Isotropic multichannel total variation framework for joint reconstruction of multicontrast parallel MRI
Erfan Ebrahim Esfahani

TL;DR
This paper introduces an isotropic multichannel total variation regularizer for joint multicontrast MRI reconstruction, improving artifact suppression and contrast detail preservation over existing methods.
Contribution
It develops a novel isotropic regularizer integrated into a convex optimization framework for multicontrast parallel MRI, enhancing image quality and reducing contrast leakage.
Findings
Outperforms state-of-the-art methods in rotation-invariance and artifact suppression.
Prevents intercontrast leakage of contrast-specific details.
Effective in fast, undersampled MRI protocols.
Abstract
Purpose: To develop a synergistic image reconstruction framework that exploits multicontrast (MC), multicoil, and compressed sensing (CS) redundancies in magnetic resonance imaging (MRI). Approach: CS, MC acquisition, and parallel imaging (PI) have been individually well developed, but the combination of the three has not been equally well studied, much less the potential benefits of isotropy within such a setting. Inspired by total variation theory, we introduce an isotropic MC image regularizer and attain its full potential by integrating it into compressed MC multicoil MRI. A convex optimization problem is posed to model the new variational framework and a first-order algorithm is developed to solve the problem. Results: It turns out that the proposed isotropic regularizer outperforms many of the state-of-the-art reconstruction methods not only in terms of rotation-invariance…
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