Method and device for analyzing near-infrared data of subjects of different ages

Abstract

The application relates to a method and device for analyzing and processing near-infrared data of subjects of different ages, the method comprising: acquiring physical coordinate points of acquisition probes and acquisition channels used for collecting the near-infrared data of the subjects, and three-dimensional standard head models and three-dimensional standard brain models, mapping each of the physical coordinate points to each of the acquired three-dimensional standard head models to obtain head model registration points; determining a brain model point closest to the head model registration point of the physical coordinate point as a brain model registration point of the corresponding acquisition probe or acquisition channel on the acquired three-dimensional standard brain model; and determining a brain partition corresponding to each of the acquisition probes and acquisition channels based on the brain partition model and the brain model registration point on the three-dimensional standard brain model corresponding to the age of the subject, so that the brain partitions corresponding to the acquisition probes and acquisition channels on the three-dimensional standard head models and brain models can be accurately obtained for subjects of different ages.

Description

Technical Field

[0001] This application relates to the field of near-infrared brain functional imaging technology, and more specifically, to methods and apparatus for analyzing and processing near-infrared data of subjects of different ages. Background Technology

[0002] Near-infrared functional brain imaging (fNIRS) is a non-invasive brain imaging technique that obtains information related to brain function by measuring light transmission and scattering in brain tissue. fNIRS is widely used in neuroscience, clinical medicine, and cognitive psychology, providing researchers with opportunities to gain a deeper understanding of brain function.

[0003] Currently, data processing software is typically required to process the acquired near-infrared (NIIR) data. For example, the positions of the acquisition probes and / or acquisition channels in the data can be registered to a brain model space to achieve localization visualization, and then the NIIR data is presented on the brain model. Most existing data processing software uses a single standard brain model; that is, both the brain model used for registering the acquisition probes and / or acquisition channels and the brain model used to present the NIIR data are single standard brain models. Furthermore, the registration results do not output the brain regions corresponding to the acquisition probes and / or acquisition channels. However, the brains of subjects of different ages differ, especially between children and adults. If existing standard brain models are used for registration and presentation of NIIR data, subjects of different ages can only have their results displayed on a standard brain model of a fixed age group. This may lead to inaccurate registration results and a mismatch between the position of the NIIR data presented on the brain model and the actual detection position corresponding to the NIIR data. In addition, if the brain regions corresponding to the acquisition probes and / or acquisition channels are not determined, it will affect the doctor's diagnosis and subsequent treatment. Summary of the Invention

[0004] This application addresses the aforementioned deficiencies in the prior art. It provides a method and apparatus for analyzing and processing near-infrared data from subjects of different age groups. This method and apparatus can register near-infrared data from subjects of different age groups with corresponding age-appropriate head and brain models. Furthermore, it can utilize the brain region model corresponding to the subject's age group to obtain the position of the acquisition probe and / or acquisition channel on the corresponding age-appropriate brain model and the corresponding brain region, thereby improving the accuracy of the registration results. This allows the subsequent presentation of near-infrared data on the brain model to more closely approximate the actual detection position corresponding to the near-infrared data.

[0005] According to the first aspect of this application, a method for analyzing and processing near-infrared data of subjects of different age groups is provided, comprising the following steps: acquiring the physical coordinate points of the acquisition probe and / or acquisition channel used to acquire the near-infrared data of the subject; acquiring a three-dimensional standard head model and a three-dimensional standard brain model corresponding to the age group of the subject; mapping each of the physical coordinate points onto the acquired three-dimensional standard head model to obtain the head model registration point of the physical coordinate points on the three-dimensional standard head model; determining the brain model point closest to the head model registration point of the physical coordinate points as the corresponding brain model registration point of the acquisition probe and / or acquisition channel on the acquired three-dimensional standard brain model; and determining the brain region corresponding to each acquisition probe and / or acquisition channel based on the brain model registration points of each acquisition probe or acquisition channel on the three-dimensional standard brain model of the subject's age group, combined with the brain region model of the subject's age group.

[0006] According to a second aspect of this application, an analysis and processing apparatus for near-infrared data of subjects of different age groups is provided, including an interface and a processor. The interface is configured to receive the physical coordinates of a data acquisition probe and / or acquisition channel used to acquire near-infrared data of the subject, as well as the near-infrared data of the subject. The processor is configured to execute the near-infrared data analysis and processing method described in any embodiment of this application.

[0007] According to a third aspect of this application, a non-transitory computer-readable medium is provided, on which instructions are stored, wherein when executed by a processor, the instructions perform the steps of the near-infrared data analysis and processing method described in any embodiment of this application.

