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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