Abstract #1271

Development of χ-separation pipeline for UK Biobank dataset

Jaehyeon Koo¹, Hwihun Jeong¹, Jiye Kim¹, Rokgi Hong¹, Hyeong-Geol Shin^2,3, Xu Li^3,4, Yun Soo Hong⁵, Ye Qiao², Dan E. Arking⁵, and Jongho Lee¹

¹Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, ²Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, ³F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ⁴Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping, χ-separation (chi-separation)

Motivation: Recently developed χ-sepnet-$$$R_2 ^*$$$ provides opportunities to apply χ-separation in the UK Biobank (UKB) dataset. However, poor quality $$$R_2 ^*$$$ from the 