[0008] The near-infrared data analysis and processing methods and apparatus for subjects of different age groups provided in various embodiments of this application, by mapping the physical coordinate points of the acquisition probe and / or acquisition channel onto a three-dimensional standard head model and a three-dimensional standard brain model matching the subject's age group, can obtain the position of the acquisition probe and / or acquisition channel on the three-dimensional standard brain model adapted to the subject's age group, as well as the brain model registration points, to achieve relatively accurate registration. Then, based on the obtained brain model registration points and combined with the brain region model corresponding to the subject's age group, the accurate brain region corresponding to the acquisition probe and / or acquisition channel can be obtained. That is, this application, in near-infrared data analysis and processing, can obtain the position of the acquisition probe and / or acquisition channel on the three-dimensional standard brain model adapted to the subject's age group, as well as the brain model registration points, to achieve relatively accurate registration. During external data processing, three-dimensional standard head models and three-dimensional standard brain models for different age groups are provided. At the same time, different brain region models are provided for different age groups. In this way, the position of the acquisition probe and / or acquisition channel on the corresponding head model and brain model, as well as the corresponding brain region, can be accurately obtained for subjects of different ages. This allows the presentation position of near-infrared data on the brain model to be closer to the actual detection position corresponding to the near-infrared data. This enables doctors to combine the accurate presentation of near-infrared data on the brain model and the distribution of brain regions to analyze near-infrared data and make more accurate and suitable diagnostic and treatment plans for the subjects. Attached Figure Description

[0009] In drawings that are not necessarily drawn to scale, the same reference numerals may describe similar parts in different views. The same reference numerals with or without letter suffixes may indicate different instances of similar parts. The drawings generally illustrate various embodiments by way of example rather than limitation and are used, together with the description and claims, to illustrate the claimed embodiments. Where appropriate, the same reference numerals are used in all drawings to refer to the same or similar parts. Such embodiments are illustrative and not intended to be exhaustive or exclusive embodiments of the apparatus or method.

[0010] Figure 1 A flowchart illustrating a method for analyzing and processing near-infrared data of subjects of different age groups according to an embodiment of this application; Figure 2 A schematic diagram showing the registration result display interface of the acquisition probe and / or acquisition channel according to an embodiment of this application; Figure 3 A flowchart illustrating a first partitioning method according to an embodiment of this application is shown; Figure 4 A schematic diagram showing an interface for displaying unregistered brain activation levels according to this application; Figure 5 A schematic diagram showing the brain activation level display interface after registration according to this application; Figure 6 A schematic diagram of the structure of a near-infrared data analysis and processing device for subjects of different age groups according to an embodiment of this application is shown. Detailed Implementation

[0011] To enable those skilled in the art to better understand the technical solutions of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments of this application will be further described in detail below with reference to the accompanying drawings and specific examples, but these are not intended to limit the scope of this application.

[0012] The terms "first," "second," and similar words used in this application do not indicate any order, quantity, or importance, but are merely used for distinction. Words such as "including" or "comprising" mean that the element preceding the word encompasses the elements listed after it, and do not exclude the possibility of encompassing other elements as well. In this application, the arrows shown in the figures for each step are merely examples of the execution order, not limitations. The technical solution of this application is not limited to the execution order described in the embodiments. The steps in the execution order can be combined, broken down, or rearranged, as long as the logical relationship of the executed content is not affected.

[0013] All terms used in this application (including technical or scientific terms) have the same meaning as understood by one of ordinary skill in the art to which this application pertains, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant art, and not as idealized or highly formalized, unless expressly defined herein. Technologies and equipment known to one of ordinary skill in the art may not be discussed in detail, but where appropriate, such technologies and equipment should be considered part of the specification.

[0014] Figure 1 A flowchart illustrating a method for analyzing and processing near-infrared data of subjects of different age groups according to an embodiment of this application is provided. This analysis and processing method can be executed by a processor in a near-infrared data analysis and processing device. The method includes the following steps: In step 101, the physical coordinates of the acquisition probes and / or acquisition channels used to acquire the near-infrared data of the subject are obtained. Specifically, the near-infrared data of the subject can be acquired using a near-infrared brain functional imaging device. The near-infrared brain functional imaging device includes a detection headgear, on which multiple acquisition probes for transmitting and / or receiving near-infrared signals are mounted. Each of the multiple acquisition probes can be configured as a transmitting probe or a receiving probe, and each pair of paired acquisition probes forms an acquisition channel. In some embodiments, one transmitting probe may correspond to multiple receiving probes, or conversely, one receiving probe may correspond to multiple transmitting probes, the pairing relationship depending on the specific requirements of the probe placement, the brain functional region to be detected, etc. During near-infrared brain function testing, the physical coordinates of the acquisition probe and / or acquisition channel can be acquired using a positioning device. These physical coordinates can be understood as the mounting points of the acquisition probe and / or acquisition channel on the testing headgear when the subject is wearing it. Additionally, the physical coordinates of the acquisition channel can be calculated from the physical coordinates of the acquisition probe determined by the positioning device. The physical coordinates corresponding to the midpoint between the paired transmitting and receiving probes are used as the physical coordinates of the acquisition channel.

