A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913242401472512 |
|---|---|
| author | Hong, Tao Hernandez-Garcia, Luis Fessler, Jeffrey A. |
| author_facet | Hong, Tao Hernandez-Garcia, Luis Fessler, Jeffrey A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_02586 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI Hong, Tao Hernandez-Garcia, Luis Fessler, Jeffrey A. Optimization and Control Numerical Analysis Image and Video Processing Signal Processing 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. |
| title | A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI |
| topic | Optimization and Control Numerical Analysis Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2303.02586 |