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Joint Total Variation ESTATICS for Robust Multi-parameter Mapping

Balbastre, Y; Brudfors, M; Azzarito, M; Lambert, C; Callaghan, MF; Ashburner, J; (2020) Joint Total Variation ESTATICS for Robust Multi-parameter Mapping. In: Martel, A and Abolmaesumi, P and Stoyanov, D and Mateus, D and Zuluaga, M and Zhou, SK and Racoceanu, D and Joskowicz, L, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. pp. 53-63). Springer: Lima, Peru. Green open access

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Abstract

Quantitative magnetic resonance imaging (qMRI) derives tissue-specific parameters – such as the apparent transverse relaxation rate R ?2, the longitudinal relaxation rate R 1 and the magnetisation transfer saturation – that can be compared across sites and scanners and carry important information about the underlying microstructure. The multiparameter mapping (MPM) protocol takes advantage of multi-echo acquisitions with variable flip angles to extract these parameters in a clinically acceptable scan time. In this context, ESTATICS performs a joint loglinear fit of multiple echo series to extract R ?2 and multiple extrapolated intercepts, thereby improving robustness to motion and decreasing the variance of the estimators. In this paper, we extend this model in two ways: (1) by introducing a joint total variation (JTV) prior on the intercepts and decay, and (2) by deriving a nonlinear maximum a posteriori estimate. We evaluated the proposed algorithm by predicting left-out echoes in a rich single-subject dataset. In this validation, we outperformed other state-of-the-art methods and additionally showed that the proposed approach greatly reduces the variance of the estimated maps, without introducing bias.

Type: Proceedings paper
Title: Joint Total Variation ESTATICS for Robust Multi-parameter Mapping
Event: International Conference on Medical Image Computing and Computer-Assisted Intervention
ISBN-13: 9783030597122
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-59713-9_6
Publisher version: https://doi.org/10.1007/978-3-030-59713-9_6
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.
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 > 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/10107075
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