CN117679025B - Detection method, system and medium for detecting blood oxygen information of neuromuscular - Google Patents
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Abstract
The present application provides detection methods, systems, and media for detecting blood oxygenation information of neuromuscular. The detection method comprises the steps that a processor creates a first grid model based on a physiological structure of a concerned muscle area, and obtains a linear relation between a reference light intensity value variation and a reference optical parameter variation of the first grid model; a first predicted optical parameter is given to each grid, the predicted optical parameter is determined based on a first iterative operation, the grid concerned is determined according to the size of the predicted optical parameter, and after the grid concerned is refined, a second iterative operation is carried out to determine the target optical parameter of each refined grid; obtaining blood oxygen blood flow information of the near infrared image and blood oxygen information of the tomographic image based on the target optical parameters and the predicted optical parameters; and performing image fusion on the reconstructed near infrared image and the tomographic image to detect blood oxygen information of the concerned muscle region. Thus, the defects of low neuromuscular blood oxygen detection resolution, incomplete information and the like are overcome.
Description
Technical Field
The application relates to the technical field of detection, in particular to a detection method, a detection system and a detection medium for detecting blood oxygen information of neuromuscular.
Background
Near infrared spectrum detection technology adopts near infrared light to detect the content of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in muscle tissue, and because the oxyhemoglobin (HbO) and the deoxyhemoglobin (HbR) have different absorption rates at different wavelengths, the content of HbO and HbR in muscle tissue can be reversely pushed through the absorption rate, so that a change image of blood oxygen level is obtained. However, due to the complexity and diversity of biological tissues, there is a limitation in that the spatial resolution is low in the detection of blood oxygen information of neuromuscular. Because of the low spatial resolution, the blood oxygen information of the neuromuscular cannot be comprehensively presented, and a doctor cannot accurately acquire the blood oxygen information condition about the neuromuscular.
Disclosure of Invention
The present application has been made in view of the above-mentioned technical problems occurring in the prior art. The present application is directed to a detection method, system, and medium for detecting blood oxygen information of a neuromuscular, which can improve the resolution of detecting blood oxygen information of a neuromuscular and comprehensively acquire blood oxygen information of a neuromuscular.
According to a first aspect of the present application, there is provided a detection method for detecting blood oxygen information of a neuromuscular, the detection method comprising: acquiring the detected light intensity variation of the concerned muscle region of the detected person before and after the target task is executed based on the near infrared data acquisition equipment and the tomography equipment respectively; a processor creates a first mesh model based on a physiological structure of the muscle region of interest to reconstruct a near infrared image and a tomographic image, respectively, comprising: obtaining a linear relation between a reference light intensity value variation and a reference optical parameter variation of a first grid model and initial optical parameters of each grid in the first grid model; assigning a first predicted optical parameter to each grid, and performing a first iterative operation on each grid according to the linear relation based on the first predicted optical parameter and the initial optical parameter to determine the predicted optical parameter of each grid based on the result of the first iterative operation; determining the concerned grids according to the expected optical parameters of each grid, refining the concerned grids, and performing a second iterative operation to determine the target optical parameters of each refined grid based on the result of the second iterative operation; obtaining blood oxygen flow information of the near infrared image and blood oxygen information of the tomographic image based on the target optical parameters of each thinned grid and the expected optical parameters of the original grid which is not thinned; and performing image fusion on the reconstructed near infrared image and the tomographic image to detect blood oxygen information of the concerned muscle region.
According to a second aspect of the present application, there is provided a system for detecting blood oxygenation information of neuromuscular, the system comprising an interface configured to: acquiring the detected light intensity variation of the concerned muscle region of the detected person before and after the target task is executed based on the near infrared data acquisition equipment and the tomography equipment respectively; the processor is configured to: creating a first mesh model based on the physiological structure of the muscle region of interest to reconstruct a near infrared image and a tomographic image, respectively, comprising: obtaining a linear relation between a reference light intensity value variation and a reference optical parameter variation of a first grid model and initial optical parameters of each grid in the first grid model; assigning a first predicted optical parameter to each grid, and performing a first iterative operation on each grid according to the linear relation based on the first predicted optical parameter and the initial optical parameter to determine the predicted optical parameter of each grid based on the result of the first iterative operation; determining the concerned grids according to the expected optical parameters of each grid, refining the concerned grids, and performing a second iterative operation to determine the target optical parameters of each refined grid based on the result of the second iterative operation; obtaining blood oxygen flow information of the near infrared image and blood oxygen information of the tomographic image based on the target optical parameters of each thinned grid and the expected optical parameters of the original grid which is not thinned; and performing image fusion on the reconstructed near infrared image and the tomographic image to detect blood oxygen information of the concerned muscle region.
