ArticlePloS one2020
Quantification of cerebral veins in patients with acute migraine with aura: A fully automated quantification algorithm using susceptibility-weighted imaging.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 11 citations in OpenAlex.
- Preliminary Study on the Application of Ultrahigh Field Magnetic Resonance in Moyamoya Disease.Oxidative medicine and cellular longevity · 2021Trial
- Vessel segmentation forMagnetic resonance in medicine · 2026Article
- Dynamic fluctuations in brain iron content during migraine attacks: insights from relaxometry and diffusion tensor imaging.Frontiers in neurology · 2024Article
- Prominent cerebral veins on susceptibility-weighted angiography in acute meningoencephalitis.Brain and behavior · 2023Article
- Structural MRI in Migraine: A Review of Migraine Vascular and Structural Changes in Brain Parenchyma.Journal of central nervous system disease · 2023Review
- Evaluating migraine with typical aura with neuroimaging.Frontiers in human neuroscience · 2023Review
- Imaging Neurovascular Uncoupling in Acute Migraine with Aura with Susceptibility Weighted Imaging.Clinical neuroradiology · 2021Article
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Authors and funding
10 authors at 1 institution in 1 country.
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Abstract
introductionSusceptibility weighted imaging (SWI) is a very sensitive technique that often depicts prominent focal veins (PFV) in patients with acute migraine with aura (MwA). Interpretation of visual venous asymmetry (VVA) between brain hemispheres on SWI may help support the clinical diagnosis of MwA. Our goal was to develop an automated algorithm for segmentation and quantification of cerebral veins using SWI. MATERIALS AND
methodsExpert readers visually evaluated SWI of patients with acute MwA for VVA. Subsequently a fully automated algorithm based on 3D normalization and 2D imaging processing using SPM and MATLAB image processing software including top-hat transform was used to quantify cerebral veins and to calculate volumetric differences between hemispheres.
resultsFifty patients with MwA were examined with SWI. VVA was present in 20 of 50 patients (40%). In 95% of patients with VVA, the fully automated calculation agreed with the side that visually harboured more PFV. Our algorithm showed a sensitivity of 95%, specificity of 90% and accuracy of 92% for detecting VVA. Patients with VVA had significantly larger vein volume on the hemisphere with more PFV compared to patients without (15.90 ± 5.38 ml vs 11.93 ± 5.31 ml; p = 0.013). The mean difference in venous volume between hemispheres in patients with VVA was larger compared to patients without VVA (16.34 ± 7.76% vs 4.31 ± 3.26% p < 1E-10). The average time between aura onset and SWI correlated negatively with venous volume of the dominant brain hemisphere (r = -0.348; p = 0.038).
conclusionA fully automated algorithm can accurately identify and quantify cerebral venous distribution on SWI. Absolute quantification may be useful for the future assessment of patients with suspected diseases, which may be associated with a unilateral abnormal degree of venous oxygenation.
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