The online version of this article (doi:10.1007/s00330-015-4008-5) contains supplementary material, which is available to authorized users.
Zhongzhao Teng and Wenjia Peng contributed equally to this work.
Although certain morphological features depicted by high resolution, multi-contrast magnetic resonance imaging (hrMRI) have been shown to be different between culprit and non-culprit middle cerebral artery (MCA) atherosclerotic lesions, the incremental value of hrMRI to define culprit lesions over stenosis has not been assessed.
Patients suspected with MCA stenosis underwent hrMRI. Lumen and outer wall were segmented to calculate stenosis, plaque burden (PB), volume (PV), length (PL) and minimum luminal area (MLA).
Data from 165 lesions (112 culprit and 53 non-culprit) in 139 individuals were included. Culprit lesions were larger and longer with a narrower lumen and increased PB compared with non-culprit lesions. More culprit lesions showed contrast enhancement. Both PB and MLA were better indicators than stenosis in differentiating lesion types (AUC were 0.649, 0.732 and 0.737 for stenosis, PB and MLA, respectively). Combinations of PB, MLA and stenosis could improve positive predictive value (PPV) and specificity significantly. An optimal combination of stenosis ≥ 50 %, PB ≥ 77 % and MLA ≤ 2.0 mm2 produced a PPV = 85.7 %, negative predictive value = 54.1 %, sensitivity = 69.6 %, specificity = 75.5 %, and accuracy = 71.5 %.
hrMRI plaque imaging provides incremental information to luminal stenosis in identifying culprit lesions.
• High resolution MRI provides incremental information in defining culprit MCA atherosclerotic lesions.
• Both plaque burden and minimum luminal area are better indicators than stenosis.
• An optimal combination includes stenosis ≥ 50 %, PB ≥ 77 % and MLA ≤ 2.0 mm2.
ESM 1 (DOCX 22 kb)330_2015_4008_MOESM1_ESM.docx
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- An assessment on the incremental value of high-resolution magnetic resonance imaging to identify culprit plaques in atherosclerotic disease of the middle cerebral artery
Adam J. Brown
Martin J. Graves
Jonathan H. Gillard
- Springer Berlin Heidelberg
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