Time-encoded (TEnc) dynamic ASL angiography is a method that provides high SNR, dynamic information about the blood supply to the brain. However, as is commonly the case with all ASL-based methods, multiple repeat encodings are required to fully sample the information required to decode the dynamic angiographic data. Here we apply spatial sparsity and temporal smoothness constraints to reconstruct highly under-sampled TEnc data, and demonstrate that high fidelity, high resolution ASL angiography can be performed in a fraction of the time it takes using conventional methods.
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