An iterative reconstruction algorithm based on total nuclear variation for multi-channel EPRI

Abstract

Oxygen concentration in tumor is a key information for effective radiation therapy. Electron paramagnetic resonance imaging (EPRI) is an advanced oxygen imaging technique. However, in the case of multi-channel EPRI， traditional imaging methods including filtered back-projection (FBP) and optimization algorithm related to total variation (TV) only consider the features of individual channel images and ignore the similarities among images of each channel. Total nuclear variation encourages shared edge positions and consistent gradient directions across multi-channel images. In this study, we propose a data divergence constrained, total nuclear variation minimization model and its Chambolle-Pock solving algorithm. We validate the accuracy of the algorithm and analyze its convergence through simulation studies using mathematical phantoms that simulate T1-weighted EPRI. Furthermore, we investigate the influence of algorithm parameters on convergence rate and evaluate the algorithm’s capability for sparse reconstruction and noise suppression. The experimental results show that the total nuclear variation minimization algorithm promotes edge structure information and gradient direction within individual channels of images. As a result, this algorithm successfully preserves essential image features while achieving accurate image reconstruction.