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Abstract #3879

Accurate Estimation of Background Phase in Virtual Conjugate Coil Expansion Combined with Wave Encoding

Congcong Liu1, Zhuoxu Cui1, Sen Jia1, Zhilang Qiu2, Xin Liu1, Hairong Zheng1, Dong Liang1, and Haifeng Wang1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Case Western Reserve University, Cleveland, OH, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionThe wave encoding model with virtual conjugate coil (Wave-VCC) extension can provide more powerful MRI-accelerated imaging performance. However, estimating the background phase covering the full frequency range is un-tractable by only acquiring the middle auto-calibration signals (ACS) line during reconstruction. Here, combining a neural network without training, a new method to generate more accurate background phase in Wave-VCC is proposed. Including ablation experiments and comparison, experiments were carried out to verify the feasibility and performance of the proposed methods, respectively.

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Keywords