According to a third aspect of the present application, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the detection method for detecting blood oxygen information of neuromuscular according to the respective embodiments of the present application.
Compared with the prior art, the embodiment of the application has the beneficial effects that:
according to the detection method for detecting the blood oxygen information of the nerve muscles, which is provided by the embodiment of the application, in the process that a subject executes a target task, the blood oxygen information of the nerve muscles changes, and the influence on the light absorptivity is generated. Based on the near infrared data acquisition equipment and the tomography equipment, the detection light intensity variation of the concerned muscle area before and after the target task is executed by the testee can be respectively acquired, and the tissue information of different depths of the concerned muscle area can be acquired. Then, based on the physiological structure of the concerned muscle area, a first grid model is created, and based on the first grid model, a first iterative operation is carried out to determine concerned grids needing to focus on image reconstruction, and a second iterative operation is carried out on the concerned grids to obtain target optical parameters.
During the target task performed by the subject, the optical parameters of the muscle region of interest may change along with the target task, for example, the optical parameters of the region of interest near the nerve may change greatly and the optical parameters of the region of interest far from the nerve may change relatively little. By determining the focus grid, focusing the reconstruction process on the focus grid with larger optical parameter variation, and performing second iterative operation on the focus grid, the accuracy of the reconstructed target optical parameter can be improved. Therefore, the near infrared image and the tomographic image are respectively reconstructed, and then the near infrared image and the tomographic image are subjected to image fusion, so that a fused image with comprehensive blood oxygen information is obtained. The fusion image can reflect blood oxygen information of nerve muscles from macroscopic and mesoscopic angles, can provide more detail information of the blood oxygen information, and is beneficial to a doctor to analyze the physiological condition of nerve muscles of a subject according to the blood oxygen information of the nerve muscles.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
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In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like reference numerals with letter suffixes or different letter suffixes may represent different examples of similar components. The drawings illustrate generally, by way of example, and not by way of limitation, various embodiments, and together with the description and claims serve to explain the disclosed embodiments. Such embodiments are illustrative and exemplary, and are not intended to be exhaustive or exclusive embodiments of the present methods, systems, or non-transitory computer readable media having instructions for implementing the methods.
Fig. 1 shows a flowchart of a detection method for detecting blood oxygen information of a neuromuscular according to an embodiment of the present application.
Fig. 2 shows a further flowchart of a detection method for detecting blood oxygen information of neuromuscular according to an embodiment of the present application.
Fig. 3 is a schematic diagram showing the structure of a system for detecting blood oxygen information of a neuromuscular according to an embodiment of the present application.
Detailed Description
The present application will be described in detail below with reference to the drawings and detailed description to enable those skilled in the art to better understand the technical scheme of the present application. Embodiments of the present application will be described in further detail below with reference to the drawings and specific examples, but not by way of limitation.
The terms "first," "second," and the like, as used herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. As used herein, the word "comprising" or "comprises" and the like means that elements preceding the word encompass the elements recited after the word, and that no other elements are excluded from the possible coverage as well. In the present application, the arrows shown in the figures of the respective steps are merely examples of the execution sequence, and the technical solution of the present application is not limited to the execution sequence described in the embodiments, and the respective steps in the execution sequence may be performed in combination, may be performed in decomposition, and may be exchanged as long as the logical relationship of the execution contents is not affected.
All terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs, unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
Fig. 1 shows a flowchart of a detection method for detecting blood oxygen information of a neuromuscular according to an embodiment of the present application. In step S101, the detected light intensity variation of the muscle region of interest of the subject before and after the target task is performed is acquired based on the near infrared data acquisition device and the tomographic imaging device, respectively. Specifically, taking blood oxygen information of nerve muscles of an arm as an example, a probe of a miniature near infrared data acquisition device and a probe of a tomography device can be integrated on the same muscle binding belt, and the binding belt adopts soft, comfortable and breathable materials, including but not limited to elastic fabrics and foam materials, so as to ensure the comfort and the safety of the arm of a subject.
