CN116807414B - Assessment method and device for near infrared brain function imaging signal quality - Google Patents
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Abstract
The application provides an evaluation method and device for near infrared brain function imaging signal quality, wherein the evaluation method comprises the following steps: acquiring at least one piece of near infrared data of a subject; determining signal quality detection parameters of the detection channels corresponding to the near infrared data based on the near infrared data; determining a block corresponding to the signal quality detection parameter according to the signal quality detection parameter; generating a signal detection quality map based on the image block and the sequence of detection channels corresponding to the image block, so as to evaluate the signal quality of each detection channel through the signal detection quality map; wherein, the signal detection quality map is associated with identification information and a block for distinguishing near infrared data. The evaluation method enables a user to intuitively judge the signal quality level of each detection channel of the detected person through different image block distribution by using the signal detection quality map, and effectively improves the efficiency of signal quality evaluation.
Description
Technical Field
The application belongs to the technical field of near-infrared brain function imaging, and particularly relates to a method and a device for evaluating near-infrared brain function imaging signal quality.
Background
The quality of near infrared data acquired in the near infrared brain function imaging technology has important significance for mining and reading data results, for example, the quality of near infrared data can directly influence the evaluation of brain activation conditions of a subject.
The evaluation function of near infrared data quality in the prior art is mostly integrated in data analysis software. The collected near infrared data is subjected to signal quality evaluation in data analysis software, a change curve of light intensity of each channel of a single subject along with detection time can be formed, a user can judge signal quality according to fluctuation conditions of the change curve, for example, the fluctuation amplitude of the signal is larger in a certain detection time, and the user can generally estimate that the signal quality of the section is poor. However, the curve fluctuation condition is affected by the setting of the light intensity coordinate range, and thus the accuracy of the quality evaluation method is low. For example, if the light intensity coordinate range is set large, even though a light intensity curve with poor signal quality may not be so pronounced in the resulting presentation. The user is difficult to determine the set size of the light intensity coordinate range, so that it is difficult to intuitively and accurately judge which channel signal quality level of the detected person is poor only through fluctuation of the light intensity change curve, and each detection channel of each detected person forms a light intensity curve.
Disclosure of Invention
Aiming at the technical problems in the prior art, the application provides an evaluation method and device for near-infrared brain function imaging signal quality, which can solve the technical problems that in the prior art, the accuracy of judging the signal quality of a detection channel is low, and a user is difficult to intuitively judge the channel signal quality level of a detected person according to a large number of curves.
In a first aspect, embodiments of the present application provide an evaluation method for near infrared brain function imaging signal quality, including steps S101 to S104. Step S101: at least one piece of near infrared data of the subject is acquired. Step S102: and determining signal quality detection parameters of the detection channels corresponding to the near infrared data based on the near infrared data. Step S103: and determining a block corresponding to the signal quality detection parameter according to the signal quality detection parameter. Step S104: generating a signal detection quality map based on the pattern block and the sequence of detection channels corresponding to the pattern block to evaluate the signal quality of each detection channel through the signal detection quality map; wherein the signal detection quality map presents identification information distinguishing the near infrared data and the tile in association.
In a second aspect, an embodiment of the present application provides an evaluation device for near-infrared brain function imaging signal quality, including an acquisition module, a determination module, and a generation module. The acquisition module is configured to acquire at least one piece of near infrared data of the subject. The determining module is configured to determine signal quality detection parameters of the detection channels corresponding to the near infrared data based on the near infrared data; and determining a block corresponding to the signal quality detection parameter according to the signal quality detection parameter. The generation module is configured to generate a signal detection quality map based on the tile and the sequence of detection channels corresponding to the tile to evaluate signal quality of each detection channel by the signal detection quality map; wherein the signal detection quality map presents identification information distinguishing the near infrared data and the tile in association.
In a third aspect, an embodiment of the present application provides a near-infrared brain function imaging signal quality evaluation system, including the above-mentioned evaluation device for near-infrared brain function imaging signal quality.
In a fourth aspect, embodiments of the present application provide a storage medium storing a computer program which, when executed by a processor, implements the steps of the above-described assessment method for near-infrared brain function imaging signal quality.
Compared with the prior art, the beneficial effects of the embodiment of the application are that: according to the method and the device, the signal quality detection parameters capable of representing the signal quality are obtained through calculation according to the near infrared data, the image blocks corresponding to the signal quality detection parameters are determined according to the signal quality detection parameters, and the signal quality map is generated based on the image blocks and the sequences of the detection channels, wherein different image blocks can represent different signal qualities instead of only checking the light intensity change curve graph of each detection channel through naked eyes, so that a user can intuitively judge the signal quality level of each detection channel of a detected person through the distribution of the image blocks through the signal quality map, the accuracy and the efficiency of signal quality evaluation and screening are improved, in addition, the signal quality map is generated based on the image blocks and the sequences of the detection channels corresponding to the image blocks, the sequences of the image blocks are consistent with the sequences of the detection channels, and accordingly the signal quality of the detection channels can be rapidly positioned, the signal quality of the detection channels can be conveniently processed in a follow-up mode, the signal quality of each detection channel can be seen by a single person, the signal quality of each detection channel can also be intuitively judged for a plurality of pieces of data of single person or each near infrared quality detection channel of near infrared data acquired by a plurality of times, the near infrared data can be visually judged on the quality of the image blocks, and the quality of the signal quality of the near infrared data can be effectively estimated through the image blocks, and the quality of the near infrared data can be effectively estimated, and the quality of the signal can be effectively estimated by the near infrared data.
