Abstract #1426

Feasibility of myelin water fraction mapping with denoised 2D multi-echo GRE images based on deep learned reconstruction

Ho-Joon Lee¹, Yeonah Kang¹, Marc Lebel², Jae Eun Song³, and Sung-Min Gho⁴

¹Department of Radiology, Haeundae Paik Hospital, Busan, Republic of Korea, ²MR Collaboration and Development, GE Healthcare, Calagary, AB, Canada, ³Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea, ⁴MR Collaboration and Development, GE Healthcare, Seoul, Republic of Korea

With advances in deep learning, feasibility has been investigated for myelin water fraction (MWF) reconstruction showing promising results, enabling fast reconstruction, however whether images denoised with deep learned reconstruction(DL) will improve MWF map quality has not been investigated. After denoising of multi-echo GRE magnitude images with a DL algorithm, data was fitted to a three-component magnitude model. Use of DL, shows better results as compared to conventionally generated maps (i.e. decreased NRMSE and mean fitting errors (WM), increased PSNR and SSIM). Gibb's ringing artifact was removed remarkably.

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