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Abstract #4620

CNN based Super-Resolution of Intravoxel Incoherent Motion Imaging for Liver

Jiqing Huang1, Jin Qin1, Lihui Wang1, Rongpin Wang2, Zi-Xiang Kuai3, Chen Ye1, Tianye Wang1, and Yuemin Zhu4

1Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Com-pute Science and Technology, Guizhou University, Guiyang, China, 2Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, China, 3Harbin Medical University Cancer Hospital, Harbin, China, 4Univ.Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France

We investigated the super-resolution reconstruction method for IVIM imaging based on convolution neural networks (CNN). Three-layers-CNN was constructed and trained firstly with a series of paired low- and high-resolution images, and then the super-resolution IVIM images were reconstructed with such network, the reconstruction quality was evaluated finally in terms of PSNR, SSIM, diffusivity, perfusion fraction and pseudo-diffusivity respectively. The results show that the CNN-based super resolution reconstruction for IVIM has a great performance and may enable IVIM to be analyzed with unprecedent resolution.

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