@inbook{a266837f0eca41ecbe36e92c847501f4,
title = "Combining Multimodal Information for Metal Artefact Reduction: An Unsupervised Deep Learning Framework",
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.",
keywords = "CT, Deep Learning, Metal Artefact Reduction, MR, Unsupervised Learning",
author = "Ranzini, {Marta B.M.} and Irme Groothuis and Kerstin Klaser and Cardoso, {M. Jorge} and Johann Henckel and Sebastien Ourselin and Alister Hart and Marc Modat",
year = "2020",
month = apr,
doi = "10.1109/ISBI45749.2020.9098633",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "600--604",
booktitle = "ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging",
note = "17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 ; Conference date: 03-04-2020 Through 07-04-2020",
}