The target task may be a grasping task, a swinging arm, a clapping palm, a light handling task, or the like, which is merely taken as an example, and other tasks are not excluded. In the course of performing the target task, tissue information of the neuromuscular may change, for example, blood oxygen information rises or falls with the progress of the target task, or optical parameters such as the absorption coefficient of light of the neuromuscular may change accordingly. For example, performing a target task results in blood oxygen flowing more rapidly in neuromuscular tissue, or increasing the number of muscle fibers, strengthening the connections between muscle fibers, etc. causes structural changes in the muscle tissue, which may increase the absorption coefficient of light.
The light intensity value of the emergent light detected by the probes in the near infrared data acquisition device and the tomography device respectively in the process of executing the target task is changed greatly compared with the light intensity value of the light detected before executing the target task, wherein the detected light intensity change quantity can be the deviation of a representative value (such as a maximum value or an average value) of the detected light intensity value and a representative value (such as a maximum value or an average value) of the detected light intensity value or a preset light intensity value in the process of executing the target task by the subject.
The tissue information of the deeper physiological structure layer can be acquired based on the near infrared data acquisition equipment, and the tissue information of the shallow physiological structure layer can be acquired based on the tomography equipment, wherein the tissue information at least comprises optical parameters and blood oxygen information.
In step S102, the processor creates a first mesh model based on the physiological structure of the muscle region of interest to reconstruct a near infrared image and a tomographic image, respectively. The physiological structure of the muscle region of interest may include an epidermis layer, a dermis layer, a subcutaneous fat layer, and a muscle tissue layer in this order from the epidermis to the inside. The processor may perform mesh division according to each physiological structure layer during the process of creating the first mesh model, for example, divide one or more layers of mesh in each physiological structure layer, which is not limited, and may set a mesh division manner by a user.
In constructing the first mesh model, the number of meshes in each physiological structural layer, the size of the meshes, and initial optical parameters (e.g., initial light absorption coefficients) are set for each mesh. The light propagation simulation can be performed by using a Monte Carlo light propagation model to simulate the light propagation process in the first grid model so as to acquire the linear relation between the reference light intensity value variation and the reference optical parameter variation of the first grid model, wherein the reference light intensity value variation and the reference optical parameter variation can be set automatically in the simulation process. That is, a linear relationship between the reference light intensity value variation amount and the reference optical parameter variation amount of the first mesh model, and the initial optical parameters of the respective meshes in the first mesh model are obtained.
In step S103, a first predicted optical parameter is given to each grid, and based on the first predicted optical parameter and the initial optical parameter, a first iterative operation is performed on each grid according to the linear relationship, so as to determine the predicted optical parameter of each grid based on the result of the first iterative operation. Specifically, the linear relationship between the light intensity value variation amount and the optical parameter variation amount of each grid follows the linear relationship between the reference light intensity value variation amount and the reference optical parameter variation amount described above.
For example, for a certain grid, the first predicted optical parameter variation may be derived based on the deviation between the first predicted optical parameter and the initial optical parameter. Based on the first predicted optical parameter variation and the linear relation, a first predicted light intensity variation can be obtained, and then, whether the first predicted light intensity variation is close to the light intensity variation actually detected by the probe is judged. If the difference between the first predicted optical parameter and the light intensity variation actually detected by the probe is larger, the deviation between the first predicted optical parameter and the actual optical parameter is larger, a new first predicted optical parameter is given to the grid, the first iterative operation is continuously performed based on the new first predicted optical parameter until the first predicted light intensity variation in the result of the first iterative operation is close to the light intensity variation actually detected by the probe, and the first iterative operation is stopped and the new first predicted optical parameter of the grid is taken as the predicted optical parameter. Wherein the first iterative operation may be a conjugate gradient algorithm to quickly coarsely estimate each mesh in the first mesh model.
In step S104, according to the size of the predicted optical parameter of each grid, a grid of interest is determined, and after the grid of interest is refined, a second iterative operation is performed to determine the target optical parameter of each refined grid based on the result of the second iterative operation. Specifically, the predicted optical parameters of each grid may be compared with preset optical parameters, which may be set by the user. For example, a grid in which the optical parameter is expected to be larger than the preset optical parameter is considered as a grid of interest, which is taken as an example only, and other methods of determining the grid of interest are not excluded.
