Abstract
Real-time reconstruction in multi-contrast magnetic resonance imaging (MC-MRI) is very challenging due to the slow scanning and reconstruction process. In this study, we propose a novel algorithm to accelerate the MC-MRI reconstruction in the framework of compressed sensing. The problem is formulated as the minimization of the least square data fitting with joint total variation (JTV) regularization term. We first utilized the iterative reweighted least square (IRLS) framework to reformulate the problem. A joint preconditioner is dexterously designed to efficiently compute the inverse of large transform matrix at each iteration. We compared our algorithm with eight cutting-edge compressive sensing MRI algorithms on real MC-MRI dataset. Extensive experiments demonstrate that the proposed algorithm can achieve far better reconstruction performance than all other eight cutting-edge methods.
This work was partially supported by U.S. NSF IIS-1423056, CMMI-1434401, CNS-1405985.
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Keywords
- Conjugate Gradient
- Compressive Sense
- Sample Ratio
- Iterative Reweighted Little Square
- Linear Time Complexity
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Li, R., Li, Y., Fang, R., Zhang, S., Pan, H., Huang, J. (2015). Fast Preconditioning for Accelerated Multi-contrast MRI Reconstruction. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_84
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DOI: https://doi.org/10.1007/978-3-319-24571-3_84
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