CN110547768A - Near-infrared brain function imaging quality control method and control system - Google Patents
Near-infrared brain function imaging quality control method and control system Download PDFInfo
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Abstract
The invention belongs to the field of brain detection, and relates to a near-infrared brain function imaging quality control method and a near-infrared brain function imaging quality control system, which comprise the following steps: converting original light intensity data acquired by a near-infrared imaging device into optical density data; calculating the standard deviation of each sliding window by adopting a sliding window method; judging abnormal points; determining an abnormal time point according to the abnormal point; intercepting optical density data of each test channel between two adjacent abnormal time points which are farthest away from each other in the plurality of detected abnormal time points; and if the time sequence length of the optical density time sequence data is greater than or equal to a certain fixed time length, determining that the intercepted optical density time sequence data preliminarily meets the quality requirement. The invention intercepts the time sequence with more stable signal quality to carry out preliminary quality control, effectively eliminates the influence of abnormal time points on the signal quality evaluation, and ensures the reliability of the signal quality.
Description
Technical Field
the invention relates to a near-infrared brain function imaging quality control method and a near-infrared brain function imaging quality control system, and belongs to the field of brain function testing.
Background
At present, the following methods are mainly used for evaluating the quality of signal data acquired by a near-infrared brain function imaging technology: 1) evaluating signal quality according to magnitude of change amplitude of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration signals; 2) evaluating the signal quality according to the correlation of the time series change trend of the concentration signals of HbO and HbR; 3) evaluating the signal quality according to the signal-to-noise ratio of the original light intensity data; 4) and calculating a power spectrum according to the concentration signals of HbO and HbR, and checking whether a heartbeat correlation peak value appears or not to evaluate the signal quality.
The influence of noise signals in the concentration data of HbO and HbR on the time series change trend cannot be eliminated in the method 2), and the quality of the concentration signal data has no objective standard and depends on subjective judgment; the other methods have different emphasis on evaluating the quality of concentration signals, so that quality control by only one method may cause errors in quality evaluation of signals. In addition, the above methods are based on the quality evaluation of the signals by a relatively complete time sequence, and cannot avoid the influence of unstable time points of data acquisition or time points of large motion artifacts on the signal quality, thereby causing the inaccuracy of concentration data signals.
disclosure of Invention
In view of the above problems, an object of the present invention is to provide a quality control method for near-infrared brain function imaging, which effectively eliminates the influence of these time points on signal quality evaluation by intercepting time series with relatively stable signal quality for subsequent quality control.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a near-infrared brain function imaging quality control method, which comprises the following steps: 1) converting original light intensity data acquired by a near-infrared imaging device into optical density data; 2) recording the optical density data changing along with time to obtain an optical density time sequence, and calculating the standard deviation of each sliding window by adopting a sliding window method for the optical density time sequence data; 3) assuming that the standard deviation of the sliding window obeys normal distribution, determining a numerical range formed by adding or subtracting a preset number of standard deviations from the normal distribution mean of the standard deviation as a normal value range, and judging data points outside the normal value range as abnormal points; 4) acquiring density time series data of all the test channels, and if the abnormal point occurs when a time point exceeds a preset number of the test channels, determining the time point as an abnormal time point; 5) and intercepting a time sequence between two adjacent abnormal time points with the farthest distance from the plurality of detected abnormal time points, and judging whether the optical density time sequence data preliminarily meet the requirements according to the length of the time sequence.
further, in the step 5), the method for determining whether the optical density time-series data meets the requirement includes: if the time sequence length is greater than or equal to a preset time length, determining that the intercepted optical density time sequence data preliminarily meets the quality requirement; and if the time sequence length is less than the preset time length, the optical density time sequence data does not meet the quality requirement.
further, the optical density time-series data which preliminarily meet the quality requirement are subjected to signal-to-noise ratio detection, channel concentration signal correlation analysis and power spectrum heartbeat peak detection.