Drawings
In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. The accompanying drawings illustrate various embodiments by way of example in general and not by way of limitation, and together with the description and claims serve to explain the disclosed embodiments. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. Such embodiments are illustrative and not intended to be exhaustive or exclusive of the present apparatus or method.
Fig. 1 is a first flowchart of an evaluation method for near infrared brain function imaging signal quality according to an embodiment of the present application.
Fig. 2 is a second flowchart of an evaluation method for near infrared brain function imaging signal quality according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a signal detection quality map of an evaluation method for near infrared brain function imaging signal quality according to an embodiment of the present application.
Fig. 4 is a third flowchart of an evaluation method for near infrared brain function imaging signal quality according to an embodiment of the present application.
Fig. 5 is a schematic diagram of a probe arrangement of a near infrared brain function imaging device.
Fig. 6 is a fourth flowchart of an evaluation method for near infrared brain function imaging signal quality according to an embodiment of the present application.
Fig. 7 is a fifth flowchart of an evaluation method for near infrared brain function imaging signal quality according to an embodiment of the present application.
Fig. 8 is a block diagram of a device for evaluating near infrared brain function imaging signal quality according to an embodiment of the present application.
Detailed Description
Various aspects and features of the present application are described herein with reference to the accompanying drawings.
It should be understood that various modifications may be made to the embodiments of the invention herein. Therefore, the above description should not be taken as limiting, but merely as exemplification of the embodiments. Other modifications within the scope and spirit of this application will occur to those skilled in the art.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and, together with a general description of the application given above and the detailed description of the embodiments given below, serve to explain the principles of the application.
These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It is also to be understood that, although the present application has been described with reference to some specific examples, those skilled in the art can certainly realize many other equivalent forms of the present application.
The foregoing and other aspects, features, and advantages of the present application will become more apparent in light of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present application will be described hereinafter with reference to the accompanying drawings; however, it is to be understood that the embodiments of the invention are merely examples of the application, which may be practiced in various ways. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the application with unnecessary or excessive detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present application in virtually any appropriately detailed structure.
The specification may use the word "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments as per the application.
The embodiment of the application provides an evaluation method for near-infrared brain function imaging signal quality, which can be applied to an evaluation device for near-infrared brain function imaging signal quality, near-infrared brain function imaging equipment and the like.
Further, as shown in fig. 1, the evaluation method for near infrared brain function imaging signal quality includes steps S101 to S104.
Step S101: at least one piece of near infrared data of the subject is acquired.
Alternatively, the above near infrared data may be understood that the subject may generate one piece of near infrared data when performing one test, that is, one piece of near infrared data is generated by one test, where the piece of near infrared data corresponds to each test channel of the near infrared brain function imaging device, that is, one piece of near infrared data includes near infrared data of a plurality of test channels in one test. The near infrared brain function imaging device can collect brain activity information by arranging a receiving probe and a transmitting probe, one receiving probe and one transmitting probe can form a pair of probe groups, and the detecting channel is formed between the receiving probe and the transmitting probe of the pair of probe groups.
Optionally, under the condition of acquiring multiple pieces of near infrared data, the same subject may acquire multiple pieces of near infrared data obtained by detection and acquisition in different periods, or multiple pieces of near infrared data obtained by acquisition for different subjects, which is not particularly limited in this application.
Alternatively, the near infrared data may include at least subject information and corresponding light intensity data or optical density data of each detection channel. The subject information may include one or more of the following: subject information of a detection task performed by a subject, subject name, detection time, subject ID, sex, date of birth, and the like.
Step S102: and determining signal quality detection parameters of the detection channels corresponding to the near infrared data based on the near infrared data.
Alternatively, the signal quality detection parameters described above can characterize the signal quality of the respective detection channels, such as the degree of fluctuation of the signal, whether the signal is valid, etc. The signal quality detection parameter may be a parameter calculated according to light intensity data or light density data of each detection channel in near infrared data, and the specific calculation mode is not limited in the application, and the signal quality detection parameter can be used for determining the quality of a signal.
Step S103: and determining a block corresponding to the signal quality detection parameter according to the signal quality detection parameter.
Alternatively, the tile corresponding to the signal quality detection parameter may be directly determined according to the value of the signal quality detection parameter, that is, the value of the signal quality detection parameter corresponds to the tile one by one, or the parameter range in which the value is located may be determined according to the value of the signal quality detection parameter, and then the tile is determined based on the parameter range, that is, the parameter range of the value of the signal quality detection parameter corresponds to the tile one by one.
Alternatively, different tiles may be presented in different display manners, specifically, different tiles may correspond to different colors, may also correspond to different padding or to different pixels, for example, the higher the signal quality, the more padding (e.g., padding dots) is displayed in a tile, and the lower the signal quality, the less padding is displayed, which may be a blank tile. By how many colors or fills and the different pixels, these appearance features enable a user to intuitively determine the level of the value of the signal quality detection parameter represented by the tile. Of course, the different display modes can also include the appearance characteristics of size, pattern and the like, and the application is not limited in particular, so that the purpose of intuitively and obviously distinguishing the signal quality detection parameters of each detection channel by a user can be achieved.
Step S104: generating a signal detection quality map based on the pattern block and the sequence of detection channels corresponding to the pattern block to evaluate the signal quality of each detection channel through the signal detection quality map; wherein the signal detection quality map presents identification information distinguishing the near infrared data and the tile in association.
Optionally, each block on the signal detection quality map is sequentially arranged according to the sequence of the detection channels, that is, each detection channel is configured with a number, the sequence of the detection channels is formed according to the sequence of the numbers, and the numbers of the blocks corresponding to the detection channels correspond to the numbers of each detection channel respectively, so that the arrangement sequence of each block on the signal detection quality map is consistent with the sequence of the detection channels, and the position of the detection channel with poor signal quality can be rapidly positioned, thereby facilitating the subsequent processing of signals of the detection channels.