[0015] In step 102, three-dimensional standard head models and three-dimensional standard brain models for the subject's age group are acquired. The near-infrared data analysis and processing device stores three-dimensional standard head models and three-dimensional standard brain models for different age groups, and each age group has corresponding three-dimensional standard head models and three-dimensional standard brain models stored, with at least three-dimensional standard head models and three-dimensional standard brain models stored for children (under 18 years old) and adults (over 18 years old). Of course, it is also possible to store further subdivided age groups for children and adults, as well as corresponding three-dimensional standard head models and three-dimensional standard brain models for each age group. For example, the children's age group may include 0-1 years, 1-2 years, 2-3 years, etc. The age groups corresponding to the acquired three-dimensional standard head models and three-dimensional standard brain models all correspond to the subject's age group. For example, if the subject's age group is children, then three-dimensional standard head models and three-dimensional standard brain models for children's age group are acquired; and if the subject's age group is adults, then three-dimensional standard head models and three-dimensional standard brain models for adults' age group are acquired. The processor can acquire three-dimensional standard head models and three-dimensional standard brain models for various age groups. For example, it can select three-dimensional standard head models and three-dimensional standard brain models for the corresponding age group based on the user's selection; or the processor can automatically acquire three-dimensional standard head models and three-dimensional standard brain models for the corresponding age group of the subject by using the age information of the subject corresponding to the near-infrared data.

[0016] In step 103, each of the physical coordinate points is mapped onto the acquired three-dimensional standard head model to obtain the head model registration points of the physical coordinate points on the three-dimensional standard head model. The mapping method may include: selecting multiple standard reference positions, and mapping each of the physical coordinate points onto the acquired three-dimensional standard head model based on the relative positional relationship between the physical coordinate points and the standard reference positions. The standard reference positions may be the occipital protuberance, the central point, the root of the nose, the left mastoid process, and the right mastoid process.

[0017] In step 104, the brain model point closest to the head model registration point of the physical coordinate point is determined as the brain model registration point of the corresponding acquisition probe and / or acquisition channel on the acquired 3D standard brain model. Specifically, based on the coordinates of the head model registration point on the 3D standard head model and the coordinates of each brain model point on the acquired 3D standard brain model corresponding to the subject's age group, the distance from each brain model point on the 3D standard brain model to each head model registration point is calculated, and the brain model point closest to the head model registration point is used as the brain model registration point of the acquisition probe or acquisition channel on the acquired 3D standard brain model. In this way, each head model registration point corresponds to a nearest brain model point on the acquired 3D standard brain model, thereby determining the brain model registration point of each acquisition probe and / or acquisition channel on the 3D standard brain model. This allows for accurate determination of the corresponding position of the acquisition probe and / or acquisition channel on the 3D standard brain model.

[0018] In step 105, based on the registration points of each acquisition probe and / or acquisition channel on the three-dimensional standard brain model of the subject's age group, and in conjunction with the brain partition model of the subject's age group, the brain partition corresponding to each acquisition probe and / or acquisition channel is determined. Specifically, the near-infrared data analysis and processing device also stores brain partition models for different age groups, and for the same age group, it stores a three-dimensional standard brain model and a corresponding brain partition model. When determining the brain partition of the acquisition probe and / or acquisition channel on the acquired three-dimensional standard brain model, it is necessary to retrieve the brain partition model for the corresponding age group to obtain a more accurate brain partition corresponding to each acquisition probe and / or acquisition channel on the three-dimensional standard brain model. The brain partition model is associated with the three-dimensional standard brain model. For example, the brain partition model may include the corresponding three-dimensional standard brain model and the brain partition information where each brain point on the three-dimensional standard brain model is located. Alternatively, the brain partition model can be deformed to obtain the three-dimensional standard brain model, and the brain partition model contains brain partition information. Different brain partitioning models define different partitioning methods for the corresponding age group's 3D standard brain model. The correlation between the brain partitioning model and the 3D standard brain model and the partitioning method are not limited here, as long as the brain model points on the 3D standard brain model can obtain the corresponding brain partitioning information through the brain partitioning model.

[0019] Figure 2 This diagram illustrates the display interface for the registration results of the acquisition probe and / or acquisition channel according to an embodiment of this application. Figure 2 As shown, users can select the age range corresponding to the subject's age on the registration result display interface, thereby presenting a 3D standard brain model for that age range. If registration has already been performed for the currently processed near-infrared data within the selected age range, the acquisition probe and / or acquisition channel can be displayed at the registration position on the 3D standard brain model. Furthermore, different colors can be used to distinguish between the transmitting probe, receiving probe, and acquisition channel, and different markers can be used to represent different receiving probes and acquisition channels. Figure 2 The three-dimensional standard brain model can be rotated or displayed at different angles to show the position of all acquisition probes on the three-dimensional standard brain model. This helps doctors to intuitively observe the distribution of each acquisition probe on the three-dimensional standard brain model.