After the grid of interest is determined, further refining processing is carried out on the grid of interest, so that more refining operations are concentrated in important areas, and the accuracy and precision of image reconstruction are improved. Each of the attention grids is subjected to finer grid division, and other original grids except for the attention grids may be combined into one or more original grids, or the other original grids may be not processed and the state of the original grids may be maintained, which is not limited. The refinement method is not limited either, and for example, new nodes may be added uniformly to each grid to perform uniform refinement.
After the grid of interest is refined, individual refined grids may be obtained and a second iterative operation based on the individual refined grids is performed. And performing a second iterative operation by using Landweber algorithm to improve the precision and accuracy of image reconstruction. Specifically, the second iterative operation may also be to configure variable optical parameters for each refinement grid, then obtain a deviation value between the variable optical parameters and the initial optical parameters, obtain a variable light intensity variation according to the linear relationship, and use the variable optical parameters of the refinement grid as target optical parameters when the variable light intensity variation approaches the light intensity variation actually detected by the probe. This is merely taken as an example and does not limit the specific embodiments.
In step S105, based on the target optical parameters of each thinned mesh and the predicted optical parameters of the original mesh that is not thinned, blood oxygen information of the near-infrared image and blood oxygen information of the tomographic image are obtained, thereby obtaining a reconstructed near-infrared image and tomographic image. For example, in the case where the optical parameter is an absorption coefficient, the target optical parameter may be a target absorption coefficient, and the predicted optical parameter may be a predicted absorption coefficient. The blood oxygen information may include oxygen-containing hemoglobin concentration (HbO) and deoxyhemoglobin concentration (HbR) information, and in particular, the oxygen-containing hemoglobin concentration (HbO) and deoxyhemoglobin concentration (HbR) information of each of the refined and non-refined original grids may be obtained based on a target absorption coefficient of each of the refined grids and an expected absorption coefficient of the non-refined original grid, and light of different wavelengths emitted simultaneously by light sources in the near infrared data acquisition device and the tomography device, according to lamberbi law. And adding the oxygen-containing hemoglobin concentration (HbO) of each refined grid and the original grid which is not refined, and adding the deoxidized hemoglobin concentration (HbR) of each refined grid and the original grid which is not refined, so that blood oxygen information of the reconstructed near infrared image and blood oxygen information of the tomographic image can be obtained.
This is merely taken as an example and does not limit the specific embodiments.
In step S106, the reconstructed near infrared image and tomographic image are subjected to image fusion for detecting blood oxygen information of the muscle region of interest. Specifically, the tomographic image and the near-infrared image can be registered, the tomographic image and the near-infrared image are converted into images under the same coordinate system, and then the tomographic image and the near-infrared image are subjected to image fusion by using an image superposition algorithm.
In the present application, the arrows shown in the figures of the respective steps are merely examples of the execution sequence, and the technical solution of the present application is not limited to the execution sequence described in the embodiments, and the respective steps in the execution sequence may be performed in combination, may be performed in decomposition, and may be exchanged as long as the logical relationship of the execution contents is not affected.
Wherein registering the tomographic image and the near infrared image may include: determining a reference coordinate system and calculating a transformation matrix based on common features in the tomographic image and the near infrared image; applying a transformation matrix to the near infrared image to obtain a transformed near infrared image; and converting the converted near infrared image into the same coordinate system of the tomographic image, and then carrying out image fusion by using an image fusion algorithm. In the process of image fusion by using an image fusion algorithm, interpolation processing is respectively carried out on the tomographic image and the near infrared image by adopting an interpolation processing method, so that the resolutions of the tomographic image and the near infrared image are consistent, a fused image after fusion can be obtained, and the fused image can intuitively display three-dimensional blood oxygen information with high resolution.
Thus, the accurate detection of the blood oxygen information of the physiological activities of the neuromuscular tissue on the macro scale is realized based on the near infrared spectrum detection technology, the blood microcirculation information of the large-range and deep layers of the neuromuscular tissue is obtained, the high-precision detection of the blood microcirculation information of the neuromuscular tissue on the mesoscale is realized based on the layered optical tomography, and the high-spatial resolution information of the blood microcirculation of the local shallow surface layer of the neuromuscular tissue is obtained. Through the fusion of the two information, multidimensional blood oxygen detection data analysis combined with local area and deep and shallow surface layer in a large range can be obtained.
