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Abstract #2166

Deep unrolled network with optimal sampling pattern to accelerate multi-echo GRE acquisition for quantitative susceptibility mapping

Jinwei Zhang1, Hang Zhang1, Pascal Spincemaille2, Mert Sabuncu3, Thanh Nguyen2, Ilhami Kovanlikaya2, and Yi Wang2
1Cornell University, New York, NY, United States, 2Weill Cornell Medical College, New York, NY, United States, 3Cornell University, Ithaca, NY, United States

To accelerate the acquisition time of quantitative susceptibility mapping (QSM) using a 3D multi-echo gradient echo (mGRE) sequence, an unrolled multi-channel deep ADMM reconstruction network with a LOUPE-ST based 2D variable density sampling pattern optimization module is trained to optimize both the k-space under-sampling pattern and the reconstruction. Prospectively under-sampled k-space data are acquired using a modified mGRE sequence and reconstructed by the trained unrolled network. Prospective study shows the learned sampling pattern achieves better image quality in QSM compared to a manually designed pattern.

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