[0020] In this application, by mapping the physical coordinates of the acquisition probe and / or acquisition channel onto a three-dimensional standard head model and a three-dimensional standard brain model that match the age group of the examinee, the position of the acquisition probe and / or acquisition channel on the three-dimensional standard brain model adapted to the examinee's age group and the brain model registration points can be obtained, thus achieving more accurate registration. Then, based on the obtained brain model registration points and the brain region model corresponding to the examinee's age group, the accurate brain region corresponding to the acquisition probe and / or acquisition channel can be obtained. That is, when processing near-infrared data, this application provides three-dimensional standard head models and three-dimensional standard brain models for different age groups, and provides different brain region models for different age groups. In this way, the position of the acquisition probe and / or acquisition channel on the corresponding head model and brain model and the corresponding brain region can be accurately obtained for examinees of different age groups. This allows the presentation position of the near-infrared data on the brain model to be closer to the actual detection position corresponding to the near-infrared data. This enables doctors to analyze the near-infrared data by combining the accurate presentation of the near-infrared data on the brain model and the brain region distribution, and make more accurate and suitable diagnostic and treatment plans for the examinee.

[0021] In some embodiments, the brain partitioning model for adult age groups is a first brain partitioning model that defines a first partitioning method for a three-dimensional standard brain model of adult age groups. Determining the brain partitions corresponding to each acquisition probe and / or acquisition channel specifically includes: determining one or more brain partitions corresponding to each acquisition probe and / or acquisition channel through the first partitioning method, and outputting the corresponding brain partition and its proportion for each acquisition probe or acquisition channel, and the sum of the proportions of each brain partition is 1.

[0022] The adult-age 3D standard brain model can be a model composed of multiple sheet-like units (e.g., triangular facets), with the vertices of each sheet-like unit serving as brain model points. The first brain region model for the adult age group can be understood as including the adult-age 3D standard brain model and the brain regions corresponding to each brain model point on the 3D standard brain model. That is, the first brain region model for the corresponding adult age group is composed of multiple sheet-like units identical to those in the corresponding 3D standard brain model, and each brain region model point in the first brain region model has a corresponding brain region. The brain region model points correspond to the brain model points on the 3D standard brain model, thus obtaining the brain regions of the brain model points on the 3D standard brain model. Both brain model points and brain region model points are vertices of sheet-like units; for example, when the sheet-like unit is a triangular facet, the brain model points and brain region model points are the vertices of the triangular facet. The first region method can include determining the brain regions corresponding to each acquisition probe and / or acquisition channel based on the correlation between the adult-age 3D standard brain model and the first brain region model, using an index associated with the coordinates of each brain region model point in the first brain region model and the index corresponding to the brain region. For example, the corresponding index can be determined based on the coordinates of the brain partition model points, and then the brain partition corresponding to the brain partition model points can be obtained through the index.

[0023] Since both the acquisition probe and the acquisition channel have a certain acquisition range and do not form only one acquisition point, theoretically, there should be multiple brain model registration points for each acquisition probe and acquisition channel on the three-dimensional standard brain model. Based on this, in order to improve the accuracy of the brain region where the acquisition probe and / or acquisition channel are located, the first partitioning method is used to determine that each acquisition probe and / or acquisition channel corresponds to one or more brain partitions. For each acquisition probe or acquisition channel, the corresponding brain partition and its proportion are output, and the sum of the proportions of each brain partition is 1.

[0024] Figure 3 A flowchart illustrating a first partitioning method according to an embodiment of this application is shown. Determining one or more brain partitions corresponding to each acquisition probe and / or acquisition channel using the first partitioning method specifically includes the following steps: In step 301, a brain partition model point on the first brain partition model that has the same coordinates as the registration points of each brain model is used as a base point. Taking a first brain partition model composed of triangular facets as an example, step 301 involves matching brain partition model points that have the same coordinates as the registration points of each brain model.

[0025] In step 302, for each base point, brain region model points within a preset distance range from the base point are searched on the first brain region model to form a base point range corresponding to each acquisition probe or acquisition channel. Each base point range includes the brain region model points searched for at the corresponding base point. Since both the acquisition probe and acquisition channel have a certain acquisition range, multiple brain model registration points corresponding to the acquisition range should exist on the three-dimensional standard brain model for each acquisition probe and acquisition channel. To reduce the computational load, when registering the acquisition probe and / or acquisition channel on the three-dimensional standard brain model, a physical coordinate point can be determined based on one acquisition probe and / or acquisition channel. That is, a physical coordinate point of the acquisition probe and / or acquisition channel used to acquire the near-infrared data of the subject is obtained, instead of multiple physical coordinate points of an acquisition range. Registration is performed on a single physical coordinate point to obtain a brain model registration point corresponding to the acquisition probe and / or acquisition channel, instead of performing registration operations on multiple physical coordinate points of the acquisition range. To improve the accuracy of determining the brain regions corresponding to the acquisition probe and / or acquisition channel, taking the determination of the brain region where an acquisition probe is located as an example, after obtaining a brain model registration point for the acquisition probe, a brain region model point with the same coordinates as the registration point is determined on the first brain region model as a base point. Brain region model points within a preset distance range from the base point are then acquired. The acquired brain region model points and the base point together form the base point range, which corresponds to the acquisition range of the acquisition probe. The preset distance can be set according to the characteristics of the acquisition probe and / or acquisition channel to accurately obtain the base point range.

