A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI

Autor:	Hong, Tao, Hernandez-Garcia, Luis, Fessler, Jeffrey A.
Rok vydání:	2023
Předmět:	Mathematics - Optimization and Control Electrical Engineering and Systems Science - Image and Video Processing Electrical Engineering and Systems Science - Signal Processing Mathematics - Numerical Analysis
Zdroj:	IEEE Transactions on Computational Imaging, 2024
Druh dokumentu:	Working Paper
DOI:	10.1109/TCI.2024.3369404
Popis:	Model-based methods are widely used for reconstruction in compressed sensing (CS) magnetic resonance imaging (MRI), using regularizers to describe the images of interest. The reconstruction process is equivalent to solving a composite optimization problem. Accelerated proximal methods (APMs) are very popular approaches for such problems. This paper proposes a complex quasi-Newton proximal method (CQNPM) for the wavelet and total variation based CS MRI reconstruction. Compared with APMs, CQNPM requires fewer iterations to converge but needs to compute a more challenging proximal mapping called weighted proximal mapping (WPM). To make CQNPM more practical, we propose efficient methods to solve the related WPM. Numerical experiments on reconstructing non-Cartesian MRI data demonstrate the effectiveness and efficiency of CQNPM. Comment: 26 pages, 26 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.02586 Zobrazit plný text záznamu View this record from Arxiv