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.