Abstract #3571

Whole-brain CBF and BAT mapping in 4 minutes using deep-learning-based, multi-band MR fingerprinting (MRF) ASL

Hongli Fan^1,2, Pan Su¹, Yang Li¹, Peiying Liu¹, Jay J. Pillai^1,3, and Hanzhang Lu^1,2,4

¹The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins School of Medicine, Baltimore, MD, United States, ²Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, United States, ³Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, United States, ⁴F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Arterial-Spin-Labeling (ASL) MRI has not been used widely in clinical practice because of lower SNR and the lack of ability to resolve cerebral-blood-flow (CBF) from bolus-arrival-time (BAT) effects¹. MR fingerprinting (MRF) ASL is a recently developed technique which has the potential to provide multiple parameters such as CBF, BAT, T₁ and cerebral-blood-volume (CBV) in one single scan^2-6. However, it still suffers from low SNR. The present work proposes a multi-band MRF-ASL in combination with deep learning, which can improve the reliability of MRF-ASL parametric maps up to 3-fold and provide whole-brain mapping of CBF and BAT in 4 minutes.

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