Near-infrared cerebral functional imaging quality control method and control system

Abstract

The invention belongs to the field of bran detection and relates to a near-infrared cerebral functional imaging quality control method and control system. The method comprises the steps as follows: original light intensity data acquired by a near-infrared imaging device are converted into light density data; the standard deviation of each sliding window is calculated by a sliding window method; abnormal points are judged; abnormal time points are determined according to abnormal points; among the detected multiple abnormal time points, light density data of each test channel between two adjacent abnormal time points at the farthest distance are intercepted; and if the time sequence length of light density time sequence data is larger than or equal to certain fixed time length, the intercepted light density time sequence data are determined to preliminarily meet the quality requirement. Preliminary quality control is performed according to intercepted time sequences with more stable signal quality, effects of abnormal time points on signal quality evaluation are effectively eliminated, and reliability of signal quality is guaranteed.

Description

technical field [0001] The invention relates to a near-infrared brain function imaging quality control method and a control system, belonging to the field of brain function testing. Background technique [0002] At present, there are mainly the following methods for evaluating the quality of signal data collected by near-infrared brain functional imaging technology: 1) Evaluate the signal quality according to the magnitude of the signal change in the concentration of oxyhemoglobin (HbO) and deoxygenated hemoglobin (HbR); 2) Evaluate the signal quality according to the correlation of the time series change trend of the concentration signals of HbO and HbR; 3) evaluate the signal quality according to the signal-to-noise ratio of the original light intensity data; 4) calculate the power spectrum according to the concentration signals of HbO and HbR, Look for heartbeat-related peaks to assess signal quality. [0003] Among them, method 2) cannot exclude the influence of noise s...

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Problems solved by technology

[0003] Among them, method 2) cannot rule out the influence of the noise signal in the concentration data of HbO and HbR on the time series trend, and the quality of the concentration signal data has no objective standard and relies on subjective judgment; several other methods evaluate the concentration signal The focus of quality is different, resulting in the use of only one method for quality control, which may cause errors in the quality evaluation of the signal

In addition, the above methods are all based on the quality evaluation of the signal based on a relatively complete time series, which cannot avoid the influence of the unstable time point of data acquisition or the time point of large motion artifacts on the signal quality, resulting in inaccurate concentration data signals

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