Further, the optical density time-series data meet the conditions that the signal-to-noise ratio is not less than a preset value, the correlation degree of each channel concentration signal is a positive value or the heartbeat peak value of the power spectrum occurs, and the optical density time-series data are considered to meet the quality requirement.
further, the signal-to-noise ratio detection comprises: and respectively calculating the signal-to-noise ratio of each test channel under different wavelengths to reflect the signal acquisition quality acquired by each test channel by defining the signal-to-noise ratio of the signal through the average value of the original light intensity time sequence data and the standard deviation ratio of the light density time sequence data.
Further, the test channel concentration signal correlation analysis comprises: normalizing the optical density time-series data which preliminarily meet the quality requirement, removing high-frequency noise and low-frequency drift through a 0.01-0.1Hz band-pass filter, converting the filtered data into oxyhemoglobin, deoxyhemoglobin and total hemoglobin concentration data according to a modified Beer-Lambert law, selecting the concentration data needing to calculate a signal correlation matrix, and calculating the concentration data time series of all the test channels which are correlated pairwise to obtain a corresponding concentration correlation coefficient matrix.
Further, the power spectrum heartbeat peak detection comprises: performing primary band-pass filtering of 0-3Hz on the optical density time-series data which preliminarily meet the quality requirement, and converting the filtered data into time series of oxyhemoglobin concentration and deoxyhemoglobin concentration according to a modified Beer-Lambert law; resampling the time series of the oxyhemoglobin concentration and the deoxyhemoglobin concentration to 5Hz, and then performing band-pass filtering of 0.01-2Hz on the time series of the resampled oxyhemoglobin concentration and deoxyhemoglobin concentration; carrying out Fourier transform on the time series of the filtered oxyhemoglobin concentration and the filtered deoxyhemoglobin concentration, converting the time series of the oxyhemoglobin concentration and the deoxyhemoglobin concentration from a time domain signal to a frequency domain signal, and obtaining amplitudes corresponding to the frequencies of the oxyhemoglobin concentration and the deoxyhemoglobin concentration; and performing modulus extraction on the amplitudes of the oxygenated hemoglobin concentration frequency curves and the deoxygenated hemoglobin concentration frequency curves, then performing square operation to obtain power corresponding to the frequencies, performing normalization on the power corresponding to the frequencies to obtain normalized power spectrums, and displaying the corresponding power spectrums with the frequencies of 0.5-1.5Hz of each test channel.
Further, in the step 4), if the abnormal time point occurs within a plurality of seconds before the time series or within a plurality of seconds after the time series is inverted, the abnormal time point is determined as an unstable data acquisition time point; and if the abnormal time point appears in the rest time periods, the abnormal time point is a motion artifact time point.
The invention also provides a near-infrared brain function imaging quality control system, which comprises: the near-infrared imaging module is used for acquiring original light intensity data and converting the original light intensity data into optical density data; the standard deviation calculation module is used for converting the optical density data into optical density time series data and calculating the standard deviation of each sliding window by adopting a sliding window method for the optical density time series data; the abnormal time point determining module is used for determining an abnormal time point according to the standard deviation data of the normal distribution; and the first judgment module is used for selecting proper optical density time sequence length according to the abnormal time point data and judging whether the optical density time sequence data preliminarily meet the requirements or not according to the length of the time sequence.
and the secondary judgment module is used for carrying out signal-to-noise ratio detection, channel concentration signal correlation analysis and power spectrum heartbeat peak detection on the optical density time-series data which preliminarily meet the quality requirement.
Due to the adoption of the technical scheme, the invention has the following advantages: 1) converting the raw light intensity data into optical density data improves the accuracy of data detection. 2) The abnormal point is judged through the standard deviation of the optical density time series data, so that the judgment of the abnormal point is simpler, more convenient and more accurate. 3) The method has the advantages that the influence of abnormal time points on signal quality evaluation is effectively eliminated by intercepting the time sequence with stable signal quality to carry out primary quality control. 4) The method further evaluates the signal quality by combining three methods of signal-to-noise ratio detection, channel concentration signal correlation analysis and power spectrum heartbeat peak detection, controls the signal quality from multiple angles and ensures the reliability of the data signal quality.
