Based on model-driven fast iterative shrinkage thresholding network for bioluminescence tomography reconstruction

Abstract

Bioluminescence tomography (BLT) is an effective noninvasive molecular imaging modality, it has shown great potential for studying and monitoring disease progression in pre-clinical imaging. As the BLT is an inherent highly ill-posed inverse problem, it is still a challenge to obtain an accurate reconstruction result. Some algorithms have been proposed to solve highly ill posedness of inverse problems. Nevertheless, Existing methods always need to consume large time or have low interpretability. Thus, in this paper, we proposed a novel model-driven deep learning network, which unfolding the Fast Iterative Shrinkage Thresholding Algorithm (FISTA) algorithm into a deep network, named FISTA-Net to overcome the above shortcoming. FISTA-Net is formed from three modules, gradient descent module, proximal mapping module and accelerate module. Key parameters of FISTA-Net including the gradient step size, thresholding value are learned from training data. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can achieve a high-quality reconstruction result of BLT.