Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction.

IEEE Transactions on Medical Imaging
Pingfan SongMiguel R D Rodrigues

Abstract

Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities in either structure level or gray level. In this paper, we propose a coupled dictionary learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage the dependency correlation between different contrasts for guided or joint reconstruction from their under-sampled k -space data. Our approach iterates between three stages: coupled dictionary learning, coupled sparse denoising, and enforcing k -space consistency. The first stage learns a set of dictionaries that not only are adaptive to the contrasts, but also capture correlations among multiple contrasts in a sparse transform domain. By capitalizing on the learned dictionaries, the second stage performs coupled sparse coding to remove the aliasing and noise in the corrupted contrasts. The third stage enforces consistency between the denoised contrasts and the measurements in the k -space domain. Numerical experiments, consisting of retrospective under-sampling of various MRI contrasts with a variet...Continue Reading

Citations

Nov 28, 2020·Physics in Medicine and Biology·Bragi SveinssonDaehyun Yoon
May 11, 2021·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·Haikun QiClaudia Prieto
May 11, 2021·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·Simon R ArridgeKris Thielemans
May 30, 2021·Magnetic Resonance Imaging·Xinwen LiuFeng Liu
Jul 14, 2021·Journal of Medical Imaging and Radiation Oncology·Shekhar S ChandraStuart Crozier

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