A near-infrared brain function imaging system and method

Abstract

The present invention provides a near-infrared brain function imaging system and method, which relates to the field of image processing technology and is applied to a distributed computing platform. The near-infrared brain function imaging system includes: a three-dimensional processing module, a data acquisition module, a data transmission module, a data processing module, a light source adjustment module, a distributed analysis module and an imaging display module; a three-dimensional brain model of an actual brain is established by the three-dimensional processing module, and a plurality of brain processing partitions are divided according to the three-dimensional brain model; the brain processing partitions are processed in parallel by using the nodes of the distributed computing platform, so that the entire brain function imaging task becomes a plurality of subtasks that can be processed in parallel, thereby improving the speed of data processing and analysis, providing accurate brain area division, and making the brain function imaging result more accurate. At the same time, the light source adjustment module is used to adjust the near-infrared light, so as to obtain a higher quality imaging result.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a near-infrared brain function imaging system and method. Background Art

[0002] At present, near-infrared brain functional imaging technology is usually used to study or diagnose brain functional activities. In near-infrared brain functional imaging, near-infrared light of a specific wavelength is sent to the head tissue, then scattered and absorbed. The scattered light is collected and recorded to analyze its intensity, time difference, and wavelength changes, etc., thereby analyzing the state of the brain area and obtaining brain function data.

[0003] In the existing technology, near-infrared brain functional imaging technology usually takes a long time to obtain complete brain function data, because data acquisition usually takes several seconds to several minutes, plus the time taken by the analysis process after data acquisition, which limits the application of infrared brain functional imaging in real-time applications and rapid diagnosis or analysis. Summary of the invention

[0004] The technical problem solved by the present invention is how to improve the efficiency of near-infrared brain function imaging.

[0005] The present invention provides a near-infrared brain function imaging system, which is applied to a distributed computing platform, and comprises: a three-dimensional processing module, a data acquisition module, a data transmission module, a data processing module, a light source adjustment module, a distributed analysis module and an imaging display module; the distributed computing platform comprises a plurality of nodes, each of which corresponds to a mirror image of the data processing module, a mirror image of the light source adjustment module and a mirror image of the distributed analysis module;

[0006] The data acquisition module is used to: obtain the transmission light signal of near-infrared light;

[0007] The three-dimensional processing module is used to: establish a three-dimensional brain model of the actual brain according to the transmitted light signal;

[0008] Dividing a plurality of brain processing partitions according to the three-dimensional brain model, each of the brain processing partitions corresponding to one or more brain functions;

[0009] The data acquisition module is also used to: divide the transmitted light signal according to the brain processing partitions, and obtain a partition light signal corresponding to each brain processing partition;

[0010] The data transmission module is used to: obtain the node corresponding to each brain processing partition according to a preset node allocation rule, and send the partition optical signal of the brain processing partition to the node;

[0011] The data processing module is used to: obtain the blood oxygen concentration change of the brain processing partition according to the partition light signal of the brain processing partition; and perform prediction according to the partition light signal to obtain the optimal light source parameter range of the brain processing partition;

[0012] The light source adjustment module is used to: input the optimal light source parameters of the brain processing partition and the partition light signal of the brain processing partition into the light source adjustment strategy network, and obtain the near-infrared light adjustment strategy of the brain processing partition corresponding to each node according to the output of the light source adjustment strategy network;

[0013] According to the near-infrared light adjustment strategy, the wavelength and power of the near-infrared light and the irradiation time of the brain processing partition are obtained;

[0014] adjusting the near-infrared light according to the wavelength, the power and the irradiation time of the near-infrared light;

[0015] When the partition light signal of the brain processing partition is within the optimal light source parameter range, stop adjusting the near infrared light;

[0016] The distributed analysis module is used to obtain a partition imaging of each brain processing partition according to the change of the blood oxygen concentration of each brain processing partition;

[0017] Obtaining brain function imaging results according to the partition imaging of all the brain processing partitions;

[0018] The imaging display module is used to display the brain function imaging results through a preset display mode.

[0019] Optionally, the three-dimensional processing module is specifically used for:

[0020] Obtaining point cloud data of the actual brain according to the transmitted light signal;

[0021] Obtaining a surface three-dimensional model of the actual brain according to the spatial position of the actual brain in real space and the point cloud data of the actual brain;

[0022] According to the transmitted light signal, obtaining the internal optical parameter distribution of the actual brain by an inverse problem solving method;

[0023] Obtaining an internal three-dimensional model of the actual brain according to the optical parameter distribution;

[0024] The three-dimensional brain model of the actual brain is obtained according to the internal three-dimensional model and the surface three-dimensional model.

[0025] Optionally, the data acquisition module is further used for:

[0026] According to the brain processing partition, obtaining the position of the brain processing partition in the three-dimensional brain model;

[0027] According to the position of the transmitted light signal on the three-dimensional brain model, obtaining the transmitted light signal corresponding to the position;

[0028] The transmitted light signal corresponding to the position is used as the partitioned light signal.

