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Abstract #4697

Quantitative R2 Map Synthesis from T1W and T2W Images Using Generative Adversarial Networks for Parkinson's Disease

Jingzhi Wu1,2, Chi Xiong3, Ying Yang1, Wen Sun4, Ying Liu1, Peng Wang1, Aiping Liu5, Yidong Yang2, Chaoshi Niu3, Wei Wei1, and Jie Wen1,2
1Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China, 2Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China, 3Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China, 4Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China, 5School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China

Synopsis

Keywords: Parkinson's Disease, Parkinson's Disease, R2*;Generative Adversarial Networks

Motivation: R2* mapping is important for measuring iron content in Parkinson's disease. However, conventional methods for obtaining R2* maps, involving multi-echo-gradient-recalled-echo sequences acquisition and image reconstruction, are both time-consuming and computationally intensive.

Goal(s): Our goal is to synthesize accurate R2* maps quickly using a Generative Adversarial Network (GAN).

Approach: A GAN model was developed to synthesize R2* maps from T1W and T2W images. The performance of synthetic R2* maps was evaluated using both internal and external datasets.

Results: The synthetic R2* maps showed excellent performance in diagnosing and assessing Parkinson's disease across all test datasets, demonstrating the feasibility of our proposed GAN model.

Impact: The generated R2* maps show good correlations with real R2* maps and have the potential to provide valuable information about the disease process. Our study provides a promising start for PD diagnosis, assessment and monitoring using synthetic R2* maps.

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