The color, filling content, pixels or patterns and the like of the image blocks on the detection quality map are used for visually displaying the height of the signal quality detection parameters, and particularly, aiming at the situation that the signal quality corresponding to the detection channel is poor, a user can quickly determine the detection channel through the signal detection quality map so as to improve the efficiency and accuracy of detecting the signal quality, and therefore the signals of the detection channel are processed in a targeted manner so as to improve the detection quality of the brain function condition.
Alternatively, the above-described evaluation of the signal quality of each detection channel by the signal detection quality map may be performed by the user directly from the signal detection quality map, or the evaluation result may be generated by a device performing the above-described evaluation method for near-infrared brain function imaging signal quality.
The signal quality is judged through the image blocks in the signal detection quality map, so that the signal quality of each detection channel corresponding to one or more pieces of near infrared data of a single person can be seen, and the signal quality of each detection channel corresponding to the near infrared data of a plurality of persons can be processed in batches. And meanwhile, the method is beneficial to checking the reasons of the signal quality level difference so as to facilitate the subsequent targeted improvement of the signal quality level.
According to the method and the device, the signal quality detection parameters capable of representing the signal quality are obtained through calculation according to the near infrared data, the image blocks corresponding to the signal quality detection parameters are determined according to the signal quality detection parameters, and the signal quality map is generated based on the image blocks and the sequences of the detection channels, wherein different image blocks can represent different signal qualities instead of only checking the light intensity change curve graph of each detection channel through naked eyes, so that a user can intuitively judge the signal quality level of each detection channel of a detected person through the distribution of the image blocks through the signal quality map, the accuracy and the efficiency of signal quality evaluation and screening are improved, in addition, the signal quality map is generated based on the image blocks and the sequences of the detection channels corresponding to the image blocks, the sequences of the image blocks are consistent with the sequences of the detection channels, and accordingly the signal quality of the detection channels can be rapidly positioned, the signal quality of the detection channels can be conveniently processed in a follow-up mode, the signal quality of each detection channel can be seen by a single person, the signal quality of each detection channel can also be intuitively judged for a plurality of pieces of data of single person or each near infrared quality detection channel of near infrared data acquired by a plurality of times, the near infrared data can be visually judged on the quality of the image blocks, and the quality of the signal quality of the near infrared data can be effectively estimated through the image blocks, and the quality of the near infrared data can be effectively estimated, and the quality of the signal can be effectively estimated by the near infrared data.
In some embodiments, as shown in fig. 2, the generating a signal detection quality map based on the tile and the sequence of detection channels corresponding to the tile in step S104 includes steps S201 to S202.
Step S201: and determining the identification information of each piece of near infrared data and the serial numbers of detection channels corresponding to each piece of near infrared data.
Step S202: taking the identification information of the plurality of pieces of near infrared data and the serial numbers of the detection channels as different coordinates, and presenting corresponding image blocks on the target positions of the signal detection quality maps to generate the signal detection quality maps; wherein, the signal quality detection parameters of the detection channels with the same number correspond to the same detection channel.
In this way, by taking the identification information of the near infrared data and the numbers of the detection channels as different coordinates, the image blocks are sequentially presented according to the number sequence of the detection channels, so that a signal detection quality map constructed as a two-dimensional map can be formed, and a user can intuitively confirm the signal quality of each detection channel through the two-dimensional map.
Alternatively, the plurality of pieces of near infrared data may be understood as acquired through multiple acquisitions, and the plurality of pieces of near infrared data may be acquired corresponding to different periods of one subject, or may be acquired corresponding to different subjects.
Alternatively, one of the identification information of the near infrared data and the number of the detection channel may be used as an abscissa, the other may be used as an ordinate, and the numbers of the detection channels are sequentially arranged, so as to determine the target positions of the image blocks of the detection channels corresponding to the plurality of near infrared data respectively.
As shown in fig. 3, an exemplary schematic diagram of a signal detection quality map is shown in fig. 3, the abscissa of the signal detection quality map is the number of the detection channel, that is, the number 1, the number 2, etc., the numbers of the detection channels are sequentially arranged to be consistent with the sequence of the detection channel, the ordinate of the signal detection quality map is identification information of near infrared data to represent different near infrared data, the identification information may be the number, for example, the number 1, the number 2, or the identification information of the ID, the name, the detection time, etc. of the same subject, and the information of the subject or the acquisition time corresponding to the near infrared data may be resolved without specific limitation. In this way, the image blocks in the same column represent the signal detection quality of the same detection channel of each near infrared data, and the image blocks in the same row represent the signal detection quality of each detection channel of one near infrared data, so that the user can quickly compare the signal quality of each detection channel of each near infrared data according to the signal detection quality map, as shown in fig. 3, the signal detection quality of each detection channel of the same near infrared data in the same row can be compared according to the signal detection quality map, and the signal detection quality of the same detection channel of different near infrared data in the same column can be compared, so as to further judge the reason of the poor signal quality.
In some embodiments, as shown in fig. 4, the generating a signal detection quality map in step S104 based on the tile and the sequence of detection channels corresponding to the tile specifically includes steps S301 to S303.
Step S301: and determining a probe arrangement mode corresponding to the near infrared data, wherein the probe arrangement mode comprises the serial numbers of all detection channels.
Step S302: and determining the sequence of the detection channels based on the serial numbers of the detection channels included in the probe arrangement mode.