In some embodiments of the present application, determining the predicted optical parameters of each grid based on the result of the first iterative operation specifically includes obtaining a first predicted light intensity variation of each grid based on the first iterative operation; and when the deviation between the first predicted light intensity variation and the detected light intensity variation is smaller than a first threshold value, the corresponding first predicted optical parameter is used as the predicted optical parameter. Specifically, the deviation may be a deviation, for example, the deviation of the first predicted optical parameter relative to the initial optical parameter may be obtained based on the first predicted optical parameter and the initial optical parameter, and the first predicted light intensity variation may be obtained by performing a first iterative operation according to a linear relationship between the reference light intensity value variation and the reference optical parameter variation. If the deviation between the first predicted light intensity variation and the detected light intensity variation is smaller than the first threshold, it is indicated that the first predicted light intensity variation is close to the detected light intensity variation, and it may be further indicated that the first predicted optical parameter at this time is close to the real optical parameter of the grid, and the first predicted optical parameter may be used as the predicted optical parameter.
In addition, if the deviation is greater than or equal to a first threshold value, the first predicted optical parameter of the grid is larger than the actual optical parameter, the first predicted optical parameter of the grid is updated, and a first iterative operation is continuously performed based on the updated first predicted optical parameter until the deviation between the updated first predicted light intensity variation obtained based on the updated first predicted optical parameter and the detected light intensity variation is smaller than the first threshold value.
In some embodiments of the present application, determining the grid of interest specifically includes comparing the predicted optical parameters of the respective grids according to the magnitude of the predicted optical parameters of the respective grids, taking the grid with the predicted optical parameters greater than or equal to the preset optical parameters as the grid of interest, and taking the grid with the predicted optical parameters less than the predicted optical parameters as the coarse grid. The predicted optical parameter is not particularly limited, and may be set by the user. When the predicted optical parameter is greater than or equal to the preset optical parameter, the optical parameter of the grid can be considered to change obviously, so that the grid is determined to be the concerned grid, and the concerned grid is used for subsequent simulation, operation and the like, so that the precision and accuracy of the reconstructed image can be improved.
Further, the method of determining the grid of interest further specifically includes obtaining an average of the predicted optical parameters of each grid and each adjacent grid thereof; comparing the average value with the maximum expected optical parameter in each adjacent grid, and determining the grid as a grid of interest if the average value is greater than or equal to a preset percentage of the maximum expected optical parameter. For example, a grid is selected, wherein each adjacent grid adjacent to the a grid has six, and the predicted optical parameters of the a grid and the adjacent six adjacent grids are summed and then averaged. Then, the largest predicted optical parameter is selected among the six adjacent grids. The average value is compared to a maximum predicted optical parameter, wherein the predetermined percentage is between 60% -100%. Taking a preset percentage of 60% as an example, if the average value is greater than or equal to 60% of the maximum expected optical parameter, the a-grid may be considered as the grid of interest. This is merely illustrative, and does not limit the specific embodiments.
In some embodiments of the present application, determining the target optical parameters of each refinement mesh based on the result of the second iterative operation specifically includes: refining each concerned grid, endowing each refined grid with a second predicted optical parameter, and carrying out second iterative operation on the refined grids according to the linear relation based on the second predicted optical parameter and the predicted optical parameter to obtain a second predicted light intensity variation of each refined grid; and when the deviation between the second predicted light intensity variation and the detected light intensity variation is smaller than a second threshold value, the corresponding second predicted optical parameter is used as the target optical parameter. That is, when the deviation between the second predicted light intensity variation and the detected light intensity variation is smaller than the second threshold value, it is indicated that the second predicted light intensity variation is closer to the detected light intensity variation, and the second predicted optical parameter is taken as the target optical parameter. And if the second predicted light intensity variation is greater than or equal to a second threshold value, continuing to update the second predicted optical parameter, and performing a second iterative operation based on the updated second predicted optical parameter.
