Haifeng Wang1, Zhilang Qiu1,2, Shi Su1, Sen Jia1,2, Ye Li1, Xin Liu1, Hairong Zheng1, Dong Liang1,3. 1. Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China. 2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China. 3. Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Abstract
PURPOSE: To propose a parameter optimization framework on wave gradients of Wave-CAIPI imaging for decreasing g-factor penalty and reducing reconstruction artifacts. THEORY AND METHODS: The influences of parameters on g-factor are theoretically analyzed. The average g-factor is chosen as a metric for parameter optimization, and then a fast calculation method is proposed to approximately and ultra-fast calculate the average g-factor. Based on this, a set of points in the function of the average g-factor with respect to the wave gradient parameters is calculated, and the optimal wave gradient parameters are found according to these points. RESULTS: In vivo human brain experiments were performed on 3T MR scanners for the comparison experiments. The results show that the proposed parameter optimization framework is able to efficiently obtain optimal wave gradient parameters, which can achieve decreased g-factor penalty and less artifacts of reconstructions than the empirical parameters. CONCLUSION: The proposed parameter optimization framework is computationally efficient and can optimize the wave gradient parameters of Wave-CAIPI imaging for better image quality than before.
PURPOSE: To propose a parameter optimization framework on wave gradients of Wave-CAIPI imaging for decreasing g-factor penalty and reducing reconstruction artifacts. THEORY AND METHODS: The influences of parameters on g-factor are theoretically analyzed. The average g-factor is chosen as a metric for parameter optimization, and then a fast calculation method is proposed to approximately and ultra-fast calculate the average g-factor. Based on this, a set of points in the function of the average g-factor with respect to the wave gradient parameters is calculated, and the optimal wave gradient parameters are found according to these points. RESULTS: In vivo human brain experiments were performed on 3T MR scanners for the comparison experiments. The results show that the proposed parameter optimization framework is able to efficiently obtain optimal wave gradient parameters, which can achieve decreased g-factor penalty and less artifacts of reconstructions than the empirical parameters. CONCLUSION: The proposed parameter optimization framework is computationally efficient and can optimize the wave gradient parameters of Wave-CAIPI imaging for better image quality than before.