Drawings
FIG. 1 is a time series of optical density for each test channel in an embodiment of the present invention;
FIG. 2 is a signal-to-noise ratio plot of optical density data for each test channel in an embodiment of the present invention;
FIG. 3 is a graph of correlation of optical density data for each test channel in an embodiment of the present invention;
FIG. 4 is a power spectrum of 0.5 Hz to 1.5Hz for each test channel in one embodiment of the present invention.
Detailed Description
the present invention is described in detail below with reference to the attached drawings. It is to be understood, however, that the drawings are provided solely for the purposes of promoting an understanding of the invention and that they are not to be construed as limiting the invention. In describing the present invention, it is to be understood that the terminology used is for the purpose of description only and is not intended to be indicative or implied of relative importance.
In one embodiment of the invention, a near-infrared brain function imaging quality control method is provided, which comprises the following steps:
1) converting original light intensity data acquired by a near-infrared imaging device into optical density data;
2) Recording optical density data changing along with time to obtain an optical density time sequence, and calculating the standard deviation of each sliding window for the optical density time sequence data by adopting a sliding window method;
3) assuming that the standard deviation of the sliding window obeys normal distribution, determining a numerical range formed by adding or subtracting 5 standard deviations to the normal distribution mean of the standard deviation as a normal value range, and judging data points outside the normal value range as abnormal points;
4) Acquiring density time series data of all test channels, and if an abnormal point occurs in the test channels with a time point exceeding 1/3 of the total channel number, determining the time point as an abnormal time point;
5) and intercepting a time sequence between two adjacent abnormal time points with the farthest distance from the detected multiple abnormal time points, and judging whether the optical density time sequence data meet the requirements according to the length of the time sequence. In the embodiment, the original light intensity data obtained by the infrared imaging device is converted into the optical density data, so that the accuracy of data detection is improved; the abnormal point is judged through the standard deviation of the optical density time sequence data, so that the judgment of the abnormal point is simpler, more convenient and more accurate; the method has the advantages that the time sequence with stable signal quality is intercepted to carry out preliminary quality control, the influence of abnormal time points on signal quality evaluation is effectively eliminated, and the reliability of optical density data is preliminarily ensured.
The method for determining whether the optical density time meets the requirement in step 5) of this embodiment is as follows: if the time sequence length is greater than or equal to the preset time length, determining that the intercepted optical density time sequence data preliminarily meets the quality requirement; and if the time sequence length is less than the preset time length, the optical density time sequence data does not meet the quality requirement.
the abnormal time points in step 4) of the present embodiment include unstable time points and motion artifact time points, where the unstable time points refer to abnormal points generated due to instability of the instrument at the start and the end of the test; the motion artifact time point refers to an abnormal point generated due to an environmental factor during a test. In this embodiment, if the abnormal time point occurs within 5 seconds before the time series or within 5 seconds after the time series, the abnormal time point is determined as an unstable data acquisition time point, and if the abnormal time point occurs within the remaining time periods, the abnormal time point is a motion artifact time point.
In step 5) of this embodiment, a certain fixed time length is preferably 300s, and the window length of the sliding window is the time for acquiring two times of original light intensity data. It should be noted that this time length is only a preferred time length set by integrating the accuracy and efficiency of the data, and those skilled in the art can select a suitable time length according to their specific testing requirements.
as shown in fig. 1, in order to facilitate an operator to simply and quickly know whether test data is available, step 5) of this embodiment further includes a visual display device capable of displaying the data, and when the intercepted optical density time series data preliminarily meets the quality requirement, the intercepted data is displayed on a display screen of the display device, so that a user can perform subsequent processing. The black points in fig. 1 are abnormal time points, and optical density time series data between two farthest and adjacent abnormal time points are intercepted, for example, 10-630s in fig. 1 are intercepted optical density time series data, the time series of the data is greater than 300s, so that the data is considered to meet the quality requirement preliminarily; and if the intercepted optical density time series data do not meet the quality requirement, displaying the data on a display screen, or reminding a user to detect again.
