Abstract #3583

Reconstruction of Quantitative Susceptibility Maps from the Phase of Susceptibility-Weighted Images Using a Deep Neural Network

Jun Li¹, Hongjian He¹, Yi-Cheng Hsu², and Jianhui Zhong^1,3

¹Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China, ²MR Collaboration, Siemens Healthcare Ltd, Shanghai, China, ³Department of Imaging Sciences, University of Rochester, Rochester, NY, United States

There is a need to obtain quantitative measures of tissue susceptibility in the form of susceptibility-weighted imaging (SWI). In this study, we used a deep neural network to generate QSM maps from SWI high pass (HP)–filtered phase images. Using the QSM maps reconstructed from mGRE data by iLSQR (mGRE iLSQR) as the ground truth, the QSM maps generated from SWI HP-filtered phase images by UNet (SWI UNet) resulted in lower residuals and a better performance in quantitative metrics compared with the QSM maps reconstructed from SWI HP-filtered phase images by iLSQR (SWI iLSQR).

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