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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here