[0026] In step 303, the brain regions corresponding to each brain region model point within the base point range on the first brain region model are used as the brain regions of the acquisition probe or acquisition channel corresponding to the base point range. For example, through the first partitioning method, the brain region model points within the base point range include points A, B, C, and D, where point A within the base point range corresponds to the middle frontal gyrus brain region, point B corresponds to the middle frontal gyrus brain region, point C corresponds to the superior frontal gyrus brain region, and point D corresponds to the superior frontal gyrus brain region. That is, the middle frontal gyrus and the superior frontal gyrus are used as the brain regions of the acquisition probe or acquisition channel corresponding to this base point range.

[0027] In step 304, based on the ratio of the number of brain region model points in each brain region within the base point range to the total number of brain region model points in the base point range, the proportion of each brain region corresponding to the acquisition probe or acquisition channel corresponding to the base point range is obtained. For example, in step 303, a total of 4 brain region model points can be obtained within the base point range, with 50% of the brain region model points located in the middle frontal gyrus and 50% in the superior frontal gyrus. Therefore, the proportion of the brain region corresponding to the acquisition probe or acquisition channel within this base point range is 50% in the middle frontal gyrus and 50% in the superior frontal gyrus. This provides a more detailed and accurate distribution of brain regions for each acquisition probe and / or acquisition channel, enabling doctors to more accurately analyze the patient's condition based on near-infrared data.

[0028] In some embodiments, the near-infrared data analysis and processing method further includes: the brain partition model of the child's age group is a second brain partition model, the second brain partition model is deformed to obtain a three-dimensional standard brain model of the child's age group and a second partitioning method is defined, and the determination of the brain partition corresponding to each acquisition probe and / or acquisition channel specifically includes: determining the individual brain partition corresponding to each acquisition probe and / or acquisition channel through the second partitioning method.

[0029] The three-dimensional standard brain model for children's age group can be a model composed of multiple patch units and multiple patch base points. The second brain region model for children's age group includes a model composed of multiple voxel units (i.e., basic units), and the three-dimensional standard brain model for children's age group is obtained by transforming the second brain region model. For example, the second brain region model for children's age group can be transformed and affinely transformed to obtain the three-dimensional standard brain model for children's age group in the MNI coordinate system. The model transformation can be understood as converting the positions of voxels in the first brain region model into position points. In addition, the second brain region model also includes the brain regions corresponding to each voxel unit. The second region method includes determining the corresponding brain regions of the second brain region model based on the correlation between the three-dimensional standard brain model and the second brain region model for children's age group, based on the brain model registration points, and determining the corresponding voxels of the second brain region model and the brain regions corresponding to the voxels. The second brain partition model corresponding to children's age group differs from the first brain partition model corresponding to adults' age group. Since voxels occupy a certain spatial range, and to reduce computational load, the second partitioning method determines the brain partition corresponding to each acquisition probe and / or acquisition channel as a single brain region corresponding to a single voxel, which meets the requirements. This second partitioning method facilitates accurate brain partitioning of the brain model registration points on the 3D standard brain model for children's age group and improves computational efficiency.

[0030] In some embodiments, determining the individual brain region corresponding to each acquisition probe and / or acquisition channel through the second partitioning method specifically includes the following steps: Transforming the coordinates of the brain model registration points to form reference coordinates; forming pre-selected coordinates by identifying the positions of each basic unit constituting the second brain region model; determining the coordinates closest to the reference coordinates among the pre-selected coordinates as index coordinates; and identifying the brain region corresponding to the basic unit corresponding to the index coordinate as the brain region corresponding to the acquisition probe or acquisition channel. Transforming the coordinates of the brain model registration points to form reference coordinates can be understood as performing an inverse affine transformation on the coordinates of the brain model registration points to obtain the coordinates before the affine transformation, which are then used as reference coordinates. Taking a second brain region model composed of voxels as an example, the positions corresponding to each voxel form pre-selected coordinates; for example, the coordinates of the voxel center point can be used as pre-selected coordinates. Because the reference coordinates and the pre-selected coordinates formed by the voxel positions after the model transformation, affine transformation, and inverse affine transformation of the second brain partition model may have some differences and are not completely corresponding, the coordinates closest to the reference coordinates among the pre-selected coordinates are determined as index coordinates to minimize the differences caused by the above deformation and transformation. The brain partition corresponding to the basic unit of the index coordinate is taken as the brain partition corresponding to the acquisition probe or acquisition channel. Through the second partition method, the voxel corresponding to the acquisition probe and / or acquisition channel on the second brain partition model can be obtained, thereby determining the brain partition where the corresponding voxel is located, and thus obtaining the brain partition corresponding to the acquisition probe or acquisition channel. Since the second brain partition model is a model composed of multiple voxels, and each voxel occupies a certain space, and the three-dimensional standard brain model for children's age group is obtained by a series of deformations of the second brain partition model, and inverse deformation is required to obtain the brain partition corresponding to the acquisition probe or acquisition channel, the brain region corresponding to the single voxel of the acquisition probe and / or acquisition channel on the second brain partition model can be determined as its brain partition. This can obtain the brain partition corresponding to the acquisition probe and / or acquisition channel more accurately, and at the same time improve the calculation speed.

