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Abstract #3860

Enhanced Sodium Imaging at 3T MRI Using BSRGAN: Achieving High SNR and Spatial Resolution

Sangwoo Kim1, Sukhoon Oh2, Sang-Young Kim3, Jinwoo Hwang3, Chae Young Lim4, Yongsik Sim4, Eunju Kim3, Eun Sook Ko4, Woo Kyoung Jeong4, Sung Tae Kim4, and Jung Hee Lee4
1Department of Radiological Science, Daewon University College, Jecheon, Korea, Republic of, 2Center for Bio-Imaging and Translational Research, Korea Basic Science Institute, Cheongju-si, Korea, Republic of, 3Philips Healthcare Korea, Seoul, Korea, Republic of, 4Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea, Republic of

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: Sodium MRI offers valuable cellular level insights but faces limitations due to low signal-to-noise ratio (SNR) and low spatial resolution.

Goal(s): This study is to enhance the quality of sodium MRI images by applying BSRGAN, with the aim of achieving an improved signal-to-noise ratio through denoising and doubling the spatial resolution.

Approach: This study presents a blind super-resolution generative adversarial network (BSRGAN) reconstruction technique applied to 3T sodium images.

Results: This approach simulates ultra-high field sodium imaging, effectively narrowing the SNR and resolution gap between 3T and 7T systems, with a fourfold reduction in scan time.

Impact: BSRGAN-based reconstruction of sodium images achieved a 1.4-fold increase in signal-to-noise ratio and a twofold improvement in spatial resolution at 3T, significantly enhancing image quality and reducing acquisition time.

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Keywords