[0029] Optionally, the data transmission module is specifically used to:

[0030] According to the brain function of each of the brain processing partitions, obtaining a functional connection weight of two of the brain processing partitions;

[0031] Allocating the two brain processing partitions whose functional connection weights are greater than a preset threshold to two adjacent nodes;

[0032] The partitioned optical signal of the brain processing partition is sent to the node through data communication technology.

[0033] Optionally, the data processing module is specifically used to:

[0034] Obtaining a light intensity change of the partitioned light signal according to the partitioned light signal of the brain processing partition;

[0035] Obtaining a change in optical density of the partitioned optical signal according to the change in light intensity;

[0036] According to the optical density change, the blood oxygen concentration change of the brain processing partition is obtained through a spectral change model.

[0037] Optionally, the distributed analysis module is specifically used for:

[0038] By means of the node, the change of the blood oxygen concentration of the brain processing partition corresponding to the node is mapped to the position of the brain processing partition in the three-dimensional brain model to obtain the time series data of the brain processing partition;

[0039] Obtaining a brain function spatial distribution map of the brain processing partition according to the time series data;

[0040] The brain function spatial distribution map is imaged as the partition of the brain processing partition.

[0041] Optionally, obtaining brain function imaging results based on the partition imaging of all the brain processing partitions includes:

[0042] According to the function of each of the brain processing partitions, obtaining functional connections between the brain processing partitions;

[0043] The brain function imaging result is obtained according to the brain function spatial distribution map and the functional connection between the brain processing partitions.

[0044] Optionally, the near-infrared brain function imaging system further includes: a light source module; the light source module is used to: emit the near-infrared light to the actual brain.

[0045] Optionally, the near-infrared brain function imaging system further includes a brain motion tracking module;

[0046] The brain motion tracking module is used to: obtain the position change of the actual brain in the real space;

[0047] The three-dimensional brain model is updated according to the posture change of the actual brain in the real space.

[0048] The present invention also provides a near-infrared brain function imaging method, which is applied to any of the above-mentioned near-infrared brain function imaging systems, and the near-infrared brain function imaging method comprises:

[0049] Acquiring a transmission light signal of near-infrared light;

[0050] establishing a three-dimensional brain model of the actual brain according to the transmitted light signal;

[0051] Dividing a plurality of brain processing partitions according to the three-dimensional brain model, each of the brain processing partitions corresponding to one or more brain functions;

[0052] According to the brain processing partitions, the transmitted light signal is segmented according to the brain processing partitions to obtain a partition light signal corresponding to each brain processing partition;

[0053] According to a preset node allocation rule, the node corresponding to each of the brain processing partitions is obtained, and the partition optical signal of the brain processing partition is sent to the node;

[0054] Obtaining a change in blood oxygen concentration of the brain processing partition according to the partition light signal of the brain processing partition; and performing a prediction according to the partition light signal to obtain an optimal light source parameter range of the brain processing partition;

[0055] Inputting the optimal light source parameters of the brain processing partition and the partition light signal of the brain processing partition into a light source adjustment strategy network, and obtaining the near-infrared light adjustment strategy of the brain processing partition corresponding to each node according to the output of the light source adjustment strategy network;

[0056] According to the near-infrared light adjustment strategy, the wavelength and power of the near-infrared light and the irradiation time of the brain processing partition are obtained;

[0057] adjusting the near-infrared light according to the wavelength, the power and the irradiation time of the near-infrared light;

[0058] When the partition light signal of the brain processing partition is within the optimal light source parameter range, stop adjusting the near infrared light;

[0059] Obtaining a partition imaging of each brain processing partition according to the change of the blood oxygen concentration of each brain processing partition;

[0060] Obtaining brain function imaging results according to the partition imaging of all the brain processing partitions;

[0061] The brain function imaging result is displayed through a preset display.

[0062] The near-infrared brain function imaging system and method of the present invention performs three-dimensional modeling of the actual brain, divides the three-dimensional brain model into brain processing partitions, divides the acquired transmission light signal according to each brain processing partition, obtains the partition light signal corresponding to each brain processing partition, and then sends the partition light signal of each brain processing partition to the node corresponding to the distributed computing platform for processing, obtains the optimal light source parameters corresponding to each brain processing partition and the partition imaging corresponding to each brain processing partition, integrates all the partition imaging, obtains the brain function imaging result, and uses the light source adjustment module to adjust the near-infrared light according to the predicted optimal light source parameters, ensures that the partition light signal of each brain processing partition is within the ideal parameter range, thereby improving the imaging effect. The nodes of the distributed computing platform are used to process the partition light signals of the brain processing partitions in parallel, so that the entire brain function imaging task becomes a plurality of subtasks that can be processed in parallel, thereby improving the speed of data processing and analysis, and at the same time, provides accurate brain area division, so that the brain function imaging result is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the structure of a near-infrared brain function imaging system in one embodiment of the present invention;

[0064] Figure 2 It is a schematic structural diagram of a near-infrared brain function imaging system in another embodiment of the present invention;

