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Abstract #0886

 Extension of MR-STAT to non-Cartesian and gradient-spoiled sequences

Oscar van der Heide1,2, Alessandro Sbrizzi1,2, Tom Bruijnen1,3, and Cornelis van den Berg1,2
1Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, 2Department of Radiology, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, Netherlands, 3Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, Netherlands

MR-STAT is a framework for obtaining multi-parametric quantitative MR maps using data from single short scans. A single large-scale optimization problem is solved in which spatial localisation of signal and estimation of tissue parameters are performed simultaneously. In previous work, MR-STAT was presented using gradient-balanced sequences with linear, Cartesian readouts. To demonstrate the generic nature of the MR-STAT framework and to explore potentially more efficient acquisition schemes, we extend MR-STAT to non-Cartesian gradient trajectories as well as gradient-spoiled sequences. We compare the our results from golden angle radial, gradient-spoiled acquisitions to low-rank ADMM MRF reconstructions on the same data sets.

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