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Abstract #4692

Fast and Automatic Rank Determination (ARD) via Deep Learning for Low-rank Calibrationless Reconstruction

Jiahao Hu1,2,3, Zheyuan Yi1,2,3, Yujiao Zhao1,2, Christopher Man1,2, Linfang Xiao1,2, Vick Lau1,2, Shi Su1,2, Ziming Huang1,2, Junhao Zhang1,2, Alex T.L. Leong1,2, Fei Chen3, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China, 3Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China


Low-rank matrix completion has emerged as a potent reconstruction approach for calibrationless parallel imaging. However, in all existing low-rank reconstruction methods, the rank threshold must be carefully chosen slice by slice in a manual and trial-and-error manner, severely hindering the adoption of low-rank reconstruction in routine clinical applications. To tackle the problem, we proposed a fast and automatic rank determination via deep learning. It directly determines optimal rank from undersampled k-space data by exploiting coil sensitivity and finite image support. Our proposed method enables fast, automatic and robust rank determination for all existing calibrationless reconstruction using low-rank matrix completion.

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