[0065] Figure 3 Schematic diagram of the process of near-infrared brain function imaging method in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0067] Combination Figure 1 As shown, the present invention provides a near-infrared brain function imaging system, which is applied to a distributed computing platform, and comprises: a three-dimensional processing module, a data acquisition module, a data transmission module, a data processing module, a light source adjustment module, a distributed analysis module and an imaging display module; the distributed computing platform comprises a plurality of nodes, each of which corresponds to a mirror image of the data processing module, a mirror image of the light source adjustment module and a mirror image of the distributed analysis module;

[0068] In a preferred embodiment of the present invention, the data acquisition module, the three-dimensional processing module, the data transmission module, the data processing module, the distributed analysis module and the imaging display module are communicatively connected in sequence, and the near-infrared brain functional imaging system is applied to a distributed computing platform. The distributed computing platform is configured with multiple nodes, and each node corresponds to a mirror image of a data processing module and a mirror image of a distributed analysis module, which can realize the same functions as the data processing module and the distributed analysis module, and realize parallel computing in sequence.

[0069] The data acquisition module is used to: obtain the transmission light signal of near-infrared light;

[0070] Specifically, during the data collection process, a near-infrared light source will be set around the head, and part of the near-infrared light will pass through the brain tissue. The data collection module collects the penetrating transmitted light and converts it into an electrical signal, namely the transmitted light signal, for subsequent processing.

[0071] The three-dimensional processing module is used to: establish a three-dimensional brain model of the actual brain according to the transmitted light signal;

[0072] A plurality of brain processing partitions are divided according to the three-dimensional brain model, and each of the brain processing partitions corresponds to one or more brain functions.

[0073] Specifically, the position, shape and connection relationship of different brain regions are reflected by a three-dimensional model, and accurate brain region marking and positioning are provided for subsequent data processing and analysis. Imaging technology is usually required to establish a three-dimensional model of the brain. In a preferred embodiment of the present invention, magnetic resonance imaging can be used. By aligning, segmenting and reconstructing the magnetic resonance imaging, the boundaries and spatial positions of different brain regions can be obtained. The above-mentioned boundary and position information can be used to construct a three-dimensional model of the brain, and the brain processing partitions are divided by the three-dimensional brain model. In a preferred embodiment of the present invention, the brain can be divided into multiple regions according to function or anatomical structure, and each brain processing partition corresponds to one or more specific brain functions, such as movement, language and vision.

[0074] The data acquisition module is also used to: divide the transmitted light signal according to the brain processing partitions according to the brain processing partitions, and obtain the partition light signal corresponding to each brain processing partition.

[0075] Specifically, in order to segment the transmitted light signal according to the brain processing partitions, it is necessary to map the transmitted light signal to the corresponding processing partitions according to the actual three-dimensional model of the brain and the division of the brain processing partitions. The mapping process usually requires calculations based on spatial position analysis. Once the transmitted light signal is segmented according to the brain processing partitions, the partition light signals corresponding to each brain processing partition can be obtained. These partition light signals can represent the activity levels of different brain regions under specific functional tasks. For example, in visual tasks, the transmitted light signal can be segmented into light signals of different processing partitions such as the visual cortex, motor cortex, and temporoparietal cortex.

[0076] The data transmission module is used to obtain the node corresponding to each brain processing partition according to a preset node allocation rule, and send the partition optical signal of the brain processing partition to the node.

[0077] Specifically, the data transmission module determines the node corresponding to each brain processing partition according to the preset node allocation rules, and sends the partition optical signal of the brain processing partition to the corresponding node, wherein the optical signal of the brain processing partition is transmitted to the corresponding computing node for processing, and the computing node is used to receive and process the transmitted light signal, and perform subsequent data processing, analysis or imaging reconstruction.

[0078] The data processing module is used to: obtain the change in blood oxygen concentration of the brain processing partition based on the partition light signal of the brain processing partition; and make predictions based on the partition light signal to obtain the optimal light source parameter range of the brain processing partition.

[0079] Specifically, the data processing module can select algorithms and methods for obtaining changes in blood oxygen concentration based on partitioned light signals according to specific needs and research objectives. In a preferred embodiment of the present invention, methods based on classical linear models, such as generalized linear models or wavelet transforms, can be used, or methods based on machine learning, such as deep learning, can be used to perform statistical analysis and pattern recognition on partitioned light signals to obtain more accurate changes in blood oxygen concentration. Predictions are made based on the partitioned light signals to obtain the optimal light source parameter range for the brain processing partitions, wherein, in a preferred embodiment of the present invention, a convolutional neural network can be used to take the partitioned light signals as the input of the convolutional neural network, and the optimal light source parameter range for the brain processing partitions can be obtained based on the output of the convolutional neural network. This range is the optimal range of power, wavelength, and irradiation time of near-infrared light.

