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Time-resolved Diffuse Optical Tomography is a valuable tool to localize and characterize heterogeneities inside a biological tissue. Novel strategies based on structured light illumination and compressive-sensing detection have been exploited to reduce the dataset while preserving the information content. In this work, we present a setup based on those strategies implementing an adaptive scheme based on Singular-Value Decomposition (SVD) to generate a set of optimal input and output patterns.