  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.

Computer Vision and Pattern Recognition;Image and Video Processing