Functional near-infrared spectroscopy techniques, in the form of either optical topography (OT) or diffuse opticaltomography (DOT), can non-invasively recover the hemodynamic changes occurring in the activated cerebral cortex. Incomparison with the traditional OT that provides a less quantitative absorption perturbation map along the subjectdomain surface, a successful DOT has ability to quantify depth-resolved information that relies on abundant boundaryoverlapping measurements using a high-density (HD) source-detector array. To achieve a trade-off between the temporalresolution and sensitivity by channel cross-talk suppression, a hybrid frequency- and time-division-multiplexing strategyhave to be normally adopted to the HD-DOT implementation, where the temporal resolution degradation due to themulti-field illuminations might still prevent from capturing the high frequency information. In this work, a deep-learningbased pre-OT method has been proposed to improve the temporal resolution of HD-DOT. The pre-OT could provideprior information on activation regions to exclude measurements of non-sensitive data. We have performed simulationand phantom experiments to evaluate the performances of the proposed method, and demonstrated its superiority overthe stand-alone HD-DOT in improving both the temporal resolution and localization accuracy.
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