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Abstract #4701

Deep Learning Based Adaptive Noise Reduction in Multi-Contrast MR Images

Kensuke Shinoda1, Kenzo Isogawa2, Masahito Nambu1, Yuichi Yamashita1, Mika Kitajima3, Hiroyuki Uetani3, and Yasuyuki Yamashita3

1MRI System Division, Canon Medical Systems Corporation, Otawara-shi, Japan, 2Corporate Research and Development Center, Toshiba Corporation, Kawasaki-shi, Japan, 3Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto-shi, Japan

We have proposed a deep learning-based approach for MR image denoising that can adapt to the input noise power. We compare the performance of the proposed denoise approach with Deep Learning based Reconstruction (dDLR) method with state-of-the art image denoising method called Block-matching and 3D filtering method (BM3D) on multiple contrast MR images. Our experiments demonstrate that the proposed method outperforms the state-of-the art BM3D image denoising method.

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