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Abstract #2410

Deep Learning Pathology Detection from Extremely Sparse K-Space Data

Linfang Xiao1,2, Yilong Liu1,2, Zheyuan Yi1,2,3, Yujiao Zhao1,2, Peiheng Zeng1,2, Alex T.L. Leong1,2, 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, 3Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China

Traditional MRI diagnosis consists of image reconstruction from k-space data and pathology identification in the image domain. In this study, we propose a strategy of direct pathology detection from extremely sparse MR k-space data through deep learning. This approach bypasses the traditional MR image reconstruction procedure prior to pathology diagnosis and provides an extremely rapid and potentially powerful tool for automatic pathology screening. Our results demonstrate that this new approach can detect brain tumors and classify their sizes and locations directly from single spiral k-space data with high sensitivity and specificity.

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