Illustratively, as shown in fig. 2, in step S201, the muscle region of interest is discretized into a uniformly rough first mesh model based on the physiological structure of the muscle region of interest, and a linear relationship between the reference light intensity value variation amount and the reference optical parameter variation amount is obtained in the process of creating the first mesh model. In step S202, a conjugate gradient algorithm is used to perform a first iterative operation, and the predicted optical parameters of each grid are determined based on the result of the first iterative operation. In step S203, the predicted optical parameters of the respective meshes are compared, the mesh with the larger optical parameter variation is taken as the mesh of interest, and the mesh of interest is further refined. In step S204, a Landweber algorithm is adopted to perform a second iterative operation on each refined grid after refinement, so as to obtain a second predicted light intensity variation of each refined grid. In step S205, it is determined whether the deviation degree of the second predicted light intensity variation from the detected light intensity variation is smaller than the second threshold, if yes, the second iterative operation is ended, and if no, step S204 is continuously performed.
In some embodiments of the application, the detection method comprises: acquiring an initial light intensity value of a concerned muscle area of a subject when the subject executes a resting state task and a detected light intensity value of the concerned muscle area after executing a target task based on a near infrared data acquisition device and a tomography device respectively, wherein the subject executes the resting state task before executing the target task; and taking the deviation of the representative value of the detected light intensity value relative to the representative value of the initial light intensity value as the detected light intensity variation. Where the resting task may be to remain in a relaxed state, such as the subject holding his arms perpendicular to both sides of the body for 3 minutes. The initial light intensity value when the subject is kept in a relaxed state is acquired based on the near infrared data acquisition device and the tomographic imaging apparatus, respectively, and specifically, an average value or a maximum value of the initial light intensity values acquired when the resting state task is performed may be taken as a representative value of the initial light intensity values. Meanwhile, the average value or the maximum value of the detected light intensity values when the subject performs the target task may also be used as a representative value of the detected light intensity values.
In some embodiments of the application, the distance between the light source and the light detector in the near infrared data acquisition device is greater than a first preset distance to obtain a detected light intensity value for a deep layer of a muscle region of interest, wherein the deep layer comprises at least a epidermis layer, a dermis layer, a subcutaneous fat layer, and a muscle tissue layer. Further, the distance between the light source and the light detector in the near infrared data acquisition device is between 1 cm and 3cm, so that a large range of tissue information of the muscle region of interest is acquired.
The distance between the light source and the light detector in the tomography equipment is smaller than a second preset distance so as to obtain a detection light intensity value of a shallow layer of the concerned muscle area, wherein the shallow layer at least comprises an epidermis layer and a dermis layer. Further, the distance between the light source and the light detector in the tomography device is between 0.2 and 1.2mm, so that tissue information of a small range of the muscle region of interest is obtained, and the accuracy of scanning the muscle region of interest by light emitted by the light source is improved.
The light detector in the near infrared data acquisition device scans and images a wide tissue area, which may cause light to diffuse in the tissue area, thereby reducing imaging resolution and failing to obtain refined blood oxygen information. And the light detector in the tomography equipment scans and images a small-range tissue area, so that the reconstruction is facilitated to obtain a high-resolution image, and more abundant and finer blood oxygen information is provided.
Therefore, based on the detected light intensity variation obtained by the near infrared data acquisition equipment and the tomography equipment, respectively reconstructing a near infrared image and a tomography image, and carrying out image fusion on the reconstructed near infrared image and the tomography image to obtain a fusion image, so that the fusion image has richer and comprehensive blood oxygen information, and a doctor can accurately analyze the physiological condition of the nerve muscle of a subject based on the fusion image.
In some embodiments of the application, the optical parameter is an absorption coefficient, or the optical parameter is an absorption coefficient and a scattering coefficient.
