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 Journal Article


Authors: Jin, S.; Han, S.; Stoyanova, R.; Ackerstaff, E.; Cho, H.
Article Title: 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
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 K2 and the extent of intact BBB regions, needed to estimate K2 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 (Formula presented.) -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 K1 and K2 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 K2 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 K2 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. © 2019 International Society for Magnetic Resonance in Medicine
Keywords: controlled study; disease classification; nonhuman; comparative study; animal experiment; animal model; automation; feasibility study; rat; blood brain barrier; dynamic contrast-enhanced magnetic resonance imaging; sprague dawley rat; cerebrovascular accident; image segmentation; echo planar imaging; pattern recognition; carotid artery obstruction; reperfusion; pattern-recognition; female; priority journal; article; bbb damage; dsc-mri; stroke model; non-negative matrix factorization
Journal Title: Journal of Magnetic Resonance Imaging
Volume: 51
Issue: 5
ISSN: 1053-1807
Publisher: Wiley Blackwell  
Date Published: 2020-05-01
Start Page: 1369
End Page: 1381
Language: English
DOI: 10.1002/jmri.26949
PUBMED: 31654463
PROVIDER: scopus
PMCID: PMC8566029
DOI/URL:
Notes: Article -- Export Date: 1 May 2020 -- Source: Scopus
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