Step S303: the tiles are sequentially arranged based on the sequence of the detection channels to generate the signal detection quality map.
Therefore, the sequence of the detection channels is determined through the numbers of the detection channels, and the detection channels and the signal detection quality spectrum can be better corresponding, namely, the coordinates of the sequence of the detection channels of the signal detection quality spectrum are consistent with the numbers of the detection channels, so that the user can more quickly determine the signal quality condition of the detection channels corresponding to the numbers based on the signal detection quality spectrum.
Optionally, the probe arrangement modes of different near infrared data may be different, but the probe arrangement modes of near infrared data forming a signal detection quality map are the same, so as to quickly obtain the number of the detection channel with poor signal quality level in the probe arrangement mode, thereby determining the brain region corresponding to the number, the position of the probe, and the like, so as to provide a reference for subsequently evaluating the reasons of poor signal quality level, such as whether the reason is personal to a subject or whether the reason is the reason of the working state failure of the probe.
Illustratively, as shown in fig. 5, the "D" and "S" shown in fig. 5 are the identifications of the receiving probe and the transmitting probe, and the number between the receiving probe and the transmitting probe is the number of the detection channel. For example, the detection channel between the "S1" probe and the "D1" probe shown in fig. 5 is the detection channel of No. 1, and the tile corresponding to the ordinate of No. 1 in fig. 3 is used to characterize the signal quality of the detection channel of No. 1.
In some embodiments, as shown in fig. 6, the evaluation method further includes step S401. Step S401: and determining color bars based on the value range of the signal quality detection parameters, wherein different colors are corresponding to different signal quality detection parameters on the color bars.
Further, with continued reference to fig. 6, the determining, according to the signal quality detection parameter, the block corresponding to the signal quality detection parameter in step S103 specifically includes step S402 and step S403.
Step S402: determining a target color corresponding to the color bar according to the signal quality detection parameter;
step S403: and determining the presentation mode of the image block corresponding to each signal quality detection parameter based on the target color.
Therefore, the color corresponding to the signal quality detection parameter can be quickly determined through the color bar, the image blocks are displayed and presented in the color, and the image blocks with the corresponding color can be accurately determined by taking the color bar as a reference, so that the signal quality of different detection channels can be distinguished in color on the signal quality detection map.
Alternatively, the color bars may form the signal quality reference range by gradient colors, that is, the color of the corresponding tile may be determined on the signal quality reference color bar by the value of the signal quality detection parameter.
Illustratively, the signal quality reference color bar shown in fig. 3 is located on the right side of the signal quality detection spectrum, and the number on the right side of the signal quality reference color bar represents the value of the signal quality detection parameter in the corresponding color, and in the case where the value of the signal quality detection parameter is high, the color of the corresponding tile may be determined to be red, and in the case where the value of the signal quality detection parameter is low, the color of the corresponding tile may be determined to be blue.
In some embodiments, the evaluation method further comprises: classifying the near infrared data based on probe arrangement modes corresponding to the near infrared data; and generating the same signal detection quality map according to the near infrared data of the same category.
Therefore, different signal detection quality maps can be generated by classifying the near infrared data of different probe arrangement modes, so that the same signal detection quality map characterizes the signal detection quality of near infrared data acquired under the same probe arrangement, if the same signal detection quality map is generated based on different probe arrangement modes, different probe arrangement modes possibly comprise different detection channels, and therefore each column of image blocks on the map possibly cannot correspond to the detection channels, brain region positions detected by each detection channel possibly are different, the significance of comparing the quality of the detection channels in the same column is smaller under the condition, the same signal detection quality map is generated based on the same probe arrangement, the fact that the brain positions detected by the detection channels are identical can be ensured, and the comparison result of the same detection channel corresponding to different near infrared data can be rapidly compared through the signal detection quality map so as to further determine the reason that the signal quality is poor.
Optionally, the classification of the near infrared data may be specifically implemented according to the numbers of each detection channel of the probe arrangement mode and the brain positions detected by the detection channels under each number, that is, the numbers of each detection channel corresponding to the near infrared data of the same class and the brain positions detected by the detection channels under each number are identical, and the corresponding probe arrangement mode or the probe arrangement name may be obtained to classify the near infrared data, so that a user may compare the signal quality according to the generated signal detection quality map.
In some embodiments, each of the near infrared data performs a subject condition for a subject of a different attribute and/or for a different subject, the evaluation method further comprising:
dividing the near infrared data into a plurality of groups according to the attribute information of the subject;
sequentially presenting identification information of each group of subjects on the signal detection quality map in groups based on the grouping result;
a detection result related to the attribute information of the subject is determined based on the signal detection quality map.
Therefore, after the image blocks on the signal detection quality map are arranged according to the attribute information of the testee, targeted evaluation can be conveniently carried out based on the attribute information of the testee, and an evaluation result related to the attribute information of the testee can be quickly obtained.
In some embodiments, the attribute information of the subject includes at least one or more of the following: age, sex, disease condition.
In some embodiments, the subject performing topic conditions include at least one or more of the following: subject execution task, subject execution task time.
For example, in the case where the above attribute information includes age, numbers 1 to 15 in fig. 3 may be determined as near infrared data corresponding to 15 old people, and numbers 16 to 30 may be determined as near infrared data corresponding to 15 young people, so that signal quality detection levels of the near infrared data of the old people and the young people can be rapidly compared according to a signal quality detection map.
In some embodiments, the determining, based on the near infrared data, the signal quality detection parameter of each detection channel corresponding to the near infrared data in step S102 specifically includes:
determining signal fluctuation values of the detection channels corresponding to the near infrared data in a preset time period based on the near infrared data;
determining signal quality detection parameters of each detection channel according to the signal fluctuation value; wherein, the larger the signal fluctuation value is, the worse the corresponding signal quality is indicated.
