Micro-CT guided deep neural network for 3D reconstructions in widefield diffuse optical tomography

Reconstructions in 3D widefield Diffuse Optical Tomography (DOT) suffer from poor spatial resolution. Therefore, widefield DOT techniques benefit from incorporating structural priors from a complementary modality, such as the micro-CT. Unfortunately, traditional Laplacian-based methods to integrate the priors in the inverse problem are highly time-consuming. Therefore, we propose a Deep Neural Network based end-to-end inverse solver that combines features from AUTOMAP and Z-net and utilizes the micro-CT priors in the training stage. Initial in silico and experimental phantom results demonstrate that the proposed network accurately reconstructs, in 3D, the absorption contrast with a high resolution.