[0031] In some embodiments, the brain model point closest to the head model registration point of the physical coordinate point is determined as the brain model registration point of the corresponding acquisition probe or acquisition channel on the three-dimensional standard brain model. This specifically includes the following steps: A first coordinate matrix is ​​formed based on the coordinates of all brain model points on the three-dimensional standard brain model. The coordinates of each head model registration point are copied to form a second coordinate matrix with the number of brain model points on the three-dimensional standard brain model as the number of rows, and each row containing the coordinates of the head model registration point. For example, let 'a' represent the data x1, y1, z1 contained in the three-dimensional coordinates of the head model registration point. The second coordinate matrix can be represented as: .

[0032] The coordinates of the multiple head model registration points are copied according to the number of brain model points, and multiple second matrices are formed. The difference between the first coordinate matrix and each second coordinate matrix is ​​calculated to form multiple difference matrices. The brain model point corresponding to the minimum value of the sum of squares of the row data in the difference matrix obtained by the following formula (1) is the brain model point closest to the head model registration point. The formula (1) is as follows: (1) Where x, y, z are the row data in the difference matrix, and A is the sum of squares of the row data.

[0033] Since the number of rows in the second coordinate matrix is ​​the same as the number of brain model points in the first coordinate matrix, the difference between the head model registration point and each brain model point can be obtained. Each difference in the difference matrix corresponds to a brain model point, thus the brain model point closest to the head model registration point can be obtained through formula (1). Compared to calculating the difference between the head model registration point and all brain model points on the three-dimensional standard brain model separately to obtain the brain model registration point, this application adopts the method of forming a matrix with the same number of rows between the head model registration point and the brain model points on the three-dimensional standard brain model before performing matrix difference calculation, which is faster and improves the calculation speed.

[0034] In some embodiments, the analysis and processing method further includes, on the brain activation level display interface, in response to the selection of an age group, determining whether the acquisition probe and acquisition channel corresponding to the current near-infrared data have determined the brain model registration point based on the currently selected age group. If the acquisition probe and acquisition channel corresponding to the current near-infrared data have not determined the brain model registration point based on the currently selected age group, the first area of ​​the brain activation level display interface does not display a three-dimensional brain model or only displays a three-dimensional brain model without activation level information, while the second area displays a two-dimensional activation map showing the dynamic change of blood oxygen concentration over time. If the acquisition probe and acquisition channel corresponding to the current near-infrared data have determined the brain model registration point based on the currently selected age group, then the first area of ​​the brain activation level display interface displays a three-dimensional brain model with activation level information, while the second area displays a two-dimensional activation map showing the dynamic change of blood oxygen concentration over time. Specifically, the determination can be based on the current near-infrared data and the user's selection of an age group to determine whether there is registration result information corresponding to the near-infrared data within the selected age group, and then the display mode of the first area on the brain activation level display interface can be adaptively changed according to the presence or absence of registration result information.

[0035] like Figure 4As shown, if the acquisition probe and acquisition channel corresponding to the current near-infrared data have not determined the brain model registration point based on the currently selected age group, the first area of ​​the brain activation level display interface will not display the three-dimensional brain model or will only display a three-dimensional brain model without activation level information. At the same time, the second area will display a two-dimensional activation map showing the dynamic change of blood oxygen concentration over time. In this way, by comparing the content displayed in the first and second areas, for example, the second area normally displays the two-dimensional activation map showing the change of blood oxygen concentration over time, so that the user knows that the reason why the three-dimensional brain model is not displayed in the first area or only a three-dimensional brain model without activation level information is because the acquisition probe and acquisition channel have not been registered, rather than because there is an abnormality in the acquired near-infrared data. This reminds the user to perform further registration operations on the acquisition probe and acquisition channel.

[0036] like Figure 5 As shown, users can select the corresponding age group of the 3D standard brain model on the brain activation level display interface based on the subject's age group. Once the acquisition probe and / or acquisition channel have obtained registration points on the 3D standard brain model for the selected age group, the corresponding activation level information is displayed at the corresponding position in the 3D brain model on the brain activation level display interface. This activation level information can be obtained from near-infrared blood oxygen concentration data, and different colors can be used to distinguish and display the corresponding activation level information. After obtaining the brain model registration points, the brain activation level display interface displays a 3D brain model with activation level information, allowing doctors to intuitively obtain comparisons of different brain regions. Furthermore, the 3D brain model can dynamically display changes in activation levels based on near-infrared data, allowing doctors to intuitively perceive changes in the activation levels of different brain regions. The two-dimensional activation map can be a dynamic diagram of the blood oxygen concentration obtained from each acquisition channel as a function of acquisition time. The horizontal axis of the two-dimensional activation map can be time, and the vertical axis can be the blood oxygen concentration value. When the two-dimensional activation map is displayed on the brain activation level display interface, doctors can intuitively see the changes in blood oxygen concentration over time, as well as the peak and trough values ​​of blood oxygen concentration. It can also be combined with the brain partitioning of the three-dimensional brain model to facilitate doctors to make a more comprehensive and accurate analysis.

