SISTER: Spectral-Image Similarity-Based Tensor With Enhanced-Sparsity Reconstruction for Sparse-View Multi-Energy CT

Abstract

Multi-energy computed tomography (MCT) has a great potential in material decomposition, tissue characterization, lesion detection, and other applications. However, the severe noise that exists within projections makes it difficult to obtain high-quality MCT images. To overcome this limitation, we propose a method termed Spectral-Image Similarity-based Tensor with Enhanced-sparsity Reconstruction (SISTER) method. SISTER utilizes the non-local feature similarity in the spatial-spectral domain by clustering similar spatial-spectral patches within non-local window-to a 4th-order tensor group. Compared with the image gradient L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm with tensor dictionary learning (L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> TDL) method, by adopting tensor decomposition rather than tensor dictionary learning, SISTER overcomes the instability of tensor dictionary. Besides, in our SISTER method the weight coefficients update strategy is also optimized. Both numerical simulation and preclinical dataset were performed to evaluate and validate the performance of SISTER. Qualitative and quantitative results show that the proposed method can lead to a promising improvement of edge preservation, finer feature recovery, and noise suppression.