature signal corresponding to the preset reference material of the preset material thickness.

[0133] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0134] This disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0135] This disclosure also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0136] This disclosure also provides a computer program product, including a computer program or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program, when executed by a processor, implements the steps of the above method.

[0137] Figure 11 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. For example, electronic device 1900 may be provided as a server or terminal device. (Refer to...) Figure 11 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0138] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). Electronic device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM Mac OS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0139] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0140] Computer-readable storage media can be tangible devices capable of holding and storing programs / instructions used by instruction execution devices. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0141] The computer program (or computer-readable program instructions) described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage medium in the respective computing / processing device.

[0142] The computer program (or computer program instructions) used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions to implement various aspects of this disclosure.

[0143] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0144] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0145] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0147] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for microstructure analysis, characterized in that, include: Based on an X-ray multi-feature grating imaging device, a target sample is scanned and imaged to determine the target feature signal of the target sample; Based on the preset continuous mapping relationship and the target feature signal, the target sample is subjected to single-energy spectral structure decomposition to determine the equivalent thickness distribution of the target sample relative to the preset reference material. The preset reference material is composed of a combination of a first reference microsphere and a second reference microsphere with different sizes. The preset continuous mapping relationship represents the relationship between the feature signal of the preset reference material and its material thickness. Based on the preset quantitative relationship model and the equivalent thickness distribution, the quantitative analysis results of the microstructure corresponding to the target sample are determined. The preset quantitative relationship model is used to indicate the size relationship between the target sample and the first reference microsphere and the second reference microsphere, and the quantitative analysis results of the microstructure are used to reflect the microstructure size characteristics of the target sample.

2. The method according to claim 1, characterized in that, The X-ray multi-characteristic grating imaging device is an imaging device based on the Talbot-Lau interferometer structure, or a grating imaging device based on the geometric projection method.

3. The method according to claim 1 or 2, characterized in that, The step of performing single-energy spectral structure decomposition on the target sample based on a preset continuous mapping relationship and the target feature signal to determine the equivalent thickness distribution of the target sample relative to a preset reference material includes: Based on the preset continuous mapping relationship and the target feature signal, construct the equivalent thickness optimization model corresponding to the target sample; The equivalent thickness optimization model is optimized and solved to determine the equivalent thickness distribution.

4. The method according to claim 1 or 2, characterized in that, The step of determining the quantitative analysis results of the microstructure of the target sample based on the preset quantitative relationship model and the equivalent thickness distribution includes: Based on the equivalent thickness distribution, the weight distribution of the first reference microsphere and the weight distribution of the second reference microsphere are determined. The quantitative analysis results of the microstructure are determined based on the preset quantitative relationship model, the size and weight distribution of the first reference microsphere, and the size and weight distribution of the second reference microsphere.

5. The method according to claim 1 or 2, characterized in that, The method further includes: Based on the X-ray multi-characteristic grating imaging device, the preset reference materials with various preset material thicknesses are scanned and imaged to determine a discrete sample dataset, wherein the discrete sample dataset includes sample feature signals corresponding to the preset reference materials with each preset material thickness; The preset continuous mapping relationship is determined based on the discrete sample dataset using a weighted polynomial fitting method.

6. The method according to claim 5, characterized in that, The X-ray multi-feature grating imaging device scans and images the preset reference material with various preset material thicknesses to determine a discrete sample dataset, including: For any preset reference material with a preset material thickness, the preset reference material with the preset material thickness is scanned and imaged using the X-ray multi-feature grating imaging device to determine the initial feature signal corresponding to the preset reference material with the preset material thickness. Based on the X-ray multi-characteristic grating imaging device, a reference material with a preset material thickness is scanned and imaged to determine the reference feature signal corresponding to the reference material with the preset material thickness. Based on the reference feature signal, the feature signal corresponding to the preset reference material with the preset material thickness is corrected to determine the sample feature signal corresponding to the preset reference material with the preset material thickness.

7. A microstructure analysis system, characterized in that, include: An imaging module is used to scan and image a target sample based on an X-ray multi-feature grating imaging device to determine the target feature signal of the target sample. The single-energy spectral structure decomposition module is used to perform single-energy spectral structure decomposition on the target sample according to a preset continuous mapping relationship and the target feature signal, and determine the equivalent thickness distribution of the target sample relative to a preset reference material. The preset reference material is composed of a first reference microsphere and a second reference microsphere with different sizes. The preset continuous mapping relationship represents the relationship between the feature signal of the preset reference material and its material thickness. The structural quantitative analysis module is used to determine the microstructure quantitative analysis results corresponding to the target sample based on a preset quantitative relationship model and the equivalent thickness distribution. The preset quantitative relationship model is used to indicate the size relationship between the target sample and the first reference microsphere and the second reference microsphere, and the microstructure quantitative analysis results are used to reflect the microstructure size characteristics of the target sample.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, or a non-volatile computer-readable storage medium carrying a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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