Name
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/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10470-6_32 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Convergence (routing),Algorithm,Ground truth,Regularization (mathematics),Inverse problem,Dirichlet distribution,Small set,Convex optimization,Mathematics,Laplace operator | Conference | 8674 |
Issue | ISSN | Citations |
Pt 2 | 0302-9743 | 4 |
PageRank | References | Authors |
0.52 | 15 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sébastien Tourbier | 1 | 19 | 1.40 |
| Xavier Bresson | 2 | 1842 | 68.08 |
| P. Hagmann | 3 | 511 | 35.38 |
| Jean-Philippe Thiran | 4 | 2320 | 257.56 |
| Reto Meuli | 5 | 296 | 107.65 |
| Meritxell Bach Cuadra | 6 | 326 | 23.59 |