Abstract

Near-infrared optical tomography (NIROT) is a non-invasive imaging technique based on diffuse propagation of light in turbid media. It has high sensitivity to tissue oxygenation, which is a vital biomarker. NIROT enables one to quantify and image oxygenation at a bed-side in clinics, thus complementing other imaging modalities. Time-domain (TD) NIROT systems benefit from time-of-flight (ToF) information, but are often affected by relatively low signal-to-noise ratio and long acquisition time. However, fast acquisition is needed for in vivo assessment of oxygenation. In this work we present a time-multiplexing approach which enables multiple-fold faster acquisition with high SNR, suitable for various ToF applications, including NIROT. We combine it with hybrid convolutional neural-network (hCNN) -enhanced reconstruction to achieve an impressive 11-fold increase in acquisition speed.

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