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Abstract #2494

Quality-Based UnwRap of SUbdivided Large Arrays (URSULA) at 9.4T

Johannes Lindemeyer1, Ana-Maria Oros-Peusquens1, Kaveh Vahedipour1, Nadim Jon Shah1, 2

1Institute of Neuroscience and Medicine 4, Forschungszentrum Jlich, Jlich, Germany; 2Department of Neurology, Faculty of Medicine, JARA, RWTH Aachen University, Aachen, Germany

Unwrapping of MR phase data becomes very time consuming for high-resolution data acquired at ultra-high fields. We present a new algorithm, URSULA (UnwRap of SUbdivided Large Arrays), an approach splitting up the original matrix in smaller-sized 3D volumes which are unwrapped individually. The computed subsets are assembled into a whole volume result using a quality-based approach. Sequential or parallel computing is applicable, especially the latter allowing for a dramatic gain in computing speed. Simulations show good reliability for a moderate number of subsets. The performance of the algorithm is exemplified on a human in vivo dataset measured at 9.4T.

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