Thus, the signal quality level of each detection channel can be directly determined through the signal fluctuation value, so as to obtain the signal quality detection parameter capable of accurately representing the signal quality.
Alternatively, the above-described preset period may be understood as an entire detection period or a partial detection period of a single detection channel of the subject, and the signal fluctuation value in the preset period can represent the signal quality of the detection channel.
Alternatively, the signal fluctuation value may be a coefficient capable of characterizing the signal fluctuation degree, and the signal quality of the corresponding detection channel may be intuitively presented by determining the color of the tile corresponding to the signal fluctuation value.
For example, the signal fluctuation value may be a variation coefficient, where the variation coefficient is related to a standard deviation and an average value of the light intensity, and the larger the value of the variation coefficient, the larger the signal fluctuation of the corresponding detection channel, and the worse the signal quality. For example, the signal quality reference on the color bar ranges from 0 to 40.0 is represented by a dark blue color, 40 is represented by a dark red color, the closer the color of the tile of the detection channel is to blue representing the better signal quality of the detection channel, and the closer to red representing the worse signal quality of the detection channel.
In some embodiments, the identification information of the plurality of pieces of near infrared data is an ordinate, the number of the detection channel is an abscissa, and the generated signal detection quality map includes a plurality of rows and a plurality of columns of tiles for evaluating signal quality detection parameters. As shown in fig. 7, the evaluation method further includes steps S501 to S503.
Step S501: a first signal quality output of near infrared data corresponding to the data of the data is determined based on the data of the tiles in the signal detection quality map.
Step S502: a second signal quality output for each of the detection channels is determined based on column data for tiles in the signal detection quality map.
Step S503: and determining a signal quality influence factor according to the first signal quality output result and the second signal quality output result, wherein the signal quality influence factor is related to the acquisition state of the subject and/or the acquisition state of the near infrared brain function imaging device.
Alternatively, the first signal quality output result may be the quality of signal detection of all detection channels corresponding to the near infrared data, and the first signal quality output result may be the quality of signal detection of the same detection channel of different near infrared data.
Therefore, the result of good and bad signal quality can be directly determined according to the data of the rows and the columns of the image blocks in the signal detection quality map, so that the reason of the poor signal quality can be evaluated, and the signal quality can be pertinently improved according to the reason. For example, it may be determined that the signal quality of a certain detection channel is poor based on the data, and then determine, according to the column data corresponding to the detection channel with poor signal quality, whether the quality of the detection channel of other near infrared data is good or bad, if the quality of the signal of the detection channel of other near infrared data is poor, the reason of the poor signal quality may be related to the acquisition state of the near infrared brain function imaging device, and if the quality of the signal of the detection channel of other near infrared data is good, the reason of the poor signal quality may be related to the acquisition state of the subject corresponding to the piece of near infrared data. The line data and the column data of the image block are comprehensively compared, so that a user is helped to screen out the reasons of poor signal quality as soon as possible.
Alternatively, the row data of the above-mentioned image block may be understood as an image block of each detection channel corresponding to one piece of near infrared data, and the column data of the image block may be understood as an image block of the same detection channel corresponding to different pieces of near infrared data.
Alternatively, in the case where the signal quality influencing factor is related to the acquisition state of the subject, it can be understood that the signal quality influencing factor is influenced by the subject's own cause, for example, the subject does not cooperate with examination, the subject is a parkinson patient, the subject's head shape is more often different, or the like, the signal quality is influenced.
Illustratively, the head shape of the subject is relatively flat, such as the head corresponding to the pillow may be relatively flat, which results in the head cap with the probe attached thereto not fitting well with the head, thereby affecting the signal quality detected by the probe.
Alternatively, in the case where the signal quality influencing factor is related to the acquisition state of the near-infrared brain function imaging device, it can be understood that the signal quality influencing factor is related to the near-infrared brain function imaging device itself, for example, a fault exists in a probe on the near-infrared brain function imaging device, or a deviation exists in the position of the probe, or the like.
In some embodiments, after the determining signal quality detection parameters of the respective detection channels corresponding thereto based on the near infrared data of step S102, the evaluation method further includes: determining output information related to the near infrared data, wherein the output information at least comprises signal quality detection parameters of each detection channel, subjects executed by a subject, data acquisition time and attribute information of the subject; and determining an evaluation result of signal quality corresponding to each detection channel based on the output information.
Wherein the attribute information of the subject may include the subject's ID, name, and/or year of birth, etc. The output mode of the signal quality detection parameters in the output information can be consistent with the arrangement mode of the image blocks in the signal quality detection map, specifically, one row of output data corresponds to related information of one piece of near infrared data, and the related information can comprise attribute information of a detected person, an execution subject, data acquisition time and signal quality detection parameters of each detection channel generated according to the sequence of the detection channels. In the multi-row output data, the items of the output data of each column are kept consistent, and the signal quality detection parameters of the detection channels of the same column correspond to the same detection channel. In addition, the same probe arrangement mode can be used for outputting in batches to form a data table or other forms of documents, and the output of different probe arrangement modes is required to form a plurality of data tables or other forms of documents according to the different probe arrangement modes.
