Clinical Feasibility of High-Resolution Brain Perfusion Imaging using Deep Learning 3D Arterial Spin Labeling

Motivation: Some clinical applications require adaptation from the default ASL recommendations to consider specific cohort needs - like epilepsy, pediatrics or tumor evaluation. Goal(s): Evaluate the feasibility and utility of high-resolution ASL-based perfusion. Approach: Full-brain background-suppressed PCASL with a 3D GRASE segmented readout, CAIPIRINHA acceleration and deep learning reconstruction was evaluated in the clinical setup with a target resolution of 2.5mm isotropic. Results: The results demonstrate the feasibility of high-resolution ASL and its diagnostic efficacy to identify areas of abnormal perfusion activity. Deep learning reconstruction increased image sharpness while preserving SNR in equivalent acquisition times under 5 minutes. Impact: We show that high-resolution ASL perfusion imaging is feasible in clinical practice and can help detect areas of abnormal perfusion. Deep learning reconstruction techniques can further enhance image resolution and reduce scan times to under 5 minutes without compromising SNR.