Abstract #0678

Rapid 3D T1 Mapping Using Deep Learning-Assisted Look-Locker Inversion Recovery MRI

Haoyang Pei^1,2, Ding Xia¹, Xiang Xu¹, Yang Yang^1,3, Yao Wang², Fang Liu⁴, and Li Feng¹

¹Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ²Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, NY, United States, ³Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, ⁴Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative ImagingLook-Locker inversion recovery (LLIR) imaging is an easy, accurate and reliable MRI method for T1 mapping. For 3D acquisition, LLIR imaging is usually performed with multiple repetitions, and additional idle time is placed between consecutive receptions. This idle time allows for signal recovery to improve SNR and ensures robustness to B1 inhomogeneity, but it also prolongs scan time. Simply eliminating the idle time reduces the accuracy of T1 quantification. In this work, a novel deep-learning approach was proposed to address this challenge, so that accurate 3D T1 maps can be generated from continuous 3D LLIR imaging without idle time.

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