Abstract #2192

Super-resolution GAN network for fast quantification of proton density mapping from highly accelerated synthetic magnetic resonance imaging

Yawen Liu¹, Pengling Ren², Hongxia Yin³, Yi Zhu⁴, Rong Wei⁵, Tingting Zhang², Zuofeng Zheng⁶, and Zhenchang Wang^1,2

¹School of Biological Science and Medical Engineering, Beihang University, Beijing, China, ²Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China, ³Department of Medical Engineering, Beijing Friendship Hospital, Capital Medical University, Beijing, China, ⁴Philips Healthcare, Beijing, China, ⁵Peking university Academy for Advanced Interdisciplinary Studies, Beijing, China, ⁶Beijing ChuiYangLiu Hospital, Beijing, China

Synopsis

Keywords: MR Fingerprinting/Synthetic MR, Image ReconstructionQuantitative magnetic resonance imaging (qMRI) can reflect the inherent characteristics of human tissue of relaxation time and proton density, and has important value for clinical diagnostic. However, long scan times limit the use of qMRI. We propose a method to optimize fast qMRI using a super-resolution generative adversarial network, thereby reducing scan time and obtaining accurate quantitative values. The results showed that this method was able to improve the image quality of qMRI, and the quantitative values were not significantly different from those obtained in conventional acquisitions.

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