[0080] The light source adjustment module is used to: input the optimal light source parameters of the brain processing partition and the partition light signal of the brain processing partition into the light source adjustment strategy network, and obtain the near-infrared light adjustment strategy of the brain processing partition corresponding to each node according to the output of the light source adjustment strategy network;

[0081] According to the near-infrared light adjustment strategy, the wavelength and power of the near-infrared light and the irradiation time of the brain processing partition are obtained;

[0082] adjusting the near-infrared light according to the wavelength, the power and the irradiation time of the near-infrared light;

[0083] When the partition light signal of the brain processing partition is within the optimal light source parameter range, stop adjusting the near-infrared light.

[0084] Specifically, after obtaining the optimal light source parameters of the brain processing partition, the optimal light source parameters are analyzed with the current partition light signal, and the near-infrared light is adjusted based on the analysis results, with the purpose of making the partition light signal of the brain processing partition within the optimal light source parameter range. According to the optimal light source parameters of each brain processing partition and the actual partition light signal data, the near-infrared light adjustment strategy of the brain processing partition corresponding to each node is obtained, and the near-infrared light can be continuously adjusted according to the difference between the real-time light signal and the preset parameters based on the feedback control model, such as the PID controller (proportional-integral-differential controller). Through the feedback of real-time data, the intensity, pulse width, wavelength, etc. of the light source are adjusted to ensure that the light signal falls within the optimal working range. In a preferred embodiment of the present invention, a recursive neural network is used to predict how the light source parameters need to be adjusted next based on past and current light signal data so as to keep the signal within the optimal range. When the monitored light signal is stably within the optimal parameter range, the adjustment behavior can be stopped until adjustment is required again, thereby ensuring that the system maintains efficient and accurate operation in a stable state. By ensuring that the optical signal of each brain processing area is within the ideal parameter range, the signal intensity and contrast of the imaging can be significantly improved, thereby obtaining higher quality imaging results.

[0085] The distributed analysis module is used to obtain a partition imaging of each brain processing partition according to the change of the blood oxygen concentration of each brain processing partition;

[0086] Based on the partition imaging of all the brain processing partitions, brain function imaging results are obtained.

[0087] Specifically, the distributed analysis module converts the changes in blood oxygen concentration of each brain processing partition into images to display the functional activities of the brain processing partitions. In a preferred embodiment of the present invention, the images are represented by different colors or grayscale values, and different brain regions can be colored or displayed in grayscale according to the changes in blood oxygen concentration. The partition imaging of all brain processing partitions is then combined to obtain brain functional imaging results.

[0088] The imaging display module is used to display the brain function imaging results through a preset display mode.

[0089] Specifically, through the preset display mode, the brain function imaging results can be displayed in different ways to further analyze brain activity. In a preferred embodiment of the present invention, a contour map can be used to represent the activity intensity of different areas by using contour lines. Contour lines connect points with the same value to form a set of contour lines, showing the changes in activity intensity. Multimodal display can also be used to combine brain function imaging results with brain images of other modalities, such as structural magnetic resonance imaging, to provide more comprehensive information.

[0090] The near-infrared brain function imaging system of the present invention performs three-dimensional modeling of the actual brain, divides the three-dimensional brain model into brain processing partitions, divides the acquired transmission light signal according to each brain processing partition, obtains the partition light signal corresponding to each brain processing partition, and then sends the partition light signal of each brain processing partition to the node corresponding to the distributed computing platform for processing, obtains the optimal light source parameters corresponding to each brain processing partition and the partition imaging corresponding to each brain processing partition, integrates all the partition imaging, obtains the brain function imaging result, and uses the light source adjustment module to adjust the near-infrared light according to the predicted optimal light source parameters, ensures that the partition light signal of each brain processing partition is within the ideal parameter range, thereby improving the imaging effect. The nodes of the distributed computing platform are used to process the partition light signals of the brain processing partitions in parallel, so that the entire brain function imaging task becomes a plurality of subtasks that can be processed in parallel, thereby improving the speed of data processing and analysis, and at the same time, provides accurate brain area division, so that the brain function imaging result is more accurate.

[0091] In the embodiment of the present invention, the three-dimensional processing module is specifically used for:

[0092] Obtaining point cloud data of the actual brain according to the transmitted light signal;

[0093] Obtaining a surface three-dimensional model of the actual brain according to the spatial position of the actual brain in real space and the point cloud data of the actual brain;

[0094] According to the transmitted light signal, obtaining the internal optical parameter distribution of the actual brain by an inverse problem solving method;

[0095] Obtaining an internal three-dimensional model of the actual brain according to the optical parameter distribution;

[0096] The three-dimensional brain model of the actual brain is obtained according to the internal three-dimensional model and the surface three-dimensional model.

