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Abstract #2237

Regularized Reconstruction Using Redundant Haar Wavelets: A Means to Achieve High Under-Sampling Factors in Non-Contrast-Enhanced 4D MRA

Jun Liu1, Jeremy Rapin1, Ti-chiun Chang1, Peter Schmit2, Xiaoming Bi3, Alban Lefebvre1, Michael Zenge2, Edgar Mueller2, Mariappan S. Nadar1

1Siemens Corporate Research, Princeton, NJ, United States; 2Siemens AG, Healthcare Sector, Erlangen, Germany; 3Siemens Medical Solutions USA Inc., Chicago, IL, United States

Non-contrast-enhanced 4D intracranial MR angiography (NCE 4D MRA) is a promising non-invasive technique for visualization of vascular anatomy. In this paper, a parallel imaging method is proposed that makes use of a regularization based on 4D redundant Haar wavelet transformation, which allows for incorporating both spatial and temporal structures. NCE 4D MRA images were reconstructed from k-space data that was under-sampled according to a spiral phyllotaxis pattern. Our results show that excellent results can be achieved with an acceleration rate of even 13.7.