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Abstract #4789

A New Deep Learning Structure for Improving Image Quality of a Low-field Portable MRI System

WENCHUAN MU1, Liang Zheng2, Danial C. Alexander3, Jia Gong1, Wenwei Yu4, and Shao Ying Huang1,5

1Engineering Product Development, Singapore University of Technology and Design, SINGAPORE, Singapore, 2Information Systems Technology and Design, Singapore University of Technology and Design, SINGAPORE, Singapore, 3Centre for Medical Image Computing and Dept. Computer Science, UCL, London, United Kingdom, 4Center for Frontier Medical Engineering, Chiba University, Chiba, Japan, 5Department of Surgery, National University of Singapore, Singapore, Singapore

A permanent magnet based low-field MRI system provides portability and affordability. However, the quality of the image is low due to a low signal-to-noise ratio (SNR). We propose a new deep learning structure which effectively integrates denoising-networks end-to-end to super-resolution-networks, to transfer the rich information available from one-off experimental imaging from a mid-field MRI scanner (1.5T) to the lower-quality data from a portable system. The procedure uses matched pairs to learn mappings from low-quality to the corresponding high-quality images. Using the proposed method, the quality and resolution of an image from a low-field MRI system is significantly improved.

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