Abstract #0686

Highly Accelerated MPRAGE Imaging of the Brain Incorporating Deep Learning Priors with Subject-Specific Novel Features

Yue Guan¹, Yudu Li^2,3, Ziyu Meng^3,4, Tianyao Wang⁵, Rong Guo^2,3, Ruihao Liu⁴, Yao Li⁴, Yiping Du⁴, and Zhi-Pei Liang^2,3

¹Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ²Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴Shanghai Jiao Tong University, Shanghai, China, ⁵Department of Radiology, The Fifth People's Hospital of Shanghai, Shanghai, China

MPRAGE imaging has been widely used in clinical applications and various attempts have been made for its acceleration. This paper presents a new method to accelerate MPRAGE imaging using sparse and random sampling of k-space and constrained reconstruction incorporating image priors and subject-specific novel features. In our current implementation, the MPRAGE image priors were obtained using deep learning on data from the Human Connectome Project, and novel localized features were recovered by solving a sparsity-constrained reconstruction. In vivo experimental results demonstrated that the proposed method can produce high-quality whole-brain MPRAGE images in 0.7x0.7x0.7 mm3 nominal resolution from a 1.5-min scan.

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