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Abstract #1786

Fast quantitative T2 mapping at ultra-high field using sparse sampling and dictionary-based reconstruction

Jochen Schmidt1,2, Dvir Radunsky3, Patrick Scheibe1, Noam Ben-Eliezer3,4,5, Robert Trampel1, and Nikolaus Weiskopf1,6
1Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2International Max Planck Research School on Neuroscience of Communication: Function, Structure and Plasticity, Leipzig, Germany, 3Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, 4Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, 5Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, NY, United States, 6Felix Bloch Institute for Solid State Physics, Leipzig University, Leipzig, Germany

Synopsis

Keywords: Quantitative Imaging, Sparse & Low-Rank ModelsQuantitative T2 mapping at ultra high-field via multi-echo spin-echo acquisitions suffers from bias introduced by pronounced transmit field inhomogeneities. Hence, accurate signal modeling is vital. Futhermore, high-resolution imaging results in longer scan times, which need to be compensated by k-space undersampling. Conventional undersampling schemes for multi-echo approaches skip identical phase encoding areas for all echo readouts. Image reconstruction quality might be improved by varying the sampling across echoes and using a low rank reconstruction approach. A multi-echo acquisition combining sparse undersampling with a dictionary modelling method was successfully used to acquire accurate T2 at 7T with a high acceleration.

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Keywords