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Abstract #2749

High spatio-temporal resolution 3D ASL renal perfusion with variable-density FSE and deep-learning reconstruction

Manuel Taso1, Uri Wollner2, Arnaud Guidon3, Rafi Barda2, Christopher J Hardy4, Sangtae Ahn4, and David C Alsop1
1Division of MRI research, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States, 2GE Research, Herzliya, Israel, 3Global MR Applications and Workflow, GE Healthcare, Boston, MA, United States, 4GE Research, Niskayuna, NY, United States

Arterial spin labeling (ASL) has proven to be a powerful research and clinical technique for functional imaging of tissues. The combination of undersampled acquisitions and compressed sensing reconstruction shows promise for increased speed, resolution, and robustness but conventional CS reconstructions are slow and may not be satisfactory, especially for low SNR data. This work explores the feasibility and performance of Deep-Learning based reconstruction of similarly sampled data to realize the full potential of these ASL acquisitions using DCI-net, an unenrolled iterative CS network. We show DCI-net performance at high acceleration rates and potential for fast volumetric ASL perfusion imaging.

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