Pattern recognition analysis of dynamic susceptibility contrast (DSC)‐MRI curves automatically segments tissue areas with intact blood–brain barrier in a rat stroke model: A feasibility and comparison study. Issue 5 (25th October 2019)

• Record Type: Journal Article
• Authors: Jin, Seokha; Han, SoHyun; Stoyanova, Radka; Ackerstaff, Ellen; Cho, HyungJoon
• Is Part Of: Journal of magnetic resonance imaging. Volume 51:Issue 5(2020)
• Page Start: 1369
• Page End: 1381
• Publication Date: 2019-10-25
• Subjects: BBB damage -- DSC‐MRI -- pattern‐recognition -- stroke model; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26949
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: The manual segmentation of intact blood–brain barrier (BBB) regions in the stroke brain is cumbersome, due to the coexistence of infarction, large blood vessels, ventricles, and intact BBB regions, specifically in areas with weak signal enhancement following contrast agent injection. Hypothesis: That from dynamic susceptibility contrast (DSC)‐MRI alone, without user intervention, regions of weak BBB damage can be segmented based on the leakage‐related parameter K 2 and the extent of intact BBB regions, needed to estimate K 2 values, determined. Study Type: Feasibility. Animal Model: Ten female Sprague–Dawley rats (SD, 200–250g) underwent 1‐hour middle carotid artery occlusion (MCAO) and 1‐day reperfusion. Two SD rats underwent 1‐hour MCAO with 3‐day and 5‐day reperfusion. Field Strength/Sequence: 7T; ADC and T1 maps using diffusion‐weighted echo planar imaging (EPI) and relaxation enhancement (RARE) with variable repetition time (TR), respectively. dynamic contrast‐enhanced (DCE)‐MRI using FLASH. DSC‐MRI using gradient‐echo EPI. Assessment: Constrained nonnegative matrix factorization (cNMF) was applied to the dynamic Δ R 2 * ‐curves of DSC‐MRI (<4 min) in a BBB‐disrupted rat model. Areas of voxels with intact BBB, classified by automated cNMF analyses, were then used in estimating K 1 and K 2 values, and compared with corresponding values from manually‐derived areas. Statistical Tests: Mean ± standard deviation of ΔT1 ‐differences between ischemic and healthy areas were displayed with unpaired Student's t ‐tests. Scatterplots were displayed with slopes and intercepts and Pearson's r values were evaluated between K 2 maps obtained with automatic (cNMF)‐ and manually‐derived regions of interest (ROIs) of the intact BBB region. Results: Mildly BBB‐damaged areas (indistinguishable from DCE‐MRI (10 min) parameters) were automatically segmented. Areas of voxels with intact BBB, classified by automated cNMF, matched closely the corresponding, manually‐derived areas when respective areas were used in estimating K 2 maps (Pearson's r = 0.97, 12 slices). Data Conclusion: Automatic segmentation of short DSC‐MRI data alone successfully identified areas with intact and compromised BBB in the stroke brain and compared favorably with manual segmentation. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1369–1381.