In another embodiment of the invention, signal-to-noise ratio detection, channel concentration signal correlation analysis and power spectrum heartbeat peak detection are carried out on the optical density time-series data which meet the quality requirement preliminarily. The signal quality is evaluated by combining three methods of signal-to-noise ratio detection, channel concentration signal correlation analysis and power spectrum heartbeat peak value detection, the signal quality is controlled from multiple angles, and the reliability of the data signal quality is further ensured.
Wherein, the signal-to-noise ratio detection comprises: and respectively calculating the signal-to-noise ratio of each test channel under different wavelengths to reflect the signal quality acquired by each test channel by defining the signal-to-noise ratio of the signal according to the average value of the original light intensity time sequence data and the standard deviation ratio of the light density time sequence data. The higher the signal-to-noise ratio, the better the signal quality. As shown in fig. 2, in general, when the signal-to-noise ratio is greater than or equal to 2, the measured data is considered to meet the quality requirement. In fig. 2 in particular, the measured data is considered to be of satisfactory quality as long as the signal-to-noise ratio of the measured data is higher than the solid bold line in the figure.
The test channel concentration signal correlation analysis comprises: normalizing the optical density time-series data which preliminarily meet the quality requirement, removing high-frequency noise and low-frequency drift through a band-pass filter of 0.01-0.1Hz, converting the filtered data into oxyhemoglobin, deoxyhemoglobin and total hemoglobin concentration data according to a modified Beer-Lambert law, selecting the concentration data of a signal correlation matrix to be calculated, and calculating the concentration data time series of all test channels which are correlated pairwise to obtain a corresponding concentration correlation coefficient matrix. In the density correlation coefficient matrix, the larger the value of the correlation coefficient, the larger the correlation between the channel signal and other channel signals. By the method, a channel which cannot acquire data due to poor contact of equipment can be detected according to a mutual correlation mode between overall signals. In general, only the correlation coefficient is very small, as shown in fig. 3, the data is not collected by the test channel when the correlation coefficient is close to-1, and the area marked by the bold solid line in fig. 3 is the test channel without data collected. However, in this embodiment, in order to ensure the accuracy of the data quality, it is preferable that the correlation coefficient of each channel is greater than or equal to 0, which is the test data meeting the quality standard.
As shown in fig. 4, the power spectrum heartbeat peak detection includes: and performing primary band-pass filtering of 0-3Hz on the optical density time sequence data which preliminarily meets the quality requirement, and converting the filtered data into time sequences of oxyhemoglobin concentration and deoxyhemoglobin concentration according to a modified Beer-Lambert law.
The time series of oxyhemoglobin concentrations, deoxyhemoglobin concentrations are resampled to 5Hz and then the resampled time series of oxyhemoglobin concentrations, deoxyhemoglobin concentrations are band-pass filtered from 0.01 to 2 Hz.
And performing Fourier transform on the filtered time series of the oxyhemoglobin concentration and the deoxyhemoglobin concentration to convert the time series of the oxyhemoglobin concentration and the deoxyhemoglobin concentration from time domain signals into frequency domain signals, and obtaining amplitudes corresponding to the frequencies of the oxyhemoglobin concentration and the deoxyhemoglobin concentration.
And performing modulus extraction on the amplitudes of the oxygenated hemoglobin concentration frequency curve and the deoxygenated hemoglobin concentration frequency curve, then performing square operation to obtain power corresponding to the frequency, performing normalization on the power corresponding to the frequency to obtain a normalized power spectrum, and displaying the corresponding power spectrum with the frequency of 0.5-1.5Hz in each test channel.
