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Solving heterogenous region for diffuse optical tomography with a convolutional forward calculation model and the inverse neural network

Diffuse optical tomography (DOT) is a noninvasive biomedical imaging method to reconstruct optical property distribution. Since the underdetermined characteristic of reconstruction process, a priori information such as the structure provided by multimodal images are beneficial for imaging quality. We introduce a deep convolutional neural network-based method to rapidly calculate the heterogenous region by the diffusive intensity distribution measured by the same device used for DOT imaging. The process is based on a convolutional forward model which can accurately calculate the diffusive light intensity distribution with known structure and corresponding optical properties. The heterogeneous region imaging network is the inverse of the forward model and trained with Monte Carlo simulation results. The trained inverse network achieves the imaging sensitivity and specificity of 0.91 and 0.89 for validation data-set and the reconstruction speed is under 0.1s peer image.