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Abstract #2619

Neuromelanin-sensitive MRI using deep learning reconstruction (DLR) denoising: comparison of DLR patterns

Sonoko Oshima1, Yasutaka Fushimi1, Satoshi Nakajima1, Akihiko Sakata1, Takuya Hinoda1, Sayo Otani1, Krishna Pandu Wicaksono1, Hiroshi Tagawa1, Yang Wang1, Yuichiro Sano2, Rimika Imai2, Masahito Nambu2, Koji Fujimoto3, Hitomi Numamoto4, Kanae Kawai Miyake4, Tsuneo Saga4, and Yuji Nakamoto1
1Department of Diagnostic Radiology and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan, 2Canon Medical Systems Corporation, Otawara, Japan, 3Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan, 4Department of Advanced Medical Imaging Research, Graduate School of Medicine, Kyoto University, Kyoto, Japan

We applied four patterns of deep learning reconstruction (DLR) denoising methods to 1 NEX neuromelanin-sensitive MR images. DLR with denoising intensity coefficient of 1.0 and edge enhancement off provided the best image quality among the four types of DLR, and it was significantly better than or as good as 5 NEX images. ROC analyses using images with all DLR patterns showed good AUCs for diagnosis of Parkinson’s disease. This DLR denoising method can improve image quality of neuromelanin-sensitive MRI with good diagnostic ability to differentiate patients with Parkinson’s disease from healthy controls.

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