As shown in fig. 4, the power spectrums of 48 test channels are simultaneously displayed by the corresponding power spectrums with the frequency of 0.5-1.5Hz in each test channel, and it can be seen clearly through the power spectrums that the heartbeat power peak appears in the power spectrums of which test channels and the heartbeat power peak does not appear, wherein the signal corresponding to the power spectrum of the test channel with the heartbeat power peak appears is the signal meeting the quality requirement. By adopting the method, the power spectrograms are sorted according to the rows and the columns, so that the inconvenience caused by the need of checking the power spectrogram of each channel for multiple times is avoided, and the operation is more convenient. Furthermore, a frequency of 0.5-1.5Hz is chosen because this frequency range is the frequency range that covers the power peaks of the heart beats.
If the signal can meet the requirements on the quality standard in the signal-to-noise ratio detection, the channel concentration signal correlation analysis and the power spectrum heartbeat peak detection, the signal which is finally determined and meets the quality standard is the signal. This is an ideal situation, and in practice, a signal can be identified as a signal that finally meets the standard if any two of the three quality standards are met.
Another embodiment of the present invention further provides a near-infrared brain function imaging quality control system, including:
The near-infrared imaging module is used for acquiring original light intensity data and converting the original light intensity data into optical density data;
The standard deviation calculation module is used for converting the optical density data into optical density time series data and calculating the standard deviation of each sliding window by adopting a sliding window method for the optical density time series data;
The abnormal time point determining module is used for determining an abnormal time point according to the standard deviation data of the normal distribution;
The first judging module is used for selecting proper optical density time sequence length according to the abnormal time point data and judging whether the optical density time sequence data preliminarily meet the requirements or not according to the length of the time sequence.
The control system further comprises a secondary judgment module, and the secondary judgment module is used for carrying out signal-to-noise ratio detection, channel concentration signal correlation analysis and power spectrum heartbeat peak detection on the optical density time sequence data which preliminarily meet the quality requirement.
The above embodiments are only for illustrating the present invention, and all the steps and the like can be changed, and all the equivalent changes and modifications based on the technical scheme of the present invention should not be excluded from the protection scope of the present invention.
Claims (10)
1. A near-infrared brain function imaging quality control method is characterized by comprising the following steps:
1) Converting original light intensity data acquired by a near-infrared imaging device into optical density data;
2) Recording the optical density data changing along with time to obtain an optical density time sequence, and calculating the standard deviation of each sliding window by adopting a sliding window method for the optical density time sequence data;
3) Assuming that the standard deviation of the sliding window obeys normal distribution, determining a numerical range formed by adding or subtracting a preset number of standard deviations from the normal distribution mean of the standard deviation as a normal value range, and judging data points outside the normal value range as abnormal points;
4) Acquiring density time series data of all the test channels, and if the abnormal point occurs when a time point exceeds a preset number of the test channels, determining the time point as an abnormal time point;
5) and intercepting a time sequence between two adjacent abnormal time points with the farthest distance from the plurality of detected abnormal time points, and judging whether the optical density time sequence data preliminarily meet the requirements according to the length of the time sequence.
2. The method for controlling quality of near-infrared brain function imaging according to claim 1, wherein in the step 5), the method for determining whether the optical density time-series data meets the requirement is as follows: if the time sequence length is greater than or equal to a preset time length, determining that the intercepted optical density time sequence data preliminarily meets the quality requirement; and if the time sequence length is less than the preset time length, the optical density time sequence data does not meet the quality requirement.
3. The method according to claim 1 or 2, wherein the optical density time-series data meeting the quality requirement is subjected to signal-to-noise ratio detection, channel concentration signal correlation analysis and power spectrum heartbeat peak detection.
4. the method according to claim 3, wherein the optical density time-series data satisfy two or more conditions selected from a signal-to-noise ratio not less than a predetermined value, a positive correlation of the concentration signals of the channels, and a peak value of a heartbeat in the power spectrum, and the optical density time-series data are considered to satisfy the quality requirement.
