Oesophageal cancer and colon cancer have five year survival rates of 15% and 63% respectively. These low survival rates are due in part to poor early detection during endoscopic screening, with conventional endoscopes providing insufficient information about tissue properties to spot a wide range of potential tumours. Improving early detection of gastrointestinal cancers would dramatically increase their five year survival rates. Spatial Frequency Domain Imaging (SFDI) is a low-cost imaging technique that can measure absorption, scattering and shape as potential indicators of cancer. Specific absorption and scattering properties are known to be linked to malignancy in the oesophagus, and shape is an important indicator in colon cancer. Though a range of research and commercial SFDI systems have been developed, adapting these for in vivo clinical application is challenging due to constraints imposed by miniaturisation, sample geometry and illumination conditions. To facilitate design of novel SFDI systems under such constraints, we have developed a model of an SFDI imaging system built on the open-source 3D modelling software Blender. Using Blender’s Cycles ray-tracing engine, we are able to simulate a range of different scattering and absorption coefficients for a number of different imaging configurations, sample geometries and illumination patterns. Using established processing algorithms, we show we can recover maps of absorption, scattering and shape in a range of simulated ex vivo and in vivo imaging geometries with relevance to clinical detection of tumours. Our system enables accessible exploration of different optical configurations and realistic illumination conditions that will inform future design of compact, low-cost instruments.
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