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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