Sparse-angle optical projection tomography based on multi-layer sparsity and deep image priors

Abstract

Optical projection tomography (OPT) is a computational imaging technique to acquire the volumetric images of biological samples ranging from millimeters to centimeters. For in-vivo OPT, it is essential to minimize the inspection time to reduce the adverse impacts on organisms, including the anesthetic side effect and phototoxicity. It can be achieved by projecting the samples from equally spaced sparse angles, but this method will induce radial artifacts in the reconstructed tomographic images. This paper develops a high-quality reconstruction method for sparse-angle OPT by jointly exploiting the multi-layer sparsity prior and deep image prior (DIP) on the volumetric images. The DIP module works in an unsupervised manner without requirement on a training dataset. This method can also address the inter-layer correlation within the samples, and process multi-layer images in parallel to improve the reconstruction accuracy and efficiency. Simulations and experiments demonstrate the superiority of the proposed method over some widely used reconstruction algorithms for sparse-angle OPT.