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Abstract #3831

Rapid $$$T_{2}^{*}$$$ and Susceptibility Mapping using Poisson Wave Encoding and Model-Based Reconstruction

Xiaoqing Wang1, Jaejin Cho2, Yohan Jun2, Berkin Bilgic3, and José P. Marques4
1Department of Radiology, Computational Radiology Laboratory, Boston Children's Hospital, and Harvard Medical School, Boston, MA, United States, 2Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School., Boston, MA, United States, 3Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and Harvard Medical School., Charlestown, MA, United States, 4Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands

Synopsis

Keywords: Pulse Sequence Design, Data Acquisition, model-based recontruction; T2* mapping; QSM

Motivation: Harmonization of quantitative $$$T_{2}^{*}$$$ and susceptibility mapping is of critical interest in research and clinical studies, yet there are no available open-source, harmonized acquisition/reconstruction strategies.

Goal(s): To develop open-source sequences and reconstructions for quantitative $$$T_{2}^{*}$$$ and susceptibility mapping with high acceleration.

Approach: A 3D multi-echo GRE sequence with wave encoding was implemented on Pulseq. Model-based reconstruction was employed to estimate quantitative maps directly from undersampled k-space.

Results: Wave encoding improves quantitative mapping at R=12. While SENSE is more flexible with the choice of sampling patterns, model-based reconstruction performs best with wave-poisson sampling. The latter further improves $$$B_{0}$$$ mapping with less phase warps.

Impact: Open-source acquisition with advanced wave encoding and reconstruction have been implemented, which has the potential to facilitate the harmonization of highly-accelerated quantitative $$$T_{2}^{*}$$$, $$$B_{0}$$$ and susceptibility mapping.

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