Frequency domain (FD) diffuse optical spectroscopy (DOS) can be used to recover absolute optical properties of biological tissue, providing valuable clinical feedback, including in diagnosis and monitoring of breast tumours. In this study, tomographic (3D) and topographic (2D) techniques for spatially-varying optical parameter recovery are presented, based on a multi-distance, handheld DOS probe. Processing pipelines and reconstruction quality are discussed and quantitatively compared, demonstrating the trade-offs between depth sensitivity, optical contrast, and computational speed. Together, the two techniques provide both depth sensitive real-time feedback, and high-resolution 3D reconstruction from a single set of measurements, enabling faster and more accurate clinical feedback.
Frequency-domain near-infrared spectroscopy (FD-NIRS) can measure absolute tissue optical properties for functional brain imaging. Multiple source-detector separations (SDS), and multiple modulation frequencies can be used for FD-NIRS application in homogeneous or multi-layer tissue models. A multiple-frequency approach is advantageous in building more compact systems with a single SDS. In this work, we compare the accuracy of estimating absolute optical properties of a multi-layered tissue model using multi-distance and multi-frequency approaches. We demonstrate that the multiple-frequency approach is comparable in accuracy to the multiple-distance approach and can be confidently implemented for brain imaging applications.
An intuitive and generalisable approach to spatial-temporal feature extraction for brain-computer interface (BCI) with high-density functional near-infrared spectroscopy (fNIRS) data is proposed, demonstrated here with frequency-domain (FD) signals for motor-task classification. Statistical analysis of the results shows that the spatially resolved convolutional neural network (CNN) model improves classification accuracy by 2.5% compared to a standard temporal CNN, further enhanced by data availability. This is a significant improvement considering the requirements of real-time BCI.
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