Compressed MRI reconstruction exploiting a rotation-invariant total variation discretization.
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| Titel: | Compressed MRI reconstruction exploiting a rotation-invariant total variation discretization. |
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| Autoren: | Esfahani EE; School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, P.O. Box 14115-175, Iran. Electronic address: ebrahim.esfahani@ut.ac.ir., Hosseini A; School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, P.O. Box 14115-175, Iran. Electronic address: hosseini.alireza@ut.ac.ir. |
| Quelle: | Magnetic resonance imaging [Magn Reson Imaging] 2020 Sep; Vol. 71, pp. 80-92. Date of Electronic Publication: 2020 Apr 14. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 8214883 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-5894 (Electronic) Linking ISSN: 0730725X NLM ISO Abbreviation: Magn Reson Imaging Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2008->: Amsterdam : Elsevier Original Publication: New York : Pergamon, c1982- |
| MeSH-Schlagworte: | Algorithms* , Magnetic Resonance Imaging*, Image Processing, Computer-Assisted/*methods, Humans ; Rotation |
| Abstract: | Inspired by the first-order method of Malitsky and Pock, we propose a new variational framework for compressed MR image reconstruction which introduces the application of a rotation-invariant discretization of total variation functional into MR imaging while exploiting BM3D frame as a sparsifying transform. In the first step, we provide theoretical and numerical analysis establishing the exceptional rotation-invariance property of this total variation functional and observe its superiority over other well-known variational regularization terms in both upright and rotated imaging setups. Thereupon, the proposed MRI reconstruction model is presented as a constrained optimization problem, however, we do not use conventional ADMM-type algorithms designed for constrained problems to obtain a solution, but rather we tailor the linesearch-equipped method of Malitsky and Pock to our model, which was originally proposed for unconstrained problems. As attested by numerical experiments, this framework significantly outperforms various state-of-the-art algorithms from variational methods to adaptive and learning approaches and in particular, it eliminates the stagnating behavior of a previous work on BM3D-MRI which compromised the solution beyond a certain iteration. (Copyright © 2020 Elsevier Inc. All rights reserved.) |
| Contributed Indexing: | Keywords: Compressed sensing; Denoising; First-order methods; Iterative image reconstruction; Magnetic resonance imaging (MRI); Variational image processing |
| Entry Date(s): | Date Created: 20200418 Date Completed: 20210129 Latest Revision: 20210129 |
| Update Code: | 20250114 |
| DOI: | 10.1016/j.mri.2020.03.008 |
| PMID: | 32302736 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Inspired by the first-order method of Malitsky and Pock, we propose a new variational framework for compressed MR image reconstruction which introduces the application of a rotation-invariant discretization of total variation functional into MR imaging while exploiting BM3D frame as a sparsifying transform. In the first step, we provide theoretical and numerical analysis establishing the exceptional rotation-invariance property of this total variation functional and observe its superiority over other well-known variational regularization terms in both upright and rotated imaging setups. Thereupon, the proposed MRI reconstruction model is presented as a constrained optimization problem, however, we do not use conventional ADMM-type algorithms designed for constrained problems to obtain a solution, but rather we tailor the linesearch-equipped method of Malitsky and Pock to our model, which was originally proposed for unconstrained problems. As attested by numerical experiments, this framework significantly outperforms various state-of-the-art algorithms from variational methods to adaptive and learning approaches and in particular, it eliminates the stagnating behavior of a previous work on BM3D-MRI which compromised the solution beyond a certain iteration.<br /> (Copyright © 2020 Elsevier Inc. All rights reserved.) |
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| ISSN: | 1873-5894 |
| DOI: | 10.1016/j.mri.2020.03.008 |