[0097] In this embodiment, the measured transmitted light intensity and propagation time are calculated by the transmitted light signal. By combining the above parameters, discrete point cloud data can be obtained. Then, according to the spatial position of the actual brain in the real space and the point cloud data, a point cloud processing algorithm is used to generate a surface three-dimensional model of the actual brain. This three-dimensional model is based on the brain surface and can accurately present the shape and structure of the brain. With reference to the fact that the propagation of the transmitted light signal is affected by the optical properties of the brain tissue, the propagation path and attenuation of the transmitted light signal in the brain tissue can be inferred by using the inverse problem solving method, thereby obtaining the internal optical parameter distribution of the actual brain. According to the obtained optical parameter distribution, the internal three-dimensional model of the actual brain can be generated by using the light transmission model and the numerical method on the basis of the surface three-dimensional model of the actual brain. The internal model can provide spatial information about the distribution of the optical properties of the brain tissue. Finally, the internal three-dimensional model is combined with the surface three-dimensional model to obtain the brain three-dimensional model of the actual brain. The above content is a specific limitation for establishing a brain three-dimensional model of the actual brain according to the transmitted light signal.

[0098] The near-infrared brain function imaging system of the present invention provides a three-dimensional model of the brain with spatial constraints and references for brain function image processing and analysis, thereby helping to improve the accuracy and reliability of data.

[0099] In an embodiment of the present invention, the data acquisition module is further used to: divide the transmitted light signal according to the brain processing partitions according to the brain processing partitions to obtain partition light signals corresponding to each brain processing partition, including:

[0100] According to the brain processing partition, obtaining the position of the brain processing partition in the three-dimensional brain model;

[0101] According to the position of the transmitted light signal on the three-dimensional brain model, obtaining the transmitted light signal corresponding to the position;

[0102] The transmitted light signal corresponding to the position is used as the partitioned light signal.

[0103] In this embodiment, based on the predefined brain processing partitions, the data acquisition module can determine the position of each processing partition in the three-dimensional brain model, wherein the partitioning can be performed according to brain functional areas or anatomical structures to meet specific research needs. Based on the position of the transmitted light signal in the three-dimensional brain model, the data acquisition module can determine the specific partition corresponding to the signal, and accurately locate the signal through the coordinate information of the position or by using an interpolation method, and assign the corresponding transmitted light signal to the corresponding brain processing partition, and use the transmitted light signal at the determined position as the light signal of the partition. By associating the transmitted light signal with a specific brain processing partition, the segmentation and classification of the light signal is achieved. The above content is for segmenting the transmitted light signal according to the brain processing partition according to the brain processing partition to obtain the specific definition of the partition light signal corresponding to each of the brain processing partitions.

[0104] The near-infrared brain function imaging system of the present invention can effectively associate the light signal with a specific brain function area by dividing the transmitted light signal according to the brain processing partition, thereby providing a more localized optical imaging analysis. At the same time, by analyzing the partitioned light signal of each brain processing partition, the characteristics and changes of each brain function area can be independently studied.

[0105] In an embodiment of the present invention, the data transmission module is used to obtain the node corresponding to each brain processing partition according to a preset node allocation rule, and send the partition optical signal of the brain processing partition to the node, including:

[0106] According to the brain function of each of the brain processing partitions, obtaining a functional connection weight of two of the brain processing partitions;

[0107] Allocating the two brain processing partitions whose functional connection weights are greater than a preset threshold to two adjacent nodes;

[0108] The partitioned optical signal of the brain processing partition is sent to the node through data communication technology.

[0109] In this embodiment, according to the functional characteristics of each brain processing partition, the data transmission module can calculate the functional connection weights between two processing partitions. These weights can represent functional correlation or interaction strength, reflecting the degree of functional connection between different brain regions. The data transmission module compares the calculated functional connection weights with a preset threshold. If the functional connection weights of a processing partition with other processing partitions exceed the threshold, it is allocated to the adjacent node for data transmission. The partitions with higher functional correlation can be transmitted and processed at a closer distance, thereby improving communication efficiency and data processing speed. The above content is for obtaining the nodes corresponding to each brain processing partition according to the preset node allocation rules, and sending the partition optical signal of the brain processing partition to the specific limitation of the node.

[0110] The near-infrared brain function imaging system of the present invention can ensure that partitions with high functional relevance are transmitted and processed preferentially by calculating functional connection weights and allocating adjacent nodes, thereby improving the efficiency and accuracy of distributed data processing.

[0111] In an embodiment of the present invention, the data processing module is used to obtain the change of blood oxygen concentration of the brain processing partition according to the partition optical signal of the brain processing partition, including:

[0112] Obtaining a light intensity change of the partitioned light signal according to the partitioned light signal of the brain processing partition;

[0113] Obtaining a change in optical density of the partitioned optical signal according to the change in light intensity;

[0114] According to the optical density change, the blood oxygen concentration change of the brain processing partition is obtained through a spectral change model.

[0115] In this embodiment, the data processing module can calculate the light intensity change of the partition light signal by comparing the light intensity values ​​at different time points based on the collected partition light signal, indirectly reflecting the concentration change of hemoglobin in the tissue, and providing relevant parameters for the blood supply of the brain processing partition. The data processing module uses optical models and optical properties to translate the light intensity change into the optical density change of the partition light signal. By analyzing the characteristics of the light signal propagating through the tissue, the light absorption change of the blood in the tissue can be obtained, and then the hemoglobin concentration change can be quantitatively analyzed. The above content is a specific definition of the blood oxygen concentration change of the brain processing partition obtained according to the partition light signal of the brain processing partition.

