Medium-adaptive Compressive Diffuse Optical Tomography

Abstract

The low spatial resolution of diffuse optical tomography (DOT) has motivated the development of high-density DOT systems utilizing spatially-encoded illumination and detection strategies. To limit the size of the inverse problem in these systems, data compression methods, such as applying Fourier or Hadamard patterns, have been explored for both illumination and detection, but were largely limited to pre-determined patterns regardless of imaging targets. Here, we show that target-optimized detection patterns can yield significantly improved DOT reconstructions. Applying reciprocity, we can further iteratively optimize both illumination and detection patterns, and show that these simultaneously optimized source/detection patterns outperform pre-determined patterns in both simulations and phantom data. In addition, we report a target-adaptive wide-field DOT imaging system and test such system with phantom measurements.