Fast and Calibrationless Low-Rank Parallel Imaging Reconstruction Through Unrolled Deep Learning Estimation of Multi-Channel Spatial Support Maps

• Published in: IEEE transactions on medical imaging Vol. 42; no. 6; pp. 1644 - 1655
• Main Authors: Yi, Zheyuan, Hu, Jiahao, Zhao, Yujiao, Xiao, Linfang, Liu, Yilong, Leong, Alex T. L., Chen, Fei, Wu, Ed X.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain; Brain - diagnostic imaging; Brain mapping; Brain slice preparation; Calibration; Coils; complex-valued network; Convolution; Datasets; Deep Learning; Estimation; Fast calibrationless reconstruction; High acceleration; Image Processing, Computer-Assisted - methods; Image reconstruction; Image resolution; Imaging; Iterative methods; low-rank parallel imaging; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Neuroimaging; Optimization; Robustness (mathematics); Sensitivity; Spatial discrimination learning; spatial support maps

Low-rank technique has emerged as a powerful calibrationless alternative for parallel magnetic resonance (MR) imaging. Calibrationless low-rank reconstruction, such as low-rank modeling of local k-space neighborhoods (LORAKS), implicitly exploits both coil sensitivity modulations and the finite spat...