Motion artefacts are an important but often disregarded problem in diffusion-weighted imaging, which can readily lead to corrupt diffusion model estimations. The new processing method proposed in this paper uses robust tensor estimation that is spatially informed to efficiently detect the most frequently occurring artefacts, namely those that result from head and cardiac motion. Simulations demonstrate that the method is more robust and accurate than previous methods. The tensor estimates are more accurate in motion artefact-free conditions, less sensitive to increases in artefact magnitude and more resistant to increasing artefact frequency. Evaluation with real diffusion-weighted (DW) imaging data shows that the method works excellently, even for datasets with a high degree of motion that otherwise need to be discarded. The method is not limited to diffusion tensor imaging but also yields objective artefact reflecting weights that can be used to inform subsequent processing or estimation of higher-order diffusion models.
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