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arXiv · 1712.02000

Joint image edge reconstruction and its application in multi-contrast MRI

Yunmei Chen·Ruogu Fang·Xiaojing Ye
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Abstract

We propose a new joint image reconstruction method by recovering edge directly from observed data. More specifically, we reformulate joint image reconstruction with vectorial total-variation regularization as an $l_1$ minimization problem of the Jacobian of the underlying multi-modality or multi-contrast images. Derivation of data fidelity for Jacobian and transformation of noise distribution are also detailed. The new minimization problem yields an optimal $O(1/k^2)$ convergence rate, where $k$ is the iteration number, and the per-iteration cost is low thanks to the close-form matrix-valued shrinkage. We conducted numerical tests on a number multi-contrast magnetic resonance image (MRI) datasets, which show that the proposed method significantly improves reconstruction efficiency and accuracy compared to the state-of-the-arts.

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Yunmei Chen, Ruogu Fang, Xiaojing Ye. 2017-12-06. Joint image edge reconstruction and its application in multi-contrast MRI. https://arxiv.org/abs/1712.02000

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