[0116] The near-infrared brain function imaging system of the present invention can achieve quantitative analysis of changes in blood oxygen concentration in brain processing areas through the application of light intensity change, light density change and spectral change models, and provide important information for brain function and disease research.

[0117] In an embodiment of the present invention, the distributed analysis module is used to obtain a partition imaging of each brain processing partition according to the change of the blood oxygen concentration of each brain processing partition, including:

[0118] By means of the node, the change of the blood oxygen concentration of the brain processing partition corresponding to the node is mapped to the position of the brain processing partition in the three-dimensional brain model to obtain the time series data of the brain processing partition;

[0119] Obtaining a brain function spatial distribution map of the brain processing partition according to the time series data;

[0120] The brain function spatial distribution map is imaged as the partition of the brain processing partition.

[0121] In this embodiment, the distributed analysis module maps the changes in blood oxygen concentration of the brain processing partition represented by the node to the corresponding position in the three-dimensional model of the brain through the corresponding relationship with the node. In this way, the time series data of each partition can be obtained, and then each brain processing partition is analyzed and processed according to the time series data to obtain its brain function spatial distribution map. By analyzing the blood oxygen concentration values ​​at different time points, the activity level and functional state of the brain processing partition can be inferred. Finally, the distributed analysis module presents the brain function spatial distribution map of the brain processing partition as a partition imaging. These imaging results can be displayed in images or other forms to intuitively display the functional activity of each brain processing partition. The above content is for obtaining the specific definition of the partition imaging of each brain processing partition according to the changes in the blood oxygen concentration of each brain processing partition.

[0122] The near-infrared brain function imaging system of the present invention converts the changes in blood oxygen concentration in the brain processing partition into a brain function spatial distribution map and presents it as a partition imaging result, thereby realizing targeted analysis of brain function through the function corresponding to each brain processing partition.

[0123] In an embodiment of the present invention, the brain function imaging result is obtained according to the partition imaging of all the brain processing partitions, including:

[0124] According to the function of each of the brain processing partitions, obtaining functional connections between the brain processing partitions;

[0125] The brain function imaging result is obtained according to the brain function spatial distribution map and the functional connection between the brain processing partitions.

[0126] In this embodiment, the distributed analysis module can calculate the functional connections between brain processing partitions by analyzing the functional characteristics of each brain processing partition. The functional connections represent the degree of interaction and communication between different brain regions, and can reveal the functional network and information transfer mechanism of the brain. The brain function spatial distribution map is then combined with the functional connections to generate brain functional imaging results. The brain functional imaging results can display the functional connection strength and pattern between different brain processing partitions, thereby providing an overall understanding of the brain functional network.

[0127] The near-infrared brain function imaging system of the present invention can reflect the functional relationship and interaction mode between different brain regions by calculating the functional connection between brain processing partitions, and provide analysis of brain function network. Combining the brain function spatial distribution map and functional connection, the distributed analysis module can generate brain function imaging results, showing the functional connection between brain processing partitions, which is helpful to understand the structure and functional organization of brain function network.

[0128] Combination Figure 2 As shown, in the embodiment of the present invention, the near-infrared brain function imaging system further includes: a light source module; the light source module is used to: emit the near-infrared light to the actual brain.

[0129] In this embodiment, the main function of the light source module is to emit near-infrared light to achieve optical excitation and detection of the brain. The light source can be composed of one or more near-infrared wavelength lasers or light-emitting diodes.

[0130] The light source module of the near-infrared brain function imaging system of the present invention can improve the stability, consistency and adjustability of the near-infrared light, thereby effectively improving the reliability and accuracy of the brain function imaging system.

[0131] Combination Figure 2 As shown, in the embodiment of the present invention, the near-infrared brain function imaging system further includes: a brain motion tracking module;

[0132] The brain motion tracking module is used to: obtain the position change of the actual brain in the real space;

[0133] The three-dimensional brain model is updated according to the posture change of the actual brain in the real space.

[0134] In this embodiment, the brain motion tracking module can use different sensors or technologies, such as inertial measurement units, photoelectric sensors or magnetic resonance imaging, to obtain brain movement information. The above sensors can measure the position, posture or posture changes of the brain in the real space. By collecting and processing these brain motion data in real time, the brain movement can be monitored and tracked, and the three-dimensional model of the brain can be updated according to the actual posture changes of the brain in the real space. Since the position and shape of the brain in space may change with the movement of the brain, the posture changes obtained by the brain motion tracking module are applied to the three-dimensional model of the brain, the model can be updated in real time to match the actual brain.

[0135] The near-infrared brain function imaging system of the present invention can maintain consistency between the model and the actual brain by updating the brain model in real time as the shape and position of the brain may change as the brain moves, thereby improving the accuracy of brain function imaging.

