Junhao Zhang^{1,2}, Zheyuan Yi^{1,2}, Yujiao Zhao^{1,2}, Linfang Xiao^{1,2}, Jiahao Hu^{1,2}, Christopher Man^{1,2}, Yujiao Zhao^{3}, and Ed X. Wu^{1,2}

^{1}Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, ^{2}Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China, ^{3}The 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.

EMS explicitly characterizes coil sensitivity function of MR receiving system using autocalibration signals and apply to reconstruct undersampled data in image space. EMS are coil-dependent information and different subjects may have different coil-subject geometry. This inevitably brings variations in shareable EMS among different subjects. Therefore, there is necessity to incorporate coil-subject geometry parameters to carry out spatial alignment to minimize the variations of EMS among different subjects. The framework of the proposed method is summarized in

Multi-channel coil data used in this study comes from Calgary-Campinas Public Database

The EMS are coil-dependent and each subject has different coil-subject geometry, resulting in variation in the coil-specific EMS. Thus, we exploit the coil-subject geometry information to minimize the variations in ESPIRiT maps. Such prior information is conventionally neglected. Phase variations would also exist in scans among different subjects. Such phase changes were well preserved to some extent in our deep learning model by using a hybrid loss, one was related to minimizing the variations in coil-specific ESPIRiT sensitivity maps and another was related to preserve the phase variations.

ESPIRiT maps of dominant eigen values are estimated and used for MR image reconstruction. ESPIRiT maps corresponding to smaller eigen values can also be estimated and employed for MR image reconstruction in future study, which may further improve the image reconstruction performance, especially for ultra-high-field MRI with rapid phase variations.

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DOI: https://doi.org/10.58530/2022/4313