[0037] in addition, Figure 4 and Figure 5 The three-dimensional brain model can also display small balls of acquisition probes and / or acquisition channels, so that users can see both the acquisition probes and / or acquisition channels, as well as the brain activation level of the brain regions corresponding to each acquisition probe and / or acquisition channel, in order to better analyze and process near-infrared data.

[0038] According to embodiments of this application, an analysis and processing device for near-infrared data of subjects of different ages is also provided. Figure 6This diagram illustrates the structure of a near-infrared data analysis and processing apparatus for subjects of different age groups according to an embodiment of this application. The analysis and processing apparatus 600 includes an interface 601 and a processor 602. The interface 601 is configured to acquire the physical coordinates of the acquisition probe and / or acquisition channel for near-infrared data of the subject, as well as the near-infrared data of the subject. The processor 602 is configured to execute the near-infrared data analysis and processing method described in any embodiment of this application.

[0039] This application maps the physical coordinates of the acquisition probe and / or acquisition channel onto a three-dimensional standard head model and a three-dimensional standard brain model that match the age group of the examinee. This allows for obtaining the position of the acquisition probe and / or acquisition channel on the three-dimensional standard brain model that is appropriate for the examinee's age group, as well as the brain model registration points, achieving more accurate registration. Then, based on the obtained brain model registration points and the brain region model corresponding to the examinee's age group, the accurate brain region corresponding to the acquisition probe and / or acquisition channel can be obtained. In other words, this application provides three-dimensional standard head models and three-dimensional standard brain models for different age groups during near-infrared data processing, and also provides different brain region models for different age groups. This ensures that the position of the acquisition probe and / or acquisition channel on the corresponding head model and brain model, as well as the corresponding brain region, can be accurately obtained for examinees of different age groups. This allows the presentation of near-infrared data on the brain model to be closer to the actual detection position corresponding to the near-infrared data. This enables doctors to analyze near-infrared data by combining the accurate presentation of near-infrared data on the brain model and the brain region distribution, making more precise and suitable diagnostic and treatment plans for the examinee.

[0040] The processor 602 may be a processing device that includes one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor 602 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 that runs other instruction sets, or a processor that runs a combination of instruction sets. The processor 602 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), etc. The processor 602 may be included within the analysis and processing device 600, or it may be located outside the analysis and processing device 600, but cooperate with the analysis and processing device 600 to perform relevant data analysis and processing.

[0041] According to embodiments of this application, a non-transitory computer-readable medium is also provided, on which instructions are stored, wherein, when executed by a processor, the instructions perform the steps of the near-infrared data analysis and processing method described in any embodiment of this application. For subjects of different ages, accurate brain regions corresponding to the acquisition probe and / or acquisition channel on a three-dimensional standard brain model can be obtained. This facilitates accurate analysis by doctors of the subject's near-infrared data in conjunction with the related brain region distribution, enabling more precise and suitable diagnostic and treatment plans for the subject.

[0042] Furthermore, although exemplary embodiments have been described herein, their scope includes any and all embodiments based on this application that have equivalent elements, modifications, omissions, combinations (e.g., schemes involving intersections of various embodiments), adaptations, or alterations. Elements in the claims will be interpreted broadly based on the language used in the claims and are not limited to the examples described in this specification or during the implementation of this application, which will be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered illustrative only, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0043] The above description is intended to be illustrative and not restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. Other embodiments may be used by those skilled in the art upon reading the above description. Furthermore, in the above detailed description, various features may be grouped together to simplify the application. This should not be construed as an intention that a feature of an unclaimed application is necessary for any claim. Rather, the subject matter of this application may be less than all the features of an embodiment of a particular application. Thus, the following claims are incorporated herein by reference as examples or embodiments, wherein each claim is independently considered as a separate embodiment, and these embodiments are contemplated as being possible in various combinations or arrangements. The scope of the invention should be determined by reference to the appended claims and the full scope of their equivalents.

[0044] The above embodiments are merely exemplary embodiments of this application and are not intended to limit the present invention. The scope of protection of the present invention is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to the present invention within the spirit and scope of this application, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of the present invention.

Claims

1. A method for analyzing and processing near-infrared data from subjects of different age groups, characterized in that, include: Obtain the physical coordinates of the acquisition probe and / or acquisition channel used to acquire near-infrared data of the subject; Obtain three-dimensional standard head models and three-dimensional standard brain models for the age group of the subjects; Each of the physical coordinate points is mapped onto the acquired three-dimensional standard head model to obtain the head model registration points of the physical coordinate points on the three-dimensional standard head model; The brain model point closest to the head model registration point of the physical coordinate point is determined as the brain model registration point of the corresponding acquisition probe and / or acquisition channel on the acquired three-dimensional standard brain model; Based on the registration points of each acquisition probe and / or acquisition channel on the three-dimensional standard brain model of the subject's age group, and in conjunction with the brain region model of the subject's age group, the brain region corresponding to each acquisition probe and / or acquisition channel is determined.