Thus, output information related to the signal quality of the near infrared data detection channels can be output in batches, so that the detection channels of a large amount of near infrared data can be rapidly and accurately evaluated according to the information output in batches. Particularly, when the signal quality cannot be accurately judged according to the signal detection quality map, the signal quality can be accurately judged according to specific data in the output information, and the signal quality can be further and more accurately evaluated after the signal detection quality map. The output mode of the signal quality detection parameters in the output information can be consistent with the arrangement mode of the image blocks in the signal quality detection map, and after a user judges the position of the detection channel with poor signal quality according to the signal quality detection map, the user can quickly position the position of the corresponding detection channel through the output information to make further judgment.
In some optional embodiments, the evaluation method may further comprise determining an evaluation parameter for evaluating the signal quality of the detection channel; and respectively comparing the evaluation parameters with the signal quality detection parameters respectively corresponding to the detection channels to obtain evaluation comparison results. Therefore, the signal quality can be directly evaluated through the comparison between the evaluation parameter and the signal quality detection parameter, so that the purpose of rapidly and accurately evaluating the signal quality is achieved.
After determining that the signal quality is poor, the signal quality of the detection channel adjacent to the detection channel can be evaluated, when determining that the signal quality of the detection channel adjacent to the detection channel is good, the light intensity or optical density data of the detection channel adjacent to the detection channel or the average value of the light intensity or optical density data can be used as the light intensity or optical density data of the detection channel with poor signal quality, and near infrared data such as the light intensity or optical density data of the detection channel with poor signal quality can be directly removed.
The embodiment of the application also provides an evaluation device 110 for near infrared brain function imaging signal quality. As shown in fig. 8, the evaluation device 110 for near-infrared brain function imaging signal quality includes an acquisition module 101, a determination module 102, and a generation module 103. The acquisition module 101 is configured to acquire at least one piece of near infrared data of the subject. The determining module 102 is configured to determine signal quality detection parameters of respective detection channels corresponding to the near infrared data based on the near infrared data; and determining a block corresponding to the signal quality detection parameter according to the signal quality detection parameter. The generation module 103 is configured to generate a signal detection quality map based on the tile and the sequence of detection channels corresponding to the tile to evaluate the signal quality of each detection channel by the signal detection quality map; wherein the signal detection quality map presents the identification information distinguishing near infrared data and the tiles in association.
According to the method and the device, the signal quality detection parameters capable of representing the signal quality are obtained through calculation according to the near infrared data, the image blocks corresponding to the signal quality detection parameters are determined according to the signal quality detection parameters, and the signal quality map is generated based on the image blocks and the sequences of the detection channels, wherein different image blocks can represent different signal qualities instead of only checking the light intensity change curve graph of each detection channel through naked eyes, so that a user can intuitively judge the signal quality level of each detection channel of a detected person through the distribution of the image blocks through the signal quality map, the accuracy and the efficiency of signal quality evaluation and screening are improved, in addition, the signal quality map is generated based on the image blocks and the sequences of the detection channels corresponding to the image blocks, the sequences of the image blocks are consistent with the sequences of the detection channels, and accordingly the signal quality of the detection channels can be rapidly positioned, the signal quality of the detection channels can be conveniently processed in a follow-up mode, the signal quality of each detection channel can be seen by a single person, the signal quality of each detection channel can also be intuitively judged for a plurality of pieces of data of single person or each near infrared quality detection channel of near infrared data acquired by a plurality of times, the near infrared data can be visually judged on the quality of the image blocks, and the quality of the signal quality of the near infrared data can be effectively estimated through the image blocks, and the quality of the near infrared data can be effectively estimated, and the quality of the signal can be effectively estimated by the near infrared data.
In some embodiments, the near infrared data is a plurality of pieces, and the determining module 102 is further configured to: determining respective identification information of a plurality of pieces of near infrared data and numbers of detection channels corresponding to the near infrared data; taking the identification information of the plurality of pieces of near infrared data and the serial numbers of the detection channels as different coordinates, and presenting corresponding image blocks on the target positions of the signal detection quality maps to generate the signal detection quality maps; wherein, the signal quality detection parameters of the detection channels with the same number correspond to the same detection channel.
In some embodiments, the generation module 103 is further configured to: determining a probe arrangement mode corresponding to the near infrared data, wherein the probe arrangement mode comprises the serial numbers of all detection channels; determining the sequence of the detection channels based on the serial numbers of the detection channels included in the probe arrangement mode; the tiles are sequentially arranged based on the sequence of the detection channels to generate the signal detection quality map.
In some embodiments, the determination module 102 is further configured to: determining color bars based on the value range of the signal quality detection parameters, wherein different colors are corresponding to different signal quality detection parameters on the color bars; determining a target color corresponding to the color bar according to the signal quality detection parameter; and determining the presentation mode of the image block corresponding to each signal quality detection parameter based on the target color.
In some embodiments, the evaluation device for near infrared brain function imaging signal quality further comprises a classification module configured to: classifying the near infrared data based on probe arrangement modes corresponding to the near infrared data; and generating the same signal detection quality map according to the near infrared data of the same category.
In some embodiments, each of the near infrared data corresponds to a subject of a different attribute and/or corresponds to a subject execution subject condition of a different subject, and the evaluation device for near infrared brain function imaging signal quality further comprises a grouping module configured to: dividing the near infrared data into a plurality of groups according to the attribute information of the subject; sequentially presenting identification information of each group of subjects on the signal detection quality map in groups based on the grouping result; a detection result related to the attribute information of the subject is determined based on the signal detection quality map.
In some embodiments, the attribute information of the subject includes at least one or more of the following: age, sex, disease condition.
In some embodiments, the subject performing topic conditions include at least one or more of the following: subject execution task, subject execution task time.
