Abstract #3020

Automatic assessment of motion artifact on Nigrosome 1 visualization protocol using CNN-LSTM

Junghwa Kang¹, Na Young Shin², and Yoonho Nam^1,2

¹Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, South Korea, yongin, Korea, Republic of, ²Seoul St.Mary’s Hospital, Department of Radiology, The Catholic University of Korea, Seoul, South Korea, Seoul, Korea, Republic of

We proposed an automatic evaluation model for estimating the degree of motion artifacts in high-resolution multi-echo gradient echo images for nigrosome-1 visualization in the substantia nigra. A combination of a convolutional neural network and a long short-term memory was used to develop the automatic motion evaluation model. The results demonstrated that the proposed model could be useful tools for N1 visualization for diagnosing Parkinson’s disease.

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