Deep learning based image reconstruction for sparse-view diffuse optical tomography

Abstract

Three critical problems, including high-cost instrumentations, time-consuming image recovery, and low image quality, limit clinical applications of diffuse optical tomography (DOT). Image reconstruction based on deep learning can enhance the image quality of DOT, especially in the case of low number of measurements where image reconstruction becomes a more ill-posed and underdetermined problem. Here, we present a sparse-view image reconstruction based on deep learning to recover the absorption coefficient of a phantom. The presented neural network enhances image quality and the results show that deep learning can enhance the contrast to noise-ratio effectively (more than 80%). The presented method provides recovered images with more accurate localization capabilities (an increase in localization metric by more than 30% compared with the classic method). Moreover, the time of image reconstruction is reduced by three orders of magnitude. This method can be applied for single-source DOT that significantly reduces the cost of required instruments for brain imaging and cervical or thyroid cancer screening.