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- W2105890341 abstract "Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)-based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n2) and (O(1/sqrt{varepsilon})), while existing techniques are in O(1/n) and O(1/ε). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy." @default.
- W2105890341 created "2016-06-24" @default.
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- W2105890341 date "2014-01-01" @default.
- W2105890341 modified "2023-09-30" @default.
- W2105890341 title "Efficient Total Variation Algorithm for Fetal Brain MRI Reconstruction" @default.
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- W2105890341 doi "https://doi.org/10.1007/978-3-319-10470-6_32" @default.
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