Calibrationless Reconstruction of Uniformly Undersampled Multi-channel MR data with Deep learning Estimation of ESPIRiT Maps
Junhao Zhang1,2, Zheyuan Yi1,2, Yujiao Zhao1,2, Linfang Xiao1,2, Jiahao Hu1,2, Christopher Man1,2, Yujiao Zhao3, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China, 3The University of HongKong, HongKong, China
Conventional ESPIRiT reconstruction requires accurate estimation of ESPIRiT maps from autocalibration samples or signals but acquiring such autocalibration signals takes time and may not be straightforward in some situations. This study aims to deploy deep learning to directly estimate ESPIRiT maps from uniformly undersampled multi-channel 2D MR data that contain no autocalibration signals. Results show that the estimated ESPIRiT maps could be reliably obtained and they could be used for ESPIRiT and SENSE reconstruction with high acceleration.
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