[0136] Combination Figure 3 As shown, the present invention also provides a near-infrared brain function imaging method, which is applied to any of the above-mentioned near-infrared brain function imaging systems, and the near-infrared brain function imaging method comprises:

[0137] S1: Acquire the transmitted light signal of near-infrared light;

[0138] S2: establishing a three-dimensional brain model of the actual brain according to the transmitted light signal;

[0139] S3: Dividing a plurality of brain processing partitions according to the three-dimensional brain model, each of the brain processing partitions corresponding to one or more brain functions;

[0140] S4: dividing the transmitted light signal according to the brain processing partitions to obtain partition light signals corresponding to each brain processing partition;

[0141] S5: obtaining the node corresponding to each of the brain processing partitions according to a preset node allocation rule, and sending the partition optical signal of the brain processing partition to the node;

[0142] S6: obtaining a change in blood oxygen concentration of the brain processing partition according to the partition light signal of the brain processing partition; and performing prediction according to the partition light signal to obtain an optimal light source parameter range of the brain processing partition;

[0143] S7: inputting the optimal light source parameters of the brain processing partition and the partition light signal of the brain processing partition into a light source adjustment strategy network, and obtaining the near-infrared light adjustment strategy of the brain processing partition corresponding to each node according to the output of the light source adjustment strategy network;

[0144] S8: according to the near-infrared light adjustment strategy, obtaining the wavelength and power of the near-infrared light and the irradiation time of the brain processing partition;

[0145] S9: adjusting the near infrared light according to the wavelength, the power and the irradiation time of the near infrared light;

[0146] S10: When the partition light signal of the brain processing partition is within the optimal light source parameter range, stop adjusting the near infrared light;

[0147] S11: obtaining a partition imaging of each brain processing partition according to the change of the blood oxygen concentration of each brain processing partition;

[0148] S12: Obtaining brain function imaging results according to the partition imaging of all the brain processing partitions;

[0149] S13: Displaying the brain function imaging result through a preset display.

[0150] The near-infrared brain function imaging method of the present invention performs three-dimensional modeling of the actual brain, divides the three-dimensional brain model into brain processing partitions, divides the acquired transmission light signal according to each brain processing partition, obtains the partition light signal corresponding to each brain processing partition, and then sends the partition light signal of each brain processing partition to the node corresponding to the distributed computing platform for processing, obtains the optimal light source parameters corresponding to each brain processing partition and the partition imaging corresponding to each brain processing partition, integrates all the partition imaging, obtains the brain function imaging result, and uses the light source adjustment module to adjust the near-infrared light according to the predicted optimal light source parameters, ensures that the partition light signal of each brain processing partition is within the ideal parameter range, thereby improving the imaging effect. The nodes of the distributed computing platform are used to process the partition light signals of the brain processing partitions in parallel, so that the entire brain function imaging task becomes a plurality of subtasks that can be processed in parallel, thereby improving the speed of data processing and analysis, and at the same time, provides accurate brain area division, so that the brain function imaging result is more accurate.

[0151] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0152] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0153] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A near-infrared brain function imaging system, characterized in that: The near-infrared brain function imaging system is applied to a distributed computing platform, and the near-infrared brain function imaging system includes: a three-dimensional processing module, a data acquisition module, a data transmission module, a data processing module, a light source adjustment module, a distributed analysis module and an imaging display module; the distributed computing platform includes a plurality of nodes, each of which corresponds to a mirror image of the data processing module, a mirror image of the light source adjustment module and a mirror image of the distributed analysis module; The data acquisition module is used to: obtain the transmission light signal of near-infrared light; The three-dimensional processing module is used to: establish a three-dimensional brain model of the actual brain according to the transmitted light signal; Dividing a plurality of brain processing partitions according to the three-dimensional brain model, each of the brain processing partitions corresponding to one or more brain functions; The data acquisition module is also used to: divide the transmitted light signal according to the brain processing partitions, and obtain a partition light signal corresponding to each brain processing partition; The data transmission module is used to: obtain the node corresponding to each brain processing partition according to a preset node allocation rule, and send the partition optical signal of the brain processing partition to the node; The data processing module is used to: obtain the blood oxygen concentration change of the brain processing partition according to the partition light signal of the brain processing partition; and perform prediction according to the partition light signal to obtain the optimal light source parameter range of the brain processing partition; The light source adjustment module is used to: input the optimal light source parameters of the brain processing partition and the partition light signal of the brain processing partition into the light source adjustment strategy network, and obtain the near-infrared light adjustment strategy of the brain processing partition corresponding to each node according to the output of the light source adjustment strategy network; According to the near-infrared light adjustment strategy, the wavelength and power of the near-infrared light and the irradiation time of the brain processing partition are obtained; adjusting the near-infrared light according to the wavelength, the power and the irradiation time of the near-infrared light; When the partition light signal of the brain processing partition is within the optimal light source parameter range, stop adjusting the near infrared light; The distributed analysis module is used to obtain a partition imaging of each brain processing partition according to the change of the blood oxygen concentration of each brain processing partition; Obtaining brain function imaging results according to the partition imaging of all the brain processing partitions; The imaging display module is used to display the brain function imaging results through a preset display mode.

