We developed a time domain near infrared optical tomography (TD NIROT) system for tissue imaging. To facilitate its evaluation, we created a dynamic tissue-mimicking phantom platform characterized by fluctuating internal optical properties and arbitrary anomaly shapes. Using water-soluble materials, we 3D printed and embedded shapes within silicone mixtures, and created the corresponding internal hollow structure after curing and submerging the phantom in water. By introducing liquid solutions of varying dye concentrations, we controlled the optical properties of the internal shape over time. The phantom was measured with our TD NIROT system. The internal shape was accurately imaged and the local dynamic changes introduced by the pumping system was revealed . These results show a great potential of such phantom systems for applications in assessing various optical imaging systems.
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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