ISMRM 2012 Prelim. Abstracts Oct 17, 2011 – Jason Su

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ISMRM 2012 Prelim. Abstracts Oct 17, 2011 – Jason Su kT points with DESPOT1 mapping @ 7T Observed modest improvements in B1 homogeneity with 1ch kT pts. Data problems: only collected 4 angles in vivo, barely gets over Ernst angle even with 2x overflipping according to AFI Correction with a B1 map is better than kT points alone Need get back and quantify improvement Try to apply kT+B1 correction with our AFI data (meeting with Ives about this today) 3T experiments? May not have enough B1 inhomogeneity to show improvement with kT+B1 correction over just B1 correction Accelerated DESPOT1 Mapping with View Sharing View sharing with proper scaling accelerates collection of SPGR DESPOT angles LCAMP may go even faster but still some work to be done MSmcDESPOT – baseline and 1yr MS study Not much new since last time even with full 1yr set for normals Progressive patients have greater increase in DV than CIS or RR TBSS? DEV/CISmcDESPOT – longitudinal MS studies with 1-3 month sampling interval Christine and Nora are now editing lesion segmentation Potential questions: How does MWF/DV in a lesion change over time? Greater shifts in EDSS than MSmcDESPOT, potential for more interesting longitudinal correlations

Accelerated DESPOT1 Mapping with View Sharing Introduction: DESPOT1 allows fast whole brain mapping of T1 with a collection of SPGR angles; typically only ~4 angles are needed, collected many more because aiming for mcDESPOT (how to spin this?); energy scaled view sharing provides a way to accelerate dynamic time series data -> consider the flip angle dimension as such Methods: 3T, phantom and in vivo, 110x110x40 (2x2x4mm), SPGR angles, 1:1:13, manipulated fully sampled raw data to simulate undersampling patterns Results Conclusion: View sharing reconstructs the SPGR images with good accuracy (1%); T1 maps have systematic errors in CSF regions (need to quantify this); hope benefits follows through in mcDESPOT collection Need to redo subtraction images w.r.t. to offline.recon, need to do histograms in parenchyma only. Should I discuss flip angle dependence of errors? Need to compare against other CS related developments in this area?