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 Huang^1,5

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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