Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI [article]

L Kerem Senel, Toygan Kilic, Alper Gungor, Emre Kopanoglu, H Emre Guven, Emine U Saritas, Aykut Koc, Tolga Cukur
2017 arXiv   pre-print
A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that in turn can
more » ... grade reconstruction quality or reduce scan efficiency. To address this problem, a statistically-segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially, while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo T_2-weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.
arXiv:1710.00532v1 fatcat:qassmq5hu5ezzazxm2dacj3fi4