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BACKGROUND: Dynamic susceptibility contrast (DSC)-MRI is a commonly used perfusion imaging technique used to assess brain tumor vascularity and angiogenesis by estimation of relative cerebral blood volume (rCBV). Despite widespread use, technical limitations lead to compromises in terms of resolution, slice coverage, and temporal resolution when acquiring dynamic data using the current single-shot echoplanar (EPI) imaging approach. In the current study we demonstrate the use of a new “multiband” (MB)-EPI sequence for multislice excitation, originally developed for fMRI as part of the Human Connectome Project, to obtain DSC perfusion MRI with whole brain coverage, higher spatial resolution, and twice the temporal resolution of current strategies. METHODS: DSC data collected using standard EPI were compared with DSC obtained using MB-EPI in 20 prospective GBM patients. Standard DSC-MRI were collected on conventional 1.5T or 3T MR scanners using a temporal resolution of 1250-1900ms, matrix size = 96x96–128x128, and 5-20 slices with 5mm slice thickness. High spatiotemporal DSC-MRI data were collected on a 3T Siemens Prisma with temporal resolution of 600-750ms, matrix size = 100x100–200x200, and 30-32 slices with 4mm slice thickness. Both DSC-MRI datasets were post-processed using standard leakage correction and deconvolution techniques to calculate rCBV and rCBF. RESULTS: Data collected using standard and MB-EPI showed similar dynamic contrast-to-noise, although MB-EPI showed more T1-weighting and leakage effects. RCBV maps obtained using both techniques provided similar information regarding the extent of hypervascularity within the tumor after leakage correction; however, rCBV maps calculated from MB-EPI data had more brain coverage and higher spatial resolution. Parametric maps of blood flow differed slightly due to higher temporal resolution MB-EPI data. CONCLUSION: High spatiotemporal DSC perfusion MRI is desirable and clinically feasible using MB-EPI. DSC using MB-EPI provides comparable quantitative data and data quality with better accuracy and spatial resolution.
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Ellingson et al. (2015) studied this question.
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