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Abstract #1173

Myocardial T1 Mapping with Synthetic Image Estimation Based Motion Correction

Hui Xue1, Saurabh Shah2, Andreas Greiser3, Christoph Guetter1, Christophe Chefdhotel1, Marie-Pierre Jolly1, Sven Zuehlsdorff2, Jens Guehring1, Peter Kellman4

1Imaging & Visualization, Siemens Corporate Research, Princeton, NJ, United States; 2CMR Research & Development, Siemens Medical Solutions USA, Inc., Chicago, IL, United States; 3Imaging & IT Division, Siemens AG, Healthcare Sector, Erlangen, Germany; 4National Heart, Lung & Blood Institute, National Institutes of Health, Bethesda, MD, United States

The state-of-art technique for cardiac T1 mapping is the modified Look-Locker Inversion Recovery (MOLLI) which acquires multiple images across several heart-beats. Its clinical applicability is often limited by frequent myocardial motion because of imperfect breath-hold or varying R-R interval. A fully automated motion correction directly utilizing MOLLI images is highly challenging due to significantly varying image contrast. We therefore propose a novel registration algorithm based on estimating motion-free synthetic images presenting similar contrast to original MOLLI data by solving a variational energy minimization problem. The validation was performed in vivo on a large cohort of patient datasets.