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Combining Multimodal Information for Metal Artefact Reduction: An Unsupervised Deep Learning Framework

Ranzini, MBM; Groothuis, I; Klaser, K; Cardoso, MJ; Henckel, J; Ourselin, S; Hart, A; (2020) Combining Multimodal Information for Metal Artefact Reduction: An Unsupervised Deep Learning Framework. In: Proceedings - International Symposium on Biomedical Imaging. (pp. pp. 600-604). IEEE: Iowa City, IA, USA. Green open access

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Abstract

Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images. In Computed Tomography (CT), supervised deep learning approaches have been shown effective but limited in generalisability, as they mostly rely on synthetic data. In Magnetic Resonance Imaging (MRI) instead, no method has yet been introduced to correct the susceptibility artefact, still present even in MAR-specific acquisitions. In this work, we hypothesise that a multimodal approach to MAR would improve both CT and MRI. Given their different artefact appearance, their complementary information can compensate for the corrupted signal in either modality. We thus propose an unsupervised deep learning method for multimodal MAR. We introduce the use of Locally Normalised Cross Correlation as a loss term to encourage the fusion of multimodal information. Experiments show that our approach favours a smoother correction in the CT, while promoting signal recovery in the MRI.

Type: Proceedings paper
Title: Combining Multimodal Information for Metal Artefact Reduction: An Unsupervised Deep Learning Framework
Event: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
ISBN-13: 9781538693308
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ISBI45749.2020.9098633
Publisher version: https://doi.org/10.1109/ISBI45749.2020.9098633
Language: English
Additional information: Metal Artefact Reduction, CT, MR, Deep Learning, Unsupervised Learning
Keywords: Metal Artefact Reduction, CT, MR, Deep Learning, Unsupervised Learning
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 Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Ortho and MSK Science
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
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10102817
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