Meeting Banner
Abstract #3571

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

Hongli Fan1,2, Pan Su1, Yang Li1, Peiying Liu1, Jay J. Pillai1,3, and Hanzhang Lu1,2,4
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 scan2-6. 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.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here