These data were part of a project to design and validate a clinically relevant method to visually assess venules from magnetic resonance imaging (MRI) structural scans, conducted primarily by Miss. Angela C.C. Jochems, supervised by Dr. Maria del C. Valdés Hernández and Prof. Joanna M. Wardlaw.
Cerebral venules remain understudied in general, perhaps due to difficulties in visualising or differentiating venules from arterioles using MRI. Recent advances in MRI allow visualisation of cerebral venules since the deoxygenated venous blood provides an intrinsic contrast agent on T2*-weighted sequences such as gradient echo (GRE), and on susceptibility weighted imaging (SWI). This has led to several visual and computational venular quantification methods being described in individuals with multiple sclerosis, sporadic small vessel disease-related features, moyamoya disease, sickle cell anaemia, neurosarcoidosis, early Alzheimer’s disease and amnestic mild cognitive impairment, cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), healthy individuals, and hypertensive rats. These studies use different regions of interest (ROI), field strengths and venular quantification metrics. We extracted these data to give an overview of the state-of-art of the brain venular assessment methods developed up to July 2019 (date of completion of this project).
Jochems, Angela; Blair, Gordon; Stringer, Michael; Thrippleton, Michael; Clancy, Una; Chappell, Francesca; Brown, Rosalind; Jaime García, Daniela; Hamilton, Olivia; Morgan, Alasdair; Marshall, Ian; Hetherington, Kirstie; Wiseman, Stewart; MacGillivray, Tom; Valdés Hernández, Maria; Doubal, Fergus; Wardlaw, Joanna. (2020). Methods to quantify and visualize venules using structural magnetic resonance imaging scans, 2000-2019 [dataset]. University of Edinburgh. Department of Neuroimaging Sciences. Centre for Clinical Brain Sciences. https://doi.org/10.7488/ds/2755.
|Date made available||21 Jan 2020|
|Temporal coverage||1 Jan 2000 - 31 Jul 2019|