5. The method of claim 3, wherein the SNR detection comprises: and respectively calculating the signal-to-noise ratio of each test channel under different wavelengths to reflect the signal acquisition quality acquired by each test channel by defining the signal-to-noise ratio of the signal through the average value of the original light intensity time sequence data and the standard deviation ratio of the light density time sequence data.
6. the method of claim 3, wherein the analysis of correlation of test channel concentration signals comprises: normalizing the optical density time-series data which preliminarily meet the quality requirement, removing high-frequency noise and low-frequency drift through a 0.01-0.1Hz band-pass filter, converting the filtered data into oxyhemoglobin, deoxyhemoglobin and total hemoglobin concentration data according to a modified Beer-Lambert law, selecting the concentration data needing to calculate a signal correlation matrix, and calculating the concentration data time series of all the test channels which are correlated pairwise to obtain a corresponding concentration correlation coefficient matrix.
7. The near-infrared brain function imaging quality control method according to claim 3, wherein the power spectrum heartbeat peak detection includes: performing primary band-pass filtering of 0-3Hz on the optical density time-series data which preliminarily meet the quality requirement, and converting the filtered data into time series of oxyhemoglobin concentration and deoxyhemoglobin concentration according to a modified Beer-Lambert law;
Resampling the time series of the oxyhemoglobin concentration and the deoxyhemoglobin concentration to 5Hz, and then performing band-pass filtering of 0.01-2Hz on the time series of the resampled oxyhemoglobin concentration and deoxyhemoglobin concentration;
carrying out Fourier transform on the time series of the filtered oxyhemoglobin concentration and the filtered deoxyhemoglobin concentration, converting the time series of the oxyhemoglobin concentration and the deoxyhemoglobin concentration from a time domain signal to a frequency domain signal, and obtaining amplitudes corresponding to the frequencies of the oxyhemoglobin concentration and the deoxyhemoglobin concentration;
And performing modulus extraction on the amplitudes of the oxygenated hemoglobin concentration frequency curves and the deoxygenated hemoglobin concentration frequency curves, then performing square operation to obtain power corresponding to the frequencies, performing normalization on the power corresponding to the frequencies to obtain normalized power spectrums, and displaying the corresponding power spectrums with the frequencies of 0.5-1.5Hz of each test channel.
8. The method according to claim 1 or 2, wherein in the step 4), if the abnormal time point occurs within a plurality of seconds before the time series or within a plurality of seconds after the time series, the abnormal time point is determined as an instable data acquisition time point; and if the abnormal time point appears in the rest time periods, the abnormal time point is a motion artifact time point.
9. A near-infrared brain function imaging quality control system, comprising:
The near-infrared imaging module is used for acquiring original light intensity data and converting the original light intensity data into optical density data;
the standard deviation calculation module is used for converting the optical density data into optical density time series data and calculating the standard deviation of each sliding window by adopting a sliding window method for the optical density time series data;
The abnormal time point determining module is used for determining an abnormal time point according to the standard deviation data of the normal distribution;
and the first judgment module is used for selecting proper optical density time sequence length according to the abnormal time point data and judging whether the optical density time sequence data preliminarily meet the requirements or not according to the length of the time sequence.
10. the near-infrared brain function imaging quality control system according to claim 9, further comprising a secondary judgment module for performing signal-to-noise ratio detection, channel concentration signal correlation analysis and power spectrum heartbeat peak detection on the optical density time-series data preliminarily meeting the quality requirement.
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| CN116807414A (en) * | 2023-08-31 | 2023-09-29 | 慧创科仪(北京)科技有限公司 | Assessment method and device for near infrared brain function imaging signal quality |
| CN116807414B (en) * | 2023-08-31 | 2023-12-29 | 慧创科仪(北京)科技有限公司 | Assessment method and device for near infrared brain function imaging signal quality |
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