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Abstract #0838

Accelerated 3D Myelin Water Imaging: Jointly Unrolled Cross-domain Optimization-based Spatio-Temporal Reconstruction Network

Jae-Hun Lee1, Dongyeob Han2, Jae-Yoon Kim1, and Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei Univ., Seoul, Korea, Republic of, 2Siemens Healthineers Ltd, Siemens Korea, Seoul, Korea, Republic of

Synopsis

Keywords: Parallel Imaging, Image Reconstruction, Quantitative Imaging, White MatterRecently, acceleration of 3D multi-echo gradient-echo (mGRE) acquisition for myelin water imaging (MWI) has been achieved using parallel imaging (PI) or deep learning network. However, these methods typically allow a low acceleration factor (R) for MWI because of the high sensitivity of the MWI estimation routine with respect noise/artifacts. Here, we developed a reconstruction deep learning network called the jointly unrolled cross-domain optimization-based spatio-temporal reconstruction network. According to retrospective and prospective reconstruction results, the proposed method achieved high-fidelity performance on the reconstructed mGRE images and MWI maps.

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Keywords