2. The analysis and processing method according to claim 1, characterized in that, The brain partitioning model for adult age groups is a first brain partitioning model that defines a first partitioning method for a three-dimensional standard brain model of adult age groups. The first brain partitioning model corresponding to an adult age group is composed of multiple sheet-like units identical to the three-dimensional standard brain model of the corresponding age group. Determining the brain partition corresponding to each acquisition probe and / or acquisition channel specifically includes: determining one or more brain partitions corresponding to each acquisition probe and / or acquisition channel through the first partitioning method, and outputting the corresponding brain partition and its proportion for each acquisition probe or acquisition channel, and the sum of the proportions of each brain partition is 1.

3. The analysis and processing method according to claim 2, characterized in that, The method of determining one or more brain regions corresponding to each acquisition probe and / or acquisition channel through the first partitioning method specifically includes: taking the brain region model points on the first brain region model that have the same coordinates as the registration points of each brain model as the base points; For each base point, brain partition model points within a preset distance range from the base point are searched on the first brain partition model to form the base point range corresponding to each acquisition probe or acquisition channel. Each base point range includes the brain partition model points searched for by the corresponding base point. The brain regions corresponding to the brain region model points of each brain region model point in the base point range on the first brain region model are taken as the brain regions of the acquisition probe or acquisition channel corresponding to the base point range. Based on the ratio of the number of brain region model points in each brain region within the base point range to the total number of brain region model points within the base point range, the proportion of each brain region corresponding to the acquisition probe or acquisition channel corresponding to the base point range is obtained.

4. The analysis and processing method according to claim 1, characterized in that, The brain partitioning model for children's age group is a second brain partitioning model. The second brain partitioning model for children's age group is composed of multiple voxel units. The second brain partitioning model is transformed to obtain a three-dimensional standard brain model for children's age group and a second partitioning method is defined. Specifically, determining the brain partition corresponding to each acquisition probe and / or acquisition channel includes: determining the individual brain partition corresponding to each acquisition probe and / or acquisition channel through the second partitioning method.

5. The analysis and processing method according to claim 4, characterized in that, The second partitioning method for determining the individual brain partition corresponding to each acquisition probe and / or acquisition channel specifically includes: The coordinates of the registration points of the brain model are transformed by inverse transformation from the second brain region model corresponding to the child's age group to the three-dimensional standard brain model of the child's age group to obtain the reference coordinates in the second brain region model. The positions of each voxel unit constituting the second brain region model are then used to form pre-selected coordinates. The coordinates closest to the reference coordinates among the pre-selected coordinates are determined as index coordinates, and the brain region corresponding to the voxel unit corresponding to the index coordinate is taken as the brain region corresponding to the acquisition probe or acquisition channel.

6. The analysis and processing method according to claim 1, characterized in that, The brain model point closest to the head model registration point of the physical coordinate point is determined as the brain model registration point of the corresponding acquisition probe or acquisition channel on the three-dimensional standard brain model. Specifically, this includes: forming a first coordinate matrix based on the coordinates of all brain model points on the three-dimensional standard brain model; copying the coordinates of each head model registration point to form a second coordinate matrix with the number of brain model points on the three-dimensional standard brain model as the number of rows, and each row of data being the coordinates of the head model registration point; calculating the difference between the first coordinate matrix and each of the second coordinate matrices to form multiple difference matrices; the brain model point corresponding to the minimum value of the sum of squares of the row data in the difference matrix obtained by the following formula (1) is the brain model point closest to the head model registration point. The formula (1) is as follows: (1) Where x, y, z are the row data in the difference matrix, and A is the sum of squares of the row data.

7. The analysis and processing method according to claim 1, characterized in that, Also includes: On the brain activation level display interface, in response to the selection of age group, it is determined whether the acquisition probe and acquisition channel corresponding to the current near-infrared data have determined the brain model registration point based on the currently selected age group; If the acquisition probe and acquisition channel corresponding to the current near-infrared data have not determined the brain model registration point based on the currently selected age group, the first area of ​​the brain activation level display interface will not display the three-dimensional brain model or will only display the three-dimensional brain model without activation level information, while the second area will display the two-dimensional activation map of blood oxygen concentration changing dynamically over time. If the acquisition probe and acquisition channel corresponding to the current near-infrared data have already determined the brain model registration point based on the currently selected age group, then a three-dimensional brain model with activation level information is displayed in the first area of ​​the brain activation level display interface, while a two-dimensional activation map showing the dynamic change of blood oxygen concentration over time is displayed in the second area.

8. A device for analyzing and processing near-infrared data from subjects of different age groups, characterized in that, include: An interface configured to receive the physical coordinates of the acquisition probe and / or acquisition channel used to acquire near-infrared data of the subject, as well as the near-infrared data of the subject; A processor configured to perform the near-infrared data analysis and processing method as described in any one of claims 1-7.

9. A non-transitory computer-readable medium having instructions stored thereon, wherein, when executed by a processor, the instructions perform the steps of the near-infrared data analysis and processing method as described in any one of claims 1-7.

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