Song, P;
Weizman, L;
Mota, JFC;
Eldar, YC;
Rodrigues, MRD;
(2018)
Coupled Dictionary Learning for Multi-contrast MRI Reconstruction.
In:
2018 25th IEEE International Conference on Image Processing (ICIP).
IEEE
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Abstract
Medical imaging tasks often involve multiple contrasts, such as T1-and T2-weighted magnetic resonance imaging (MRI) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities. In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage an available guidance contrast to restore the target contrast. Our approach consists of three stages: coupled dictionary learning, coupled sparse denoising, and k-space consistency enforcing. The first stage learns a group of dictionaries that capture correlations among multiple contrasts. By capitalizing on the learned adaptive dictionaries, the second stage performs joint sparse coding to denoise the corrupted target image with the aid of a guidance contrast. The third stage enforces consistency between the denoised image and the measurements in the k-space domain. Numerical experiments on the retrospective under-sampling of clinical MR images demonstrate that incorporating additional guidance contrast via our design improves MRI reconstruction, compared to state-of-the-art approaches.
| Type: | Proceedings paper |
|---|---|
| Title: | Coupled Dictionary Learning for Multi-contrast MRI Reconstruction |
| Event: | 2018 25th IEEE International Conference on Image Processing (ICIP), 7-10 October 2018, Athens, Greece |
| ISBN-13: | 978-1-4799-7061-2 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICIP.2018.8451341 |
| Publisher version: | https://doi.org/10.1109/ICIP.2018.8451341 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | multi-contrast MRI, coupled dictionary learning, coupled sparse denoising, guidance information |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10061954 |
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