Fig. 3 is a schematic diagram showing the structure of a system for detecting blood oxygen information of neuromuscular according to an embodiment of the present application. The system 300 comprises an interface 301 and a processor 302, the interface 301 being configured to obtain the detected light intensity variation of the muscle region of interest of the subject before and after performing the target task based on the near infrared data acquisition device and the tomography device, respectively. The processor 302 is configured to: creating a first mesh model based on the physiological structure of the muscle region of interest to reconstruct a near infrared image and a tomographic image, respectively, comprising: obtaining a linear relation between a reference light intensity value variation and a reference optical parameter variation of a first grid model and initial optical parameters of each grid in the first grid model; assigning a first predicted optical parameter to each grid, and performing a first iterative operation on each grid according to the linear relation based on the first predicted optical parameter and the initial optical parameter to determine the predicted optical parameter of each grid based on the result of the first iterative operation; determining the concerned grids according to the expected optical parameters of each grid, refining the concerned grids, and performing a second iterative operation to determine the target optical parameters of each refined grid based on the result of the second iterative operation; obtaining blood oxygen flow information of the near infrared image and blood oxygen information of the tomographic image based on the target optical parameters of each thinned grid and the expected optical parameters of the original grid which is not thinned; and performing image fusion on the reconstructed near infrared image and the tomographic image to detect blood oxygen information of the concerned muscle region. Thus, the resolution of the blood oxygen information of the nerve muscle can be improved, and the blood oxygen information of the nerve muscle can be obtained comprehensively.
The detection methods for detecting blood oxygen information of neuromuscular in various embodiments of the present application may be combined herein, and will not be described herein.
The interface 301 may transmit information and may include, but is not limited to, a network adapter, cable connector, serial connector, USB connector, parallel connector, high speed data transmission adapter, etc., such as fiber optic, USB 3.0, thunderbolt interface (Thunderbolt), etc., a wireless network adapter, such as WiFi adapter, telecommunication (3G, 4G/LTE, etc.) adapter, etc. In some embodiments, the interface 301 may be a network interface and the system 300 may be connected to a network, such as, but not limited to, a local area network or the internet, through the interface 301.
The processor 302 may be a processing device including one or more general purpose processing devices, such as a microprocessor, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or the like. More specifically, the processor 302 may be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, a processor running other instruction sets, or a processor running a combination of instruction sets. The processor 302 may also be one or more special purpose processing devices such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a system on a chip (SoC), or the like.
Furthermore, the method for detecting blood oxygen information of a neuromuscular may also be stored in a computer readable storage medium in the form of computer program instructions which when executed by a processor cause the processor to perform the method for detecting blood oxygen information of a neuromuscular according to the various embodiments of the application to perform the various steps of the method for detecting blood oxygen information of a neuromuscular according to the various embodiments of the application. The storage medium may include read-only memory (ROM), flash memory, random Access Memory (RAM), dynamic Random Access Memory (DRAM) such as Synchronous DRAM (SDRAM) or Rambus DRAM, static memory (e.g., flash memory, static random access memory), etc., upon which computer-executable instructions may be stored in any format.
Furthermore, although exemplary embodiments have been described herein, the scope thereof includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of the various embodiments across), adaptations or alterations as pertains to the present application. The elements in the claims are to be construed broadly based on the language employed in the claims and are not limited to examples described in the present specification or during the practice of the application, which examples are to be construed as non-exclusive. It is intended, therefore, that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the above detailed description, various features may be grouped together to streamline the application. This is not to be interpreted as an intention that the disclosed features not being claimed are essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with one another in various combinations or permutations. The scope of the application should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application, the scope of which is defined by the claims. Various modifications and equivalent arrangements of this application will occur to those skilled in the art, and are intended to be within the spirit and scope of the application.
Claims (10)
1. A detection method for detecting blood oxygen information of a neuromuscular, the detection method comprising:
acquiring the detected light intensity variation of the concerned muscle region of the detected person before and after the target task is executed based on the near infrared data acquisition equipment and the tomography equipment respectively;
a processor creates a first mesh model based on a physiological structure of the muscle region of interest to reconstruct a near infrared image and a tomographic image, respectively, comprising:
obtaining a linear relation between a reference light intensity value variation and a reference optical parameter variation of a first grid model and initial optical parameters of each grid in the first grid model;
Assigning first predicted optical parameters to each grid, and performing first iterative operation on each grid according to the linear relation based on the first predicted optical parameters and the initial optical parameters;
Based on the first iterative operation, a first predicted light intensity variation of each grid is obtained;
when the deviation between the first predicted light intensity variation and the detected light intensity variation is smaller than a first threshold value, the corresponding first predicted optical parameter is used as the predicted optical parameter;
Determining a grid of interest according to the size of the expected optical parameters of each grid;
Refining each concerned grid, endowing each refined grid with a second predicted optical parameter, and carrying out second iterative operation on the refined grids according to the linear relation based on the second predicted optical parameter and the predicted optical parameter to obtain a second predicted light intensity variation of each refined grid;
When the deviation between the second predicted light intensity variation and the detected light intensity variation is smaller than a second threshold value, the corresponding second predicted optical parameter is used as a target optical parameter;
Obtaining blood oxygen information of the near infrared image and blood oxygen information of the tomographic image based on the target optical parameters of each thinned grid and the predicted optical parameters of the original grid which is not thinned;
And performing image fusion on the reconstructed near infrared image and the tomographic image to detect blood oxygen information of the concerned muscle region.
2. The method of claim 1, wherein determining the grid of interest based on the magnitude of the predicted optical parameter for each grid comprises:
and comparing the predicted optical parameters of the grids, taking the grid with the predicted optical parameters being greater than or equal to the preset optical parameters as a concerned grid, and taking the grid with the predicted optical parameters being smaller than the predicted optical parameters as a coarse grid.
3. The method of detecting according to claim 2, wherein the method of determining the grid of interest further specifically comprises:
Obtaining the average value of the predicted optical parameters of each grid and each adjacent grid;
comparing the average value with the maximum expected optical parameter in each adjacent grid, and determining the grid as a grid of interest if the average value is greater than or equal to a preset percentage of the maximum expected optical parameter.
4. The method of detection according to claim 1, characterized in that the method of detection comprises:
Acquiring an initial light intensity value of a concerned muscle area of a subject when the subject executes a resting state task and a detected light intensity value of the concerned muscle area after executing a target task based on a near infrared data acquisition device and a tomography device respectively, wherein the subject executes the resting state task before executing the target task;
And taking the deviation of the representative value of the detected light intensity value relative to the representative value of the initial light intensity value as the detected light intensity variation.
5. The detection method according to claim 1, wherein a distance between the light source and the light detector in the near infrared data collection device is greater than a first preset distance; the distance between the light source and the light detector in the tomography device is smaller than a second preset distance.
6. The method of claim 5, wherein a distance between the light source and the light detector in the near infrared data collection device is between 1-3 cm; the distance between the light source and the light detector in the tomography device is between 0.2 and 1.2 mm.
7. The method according to claim 1, wherein the optical parameter is an absorption coefficient or the optical parameter is an absorption coefficient and a scattering coefficient.
8. The method of claim 1, wherein the first iterative operation is performed using a conjugate gradient algorithm and the second iterative operation is performed using a Landweber algorithm.
9. A system for detecting blood oxygenation information of a neuromuscular, the system comprising an interface configured to:
acquiring the detected light intensity variation of the concerned muscle region of the detected person before and after the target task is executed based on the near infrared data acquisition equipment and the tomography equipment respectively;
the processor is configured to:
creating a first mesh model based on the physiological structure of the muscle region of interest to reconstruct a near infrared image and a tomographic image, respectively, comprising:
obtaining a linear relation between a reference light intensity value variation and a reference optical parameter variation of a first grid model and initial optical parameters of each grid in the first grid model;
Assigning first predicted optical parameters to each grid, and performing first iterative operation on each grid according to the linear relation based on the first predicted optical parameters and the initial optical parameters;
Based on the first iterative operation, a first predicted light intensity variation of each grid is obtained;
when the deviation between the first predicted light intensity variation and the detected light intensity variation is smaller than a first threshold value, the corresponding first predicted optical parameter is used as the predicted optical parameter; determining a grid of interest according to the size of the expected optical parameters of each grid;
Refining each concerned grid, endowing each refined grid with a second predicted optical parameter, and carrying out second iterative operation on the refined grids according to the linear relation based on the second predicted optical parameter and the predicted optical parameter to obtain a second predicted light intensity variation of each refined grid;
When the deviation between the second predicted light intensity variation and the detected light intensity variation is smaller than a second threshold value, the corresponding second predicted optical parameter is used as a target optical parameter;
Obtaining blood oxygen flow information of the near infrared image and blood oxygen information of the tomographic image based on the target optical parameters of each thinned grid and the expected optical parameters of the original grid which is not thinned;
And performing image fusion on the reconstructed near infrared image and the tomographic image to detect blood oxygen information of the concerned muscle region.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which when executed by a processor causes the processor to perform the detection method for detecting blood oxygen information of neuromuscular according to any one of claims 1 to 8.
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