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Abstract #4040

Deep learning acceleration and reconstruction for single-channel signals

Yongquan Ye1, Zhongqi Zhang1, Eric Z Chen2, Xiao Chen2, Shanhui Sun2, and Jian Xu1
1United Imaging, Houston, TX, United States, 2United Imaging Intelligence, Cambridge, MA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionThe capacity of the deep learning ReconNet3D model for single-channel image reconstruction with highly under-sampled k-spaces is demonstrated. Without the need for coil sensitivity information, the proposed method can achieve an acceleration factor of 4 on a dual-channel VTC coil. Supporting high-factor acceleration with limited coil channels can be very beneficial for imaging with surface coils or preclinical animal scans.

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Keywords