In some embodiments, the determination module 102 is further configured to: determining signal fluctuation values of the detection channels corresponding to the near infrared data in a preset time period based on the near infrared data; determining signal quality detection parameters of each detection channel according to the signal fluctuation value; wherein, the larger the signal fluctuation value is, the worse the corresponding signal quality is indicated.
In some embodiments, the identification information of the plurality of pieces of near infrared data is an ordinate, the sequence of the detection channels is an abscissa, and the generated signal detection quality map includes a plurality of rows and a plurality of columns of tiles for evaluating signal quality detection parameters. The determination module 102 is further configured to: determining a first signal quality output result of near infrared data corresponding to data of a block in the signal detection quality map based on the data of the data; determining a second signal quality output for each of the detection channels based on column data for tiles in the signal detection quality map; and determining a signal quality influence factor according to the first signal quality output result and the second signal quality output result, wherein the signal quality influence factor is related to the acquisition state of the subject and/or the acquisition state of the near infrared brain function imaging device.
In some embodiments, the determination module 102 is further configured to: after determining signal quality detection parameters of each detection channel corresponding to the near infrared data based on the near infrared data, determining output information related to the near infrared data, wherein the output information at least comprises the signal quality detection parameters of each detection channel, an executed subject of a subject, data acquisition time and attribute information of the subject; and determining an evaluation result of signal quality corresponding to each detection channel based on the output information.
The embodiment of the application also provides a near-infrared brain function imaging signal quality evaluation system, which comprises the near-infrared brain function imaging signal quality evaluation device.
According to the method and the device, the signal quality detection parameters capable of representing the signal quality are obtained through calculation according to the near infrared data, the image blocks corresponding to the signal quality detection parameters are determined according to the signal quality detection parameters, and the signal quality map is generated based on the image blocks and the sequences of the detection channels, wherein different image blocks can represent different signal qualities instead of only checking the light intensity change curve graph of each detection channel through naked eyes, so that a user can intuitively judge the signal quality level of each detection channel of a detected person through the distribution of the image blocks through the signal quality map, the accuracy and the efficiency of signal quality evaluation and screening are improved, in addition, the signal quality map is generated based on the image blocks and the sequences of the detection channels corresponding to the image blocks, the sequences of the image blocks are consistent with the sequences of the detection channels, and accordingly the signal quality of the detection channels can be rapidly positioned, the signal quality of the detection channels can be conveniently processed in a follow-up mode, the signal quality of each detection channel can be seen by a single person, the signal quality of each detection channel can also be intuitively judged for a plurality of pieces of data of single person or each near infrared quality detection channel of near infrared data acquired by a plurality of times, the near infrared data can be visually judged on the quality of the image blocks, and the quality of the signal quality of the near infrared data can be effectively estimated through the image blocks, and the quality of the near infrared data can be effectively estimated, and the quality of the signal can be effectively estimated by the near infrared data.
The embodiment of the application also provides a storage medium, which stores a computer program, wherein the computer program realizes the steps of the assessment method for near infrared brain function imaging signal quality when being executed by a processor.
Note that according to various units in various embodiments of the present application, they may be implemented as computer-executable instructions stored on a memory, which when executed by a processor, may implement corresponding steps; may also be implemented as hardware having corresponding logic computing capabilities; and may also be implemented as a combination of software and hardware (firmware). In some embodiments, the processor may be implemented as any one of FPGA, ASIC, DSP chip, SOC (system on a chip), MPU (e.g., without limitation, cortex), etc. The processor may be communicatively coupled to the memory and configured to execute computer-executable instructions stored therein. The memory may include read-only memory (ROM), flash memory, random Access Memory (RAM), dynamic Random Access Memory (DRAM) such as Synchronous DRAM (SDRAM) or Rambus DRAM, static memory (e.g., flash memory, static random access memory), etc., upon which computer-executable instructions are stored in any format. Computer-executable instructions may be accessed by the processor, read from ROM or any other suitable memory location, and loaded into RAM for execution by the processor to implement a wireless communication method in accordance with various embodiments of the present application.
It should be noted that, among the components of the system of the present application, the components thereof are logically divided according to functions to be implemented, but the present application is not limited thereto, and the components may be re-divided or combined as needed, for example, some components may be combined into a single component, or some components may be further decomposed into more sub-components.
Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some or all of the components in a system according to embodiments of the present application may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present application may also be embodied as an apparatus or device program (e.g., computer program and computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present application may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form. In addition, the application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
Furthermore, although exemplary embodiments have been described herein, the scope thereof includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of the various embodiments across), adaptations or alterations as pertains to the present application. Elements in the claims are to be construed broadly based on the language employed in the claims and are not limited to examples described in the present specification or during the practice of the present application, which examples are to be construed as non-exclusive.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the above detailed description, various features may be grouped together to streamline the application. This is not to be interpreted as an intention that the disclosed features not being claimed are essential to any claim. Rather, the subject matter of the present application is capable of less than all of the features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with one another in various combinations or permutations. The scope of the application should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application, the scope of which is defined by the claims. Various modifications and equivalent arrangements may be made to the present application by those skilled in the art, which modifications and equivalents are also considered to be within the scope of the present application.
Claims (12)
1. A method for assessing near infrared brain function imaging signal quality, comprising:
acquiring a plurality of pieces of near infrared data of a subject;
determining signal quality detection parameters of the detection channels corresponding to the near infrared data based on the near infrared data;
determining a block corresponding to the signal quality detection parameter according to the signal quality detection parameter;
generating a signal detection quality map based on the pattern block and the sequence of detection channels corresponding to the pattern block to evaluate the signal quality of each detection channel through the signal detection quality map; wherein the signal detection quality map associatively presents identification information distinguishing the near infrared data and the image block; wherein,
the generating a signal detection quality map based on the block and the sequence of detection channels corresponding to the block specifically includes:
Determining respective identification information of a plurality of pieces of near infrared data and numbers of detection channels corresponding to the near infrared data;
taking the identification information of the plurality of pieces of near infrared data and the serial numbers of the detection channels as different coordinates, and presenting corresponding image blocks on the target positions of the signal detection quality maps to generate the signal detection quality maps; wherein, the signal quality detection parameters of the detection channels with the same number correspond to the same detection channel.
