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MRI super-resolution using multi-channel total variation

Brudfors, M; Balbastre, Y; Nachev, P; Ashburner, J; (2018) MRI super-resolution using multi-channel total variation. In: Nixon, M and Mahmoodi, S and Zwiggelaar, R, (eds.) Medical Image Understanding and Analysis: 22nd Conference, MIUA 2018, Southampton, UK, July 9-11, 2018, Proceedings. (pp. pp. 217-228). Springer: Cham, Switzerland. Green open access

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Abstract

This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast. The model recasts the recovery of high resolution images as an inverse problem, in which a forward model simulates the slice-select profile of the MR scanner. The paper introduces a prior based on multi-channel total variation for MRI super-resolution. Bias-variance trade-off is handled by estimating hyper-parameters from the low resolution input scans. The model was validated on a large database of brain images. The validation showed that the model can improve brain segmentation, that it can recover anatomical information between images of different MR contrasts, and that it generalises well to the large variability present in MR images of different subjects.

Type: Proceedings paper
Title: MRI super-resolution using multi-channel total variation
Event: MIUA: Annual Conference on Medical Image Understanding and Analysis, 9-11 July 2018, Southampton, UK
ISBN-13: 9783319959207
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-95921-4_21
Publisher version: https://doi.org/10.1007/978-3-319-95921-4_21
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Super-resolution, Multi-channel total variation, MRI, ADMM
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10060554
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