Abstract
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 and multi-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.
Highlights
Multiple sclerosis (MS) is an immune-mediated demyelinating disease that affects both white matter (WM) and grey matter (GM)
MS plaques appear as areas of low-signal intensity and high-signal intensity compared with normal-appearing white matter (NAWM) on T1-weighted and T2-weighted sequences respectively
Three lesion filling algorithms were used for comparison purposes using T1-weighted images: the lesion automated preprocessing method (Chard et al, 2010), here referred to as LEAP; the filling method implemented in FSL (Battaglini et al, 2012), here referred to as FSL, and the non-local means inpainting method (Guizard et al, 2015), here referred to as NLMI
Summary
Multiple sclerosis (MS) is an immune-mediated demyelinating disease that affects both white matter (WM) and grey matter (GM). It is characterised pathologically by areas of inflammation, demyelination, axonal loss and gliosis scattered throughout the central nervous system. These pathological processes affect several quantitative MRI indices, and can be indirectly measured with advanced imaging methods. White matter plaques are relatively easy to detect using conventional MRI techniques, whereas grey matter lesions can be observed using specialised sequences, such as double inversion recovery (DIR). Lesions and atrophy are two interconnected aspects of the disease, linked to different disease mechanisms, and both are extremely important for MS studies
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