Coupled dictionary learning for multi-contrast MRI reconstruction

Pingfan Song, Lior Weizman, João F. C. Mota, Yonina C. Eldar, Miguel Raul Dias Rodrigues

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)
64 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2018 25th IEEE International Conference on Image Processing (ICIP)
Number of pages5
ISBN (Electronic)9781479970612
Publication statusPublished - 6 Sept 2018
Event25th IEEE International Conference on Image Processing 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018


Conference25th IEEE International Conference on Image Processing 2018
Abbreviated titleIEEE ICIP 2018
Internet address


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