Multi-tasking to Correct: Motion-Compensated MRI via Joint Reconstruction and Registration
| Autor: | Martin J. Graves, Guy B. Williams, Veronica Corona, Carole Le Guyader, Noémie Debroux, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb |
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| Přispěvatelé: | Corona, Veronica [0000-0003-2160-5482], Graves, Martin [0000-0003-4327-3052], Williams, Guy [0000-0001-5223-6654], Apollo - University of Cambridge Repository |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 2D registration Nonlinear elasticity Fidelity Motion (geometry) 030218 nuclear medicine & medical imaging Image (mathematics) Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Joint model FOS: Mathematics Computer vision Mathematics - Numerical Analysis media_common ComputingMethodologies_COMPUTERGRAPHICS business.industry Work (physics) Contrast (statistics) Numerical Analysis (math.NA) Magnetic Resonance Imaging Term (time) Weighted total variation Motion correction Artificial intelligence Reconstruction business Joint (audio engineering) 030217 neurology & neurosurgery |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030223670 SSVM |
| Popis: | This work addresses a central topic in Magnetic Resonance Imaging (MRI) which is the motion-correction problem in a joint reconstruction and registration framework. From a set of multiple MR acquisitions corrupted by motion, we aim at - jointly - reconstructing a single motion-free corrected image and retrieving the physiological dynamics through the deformation maps. To this purpose, we propose a novel variational model. First, we introduce an $L^2$ fidelity term, which intertwines reconstruction and registration along with the weighted total variation. Second, we introduce an additional regulariser which is based on the hyperelasticity principles to allow large and smooth deformations. We demonstrate through numerical results that this combination creates synergies in our complex variational approach resulting in higher quality reconstructions and a good estimate of the breathing dynamics. We also show that our joint model outperforms in terms of contrast, detail and blurring artefacts, a sequential approach. 12 pages, 3 figure, accepted for publication in Scale Space and Variational Methods in Computer Vision conference proceedings |
| Databáze: | OpenAIRE |
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