Journal
NEUROIMAGE
Volume 139, Issue -, Pages 376-384Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2016.06.053
Keywords
Error correction; Multiple sclerosis; Lesions; Segmentation errors; Artefacts; MRI
Funding
- National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/ UCL High Impact Initiative)
- EPSRC [EP/H046410/ 1, EP/H046410/1, EP/J020990/1, EP/K005278]
- MRC [MR/J01107X/1]
- NIHR Biomedical Research Unit (Dementia) at UCL
- NIHR BRC UCLH/UCL [BW.mn.BRC10269]
- Medical Research Council [MR/J01107X/1]
- UK Multiple Sclerosis Society [892/08]
- Brain Research Trust
- EPSRC [EP/J020990/1, EP/H046410/1] Funding Source: UKRI
- MRC [MR/J01107X/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/H046410/1, EP/J020990/1] Funding Source: researchfish
- Medical Research Council [MR/J01107X/1] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0509-10143] Funding Source: researchfish
Ask authors/readers for more resources
Multiple sclerosis lesions influence the process of image analysis, leading to tissue segmentation problems and biased morphometric estimates. Existing techniques try to reduce this bias by filling all lesions as normal-appearing white matter on T1-weighted images, considering each time-point separately. However, due to lesion segmentation errors and the presence of structures adjacent to the lesions, such as the ventricles and deep grey matter nuclei, filling all lesions with white matter-like intensities introduces errors and artefacts. In this paper, we present a novel lesion filling strategy inspired by in-painting techniques used in computer graphics applications for image completion. The proposed technique uses a five-dimensional (5D), patch-based (multi-modality andmulti-time-point), Non-Local Means algorithm that fills lesions with the most plausible texture. We demonstrate that this strategy introduces less bias, fewer artefacts and spurious edges than the current, publicly available techniques. The proposed method is modality-agnostic and can be applied to multiple time-points simultaneously. In addition, it preserves anatomical structures and signal-to-noise characteristics even when the lesions are neighbouring grey matter or cerebrospinal fluid, and avoids excess of blurring or rasterisation due to the choice of the segmentation plane, shape of the lesions, and their size and/or location. (C) 2016 The Authors. Published by Elsevier Inc.
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