Magnetic resonance imaging manifestations of cerebral small vessel disease: automated quantification and clinical application

Abstract

The common cerebral small vessel disease (CSVD) neuroimaging features visible on conventional structural magnetic resonance imaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. The CSVD neuroimaging features have shared and distinct clinical consequences, and the automatic quantification methods for these features are increasingly used in research and clinical settings. This review article explores the recent progress in CSVD neuroimaging feature quantification and provides an overview of the clinical consequences of these CSVD features as well as the possibilities of using these features as endpoints in clinical trials. The added value of CSVD neuroimaging quantification is also discussed for researches focused on the mechanism of CSVD and the prognosis in subjects with CSVD.

Chronic kidney disease and its association with cerebral small vessel disease in the general older hypertensive population

“…Brain atrophy can be measured using different visual scales, as the Pasquier scale or the Koedam score. Brain atrophy can also be assessed using automatic brain tissue segmentation (for example of gray matter or of white matter) [ 70 ]. In addition, the pathophysiological geneses behind brain atrophy are diverse, and different pathological processes in the central nervous system can lead to brain atrophy.…”

Assessment of cheese sign and its association with vascular risk factors: Data from PUMCH dementia cohort

“…Our future research will aim to explore cheese sign in different diseases, such as Alzheimer's disease, vascular dementia, and hereditary cerebral small vessel disease. In addition, high-field MRI, advanced neuroimaging quantification, [23] and more autopsy studies can prompt the determination of the underlying pathological mechanism.…”

MixLacune: Segmentation of lacunes of presumed vascular origin

“…Lacunes are hard to find, because of their small size, versatile shape, and sparse occurrence in the brain [1,3,4]. For this reason, a two-stage segmentation approach was chosen.…”

“…They are visualized as a hypointense cavity on Fluidattenuated inversion recovery (FLAIR) and T1-weighted imaging, usually with a hyperintense rim on FLAIR imaging [1,3]. Quantification of lacunes relies on manual annotation or semi-automatic / interactive approaches; and almost no automatic methods exist for this task [3,4]. Initial work by Ghafoorian et al [5] presented a method for detection of lacunes with a deep multi-scale locationaware 3D convolutional neural network (CNN).…”