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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