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Abstract #0843

Comparison of MR Fingerprinting ASL with Gd-based DSC MRI: validation and direct parametric mapping with deep learning

Pan Su1,2, Peiying Liu1, Yang Li1, Ye Qiao1, Jun Hua1,3, Doris Lin1, Jay J. Pillai1,4, Argye E. Hillis5,6,7, and Hanzhang Lu1,3

1The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 2Graduate School of Biomedical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States, 3F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, United States, 4Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 5Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 6Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 7Cognitive Science, Johns Hopkins University, Baltimore, MD, United States

MR Fingerprinting (MRF)-based Arterial-Spin-Labeling (ASL) has been recently proposed as a new approach to measure multiple hemodynamic parameters in a single scan. However, the previous implementation of MRF-ASL lacks the comparison with clinical standard techniques such as dynamic-susceptibility-contrast (DSC). Therefore, in this work, we validated MRF-ASL by comparing with DSC MRI. The results showed that these two methods provided visually comparable and quantitatively correlated perfusion estimations. Furthermore, we sought to directly estimate DSC-equivalent parameters from the MRF-ASL raw data using a deep-learning (DL) approach. DL-derived maps show better quality and are more consistent with DSC maps, compared to dictionary-matching results.

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