Functional near-infrared spectroscopy techniques, in the form of either optical topography (OT) or diffuse optical tomography (DOT), can non-invasively recover the hemodynamic changes occurring in the activated cerebral cortex. In comparison with the traditional OT that provides a less quantitative absorption perturbation map along the subject domain surface, a successful DOT has ability to quantify depth-resolved information that relies on abundant boundary overlapping measurements using a high-density (HD) source-detector array. To achieve a trade-off between the temporal resolution and sensitivity by channel cross-talk suppression, a hybrid frequency- and time-division-multiplexing strategy have to be normally adopted to the HD-DOT implementation, where the temporal resolution degradation due to the multi-field illuminations might still prevent from capturing the high frequency information. In this work, a deep-learning based pre-OT method has been proposed to improve the temporal resolution of HD-DOT. The pre-OT could provide prior information on activation regions to exclude measurements of non-sensitive data. We have performed simulation and phantom experiments to evaluate the performances of the proposed method, and demonstrated its superiority over the stand-alone HD-DOT in improving both the temporal resolution and localization accuracy.
Near-infrared spectroscopy (NIRS) has received extensive attention in the field of brain functional investigation because of its noninvasiveness, safety and environmental adaptation. Nevertheless, this modality still demands an enhancement in the measurement reliability and the channel availability for broader applications and quantitative accomplishment. In this study we have developed a three-wavelength, 240-channel continuous-wave NIRS-DOT system of lock-in photoncounting mode. The system combines high-superiority of the lock-in detection in noise suppression and parallelism capability with ultra-high sensitivity of the photon-counting technology, and provides 20 source-fiber optodes connecting to their respective three-wavelength laser diode sets and 12 detection-fiber optodes connecting to their respective photoncounting photomultiplier-tubes. The light intensity can be automatically adjusted according to the custom configurations for an optimal operation. The system has been validated using a series of static and dynamic phantom experiments, demonstrating appealing performances in stability, linearity, anti-noise, inter-channel crosstalk and temporal resolution.
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.
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