2. The method for assessing the quality of a near infrared brain function imaging signal according to claim 1, wherein said generating a signal detection quality map based on said pattern and a sequence of said detection channels corresponding to said pattern comprises in particular:
determining a probe arrangement mode corresponding to the near infrared data, wherein the probe arrangement mode comprises the serial numbers of all detection channels;
determining the sequence of the detection channels based on the serial numbers of the detection channels included in the probe arrangement mode;
the tiles are sequentially arranged based on the sequence of the detection channels to generate the signal detection quality map.
3. The evaluation method for near infrared brain function imaging signal quality according to claim 1, characterized in that the evaluation method further comprises:
determining color bars based on the value range of the signal quality detection parameters, wherein different colors are corresponding to different signal quality detection parameters on the color bars;
the determining the block corresponding to the signal quality detection parameter according to the signal quality detection parameter specifically includes:
determining a target color corresponding to the color bar according to the signal quality detection parameter;
and determining the presentation mode of the image block corresponding to each signal quality detection parameter based on the target color.
4. The evaluation method for near infrared brain function imaging signal quality according to claim 1, characterized in that the evaluation method further comprises:
classifying the near infrared data based on probe arrangement modes corresponding to the near infrared data;
and generating the same signal detection quality map according to the near infrared data of the same category.
5. The evaluation method for near infrared brain function imaging signal quality according to claim 1, wherein each of the near infrared data performs a subject condition for a subject of a different attribute and/or for a different subject, the evaluation method further comprising:
Dividing the near infrared data into a plurality of groups according to the attribute information of the subject;
sequentially presenting identification information of each group of subjects on the signal detection quality map in groups based on the grouping result;
a detection result related to the attribute information of the subject is determined based on the signal detection quality map.
6. The method for assessing the quality of a near infrared brain function imaging signal according to claim 5, wherein said subject's attribute information includes at least one or more of the following: age, sex, and disease condition; and the subject execution subject condition includes at least one or more of the following information: subject execution task, subject execution task time.
7. The method for evaluating the quality of near-infrared brain function imaging signals according to claim 1, wherein said determining signal quality detection parameters of the respective detection channels corresponding thereto based on said near-infrared data specifically comprises:
determining signal fluctuation values of the detection channels corresponding to the near infrared data in a preset time period based on the near infrared data;
determining signal quality detection parameters of each detection channel according to the signal fluctuation value; wherein, the larger the signal fluctuation value is, the worse the corresponding signal quality is indicated.
8. The method for evaluating the quality of near-infrared brain function imaging signals according to claim 1, wherein identification information of a plurality of pieces of the near-infrared data is on an ordinate, a sequence of the detection channels is on an abscissa, and the generated signal detection quality map contains a plurality of rows and a plurality of columns of tiles that evaluate signal quality detection parameters, the method further comprising:
determining a first signal quality output result of near infrared data corresponding to data of a block in the signal detection quality map based on the data of the data;
determining a second signal quality output for each of the detection channels based on column data for tiles in the signal detection quality map;
and determining a signal quality influence factor according to the first signal quality output result and the second signal quality output result, wherein the signal quality influence factor is related to the acquisition state of the subject and/or the acquisition state of the near infrared brain function imaging device.
9. The evaluation method for near infrared brain function imaging signal quality according to claim 1, wherein after said determining signal quality detection parameters of respective detection channels corresponding thereto based on said near infrared data, said evaluation method further comprises:
Determining output information related to the near infrared data, wherein the output information at least comprises signal quality detection parameters of each detection channel, subjects executed by a subject, data acquisition time and attribute information of the subject;
and determining an evaluation result of signal quality corresponding to each detection channel based on the output information.
10. An evaluation device for near infrared brain function imaging signal quality, comprising:
an acquisition module configured to acquire a plurality of pieces of near infrared data of a subject;
a determining module configured to determine signal quality detection parameters of respective detection channels corresponding thereto based on the near infrared data; the method comprises the steps of,
determining a block corresponding to the signal quality detection parameter according to the signal quality detection parameter, wherein the block has a color corresponding to the block;
a generation module configured to generate a signal detection quality map based on the tile and a sequence of the detection channels corresponding to the tile to evaluate signal quality of each detection channel by the signal detection quality map; wherein the signal detection quality map associatively presents identification information distinguishing the near infrared data and the image block; wherein,
The generation module is further configured to: determining respective identification information of a plurality of pieces of near infrared data and numbers of detection channels corresponding to the near infrared data;
taking the identification information of the plurality of pieces of near infrared data and the serial numbers of the detection channels as different coordinates, and presenting corresponding image blocks on the target positions of the signal detection quality maps to generate the signal detection quality maps; wherein, the signal quality detection parameters of the detection channels with the same number correspond to the same detection channel.
11. An evaluation system for near-infrared brain function imaging signal quality, characterized by comprising the evaluation device for near-infrared brain function imaging signal quality according to claim 10.
12. A storage medium, characterized in that a computer program is stored, which, when being executed by a processor, implements the steps of the assessment method for near infrared brain function imaging signal quality as claimed in any one of claims 1 to 9.
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