2. The near-infrared brain function imaging system according to claim 1, characterized in that: The three-dimensional processing module is specifically used for: Obtaining point cloud data of the actual brain according to the transmitted light signal; Obtaining a surface three-dimensional model of the actual brain according to the spatial position of the actual brain in real space and the point cloud data of the actual brain; According to the transmitted light signal, obtaining the internal optical parameter distribution of the actual brain by an inverse problem solving method; Obtaining an internal three-dimensional model of the actual brain according to the optical parameter distribution; The three-dimensional brain model of the actual brain is obtained according to the internal three-dimensional model and the surface three-dimensional model.

3. The near-infrared brain function imaging system according to claim 1, characterized in that: The data acquisition module is also specifically used for: According to the brain processing partition, obtaining the position of the brain processing partition in the three-dimensional brain model; According to the position of the transmitted light signal on the three-dimensional brain model, obtaining the transmitted light signal corresponding to the position; The transmitted light signal corresponding to the position is used as the partitioned light signal.

4. The near-infrared brain function imaging system according to claim 1, characterized in that: The data transmission module is specifically used for: According to the brain function of each of the brain processing partitions, obtaining a functional connection weight of two of the brain processing partitions; Allocating the two brain processing partitions whose functional connection weights are greater than a preset threshold to two adjacent nodes; The partitioned optical signal of the brain processing partition is sent to the node through data communication technology.

5. The near-infrared brain function imaging system according to claim 1, characterized in that: The data processing module is specifically used for: Obtaining a light intensity change of the partitioned light signal according to the partitioned light signal of the brain processing partition; Obtaining a change in optical density of the partitioned optical signal according to the change in light intensity; According to the optical density change, the blood oxygen concentration change of the brain processing partition is obtained through a spectral change model.

6. The near-infrared brain function imaging system according to claim 1, characterized in that: The distributed analysis module is specifically used for: By means of the node, the change of the blood oxygen concentration of the brain processing partition corresponding to the node is mapped to the position of the brain processing partition in the three-dimensional brain model to obtain the time series data of the brain processing partition; Obtaining a brain function spatial distribution map of the brain processing partition according to the time series data; The brain function spatial distribution map is imaged as the partition of the brain processing partition.

7. The near-infrared brain function imaging system according to claim 6, characterized in that: The brain function imaging result is obtained according to the partition imaging of all the brain processing partitions, including: According to the function of each of the brain processing partitions, obtaining functional connections between the brain processing partitions; The brain function imaging result is obtained according to the brain function spatial distribution map and the functional connection between the brain processing partitions.

8. The near-infrared brain function imaging system according to claim 1, characterized in that: Also includes a light source module; The light source module is used to emit the near-infrared light to the actual brain.

9. The near-infrared brain function imaging system according to claim 1, characterized in that: The near-infrared brain function imaging system also includes a brain motion tracking module; The brain motion tracking module is used to: obtain the position change of the actual brain in the real space; The three-dimensional brain model is updated according to the posture change of the actual brain in the real space.

10. A near-infrared brain function imaging method, characterized in that: Applied to the near-infrared brain function imaging system according to any one of claims 1 to 9, the near-infrared brain function imaging method comprises: Acquiring a transmission light signal of near-infrared light; establishing a three-dimensional brain model of the actual brain according to the transmitted light signal; Dividing a plurality of brain processing partitions according to the three-dimensional brain model, each of the brain processing partitions corresponding to one or more brain functions; According to the brain processing partitions, the transmitted light signal is segmented according to the brain processing partitions to obtain a partition light signal corresponding to each brain processing partition; According to a preset node allocation rule, a node corresponding to each of the brain processing partitions is obtained, and the partition optical signal of the brain processing partition is sent to the node; Obtaining a change in blood oxygen concentration of the brain processing partition according to the partition light signal of the brain processing partition; and performing a prediction according to the partition light signal to obtain an optimal light source parameter range of the brain processing partition; Inputting the optimal light source parameters of the brain processing partition and the partition light signal of the brain processing partition into a light source adjustment strategy network, and obtaining the near-infrared light adjustment strategy of the brain processing partition corresponding to each node according to the output of the light source adjustment strategy network; According to the near-infrared light adjustment strategy, the wavelength and power of the near-infrared light and the irradiation time of the brain processing partition are obtained; adjusting the near-infrared light according to the wavelength, the power and the irradiation time of the near-infrared light; When the partition light signal of the brain processing partition is within the optimal light source parameter range, stop adjusting the near infrared light; Obtaining a partition imaging of each brain processing partition according to the change of the blood oxygen concentration of each brain processing partition; Obtaining brain function imaging results according to the partition imaging of all the brain processing partitions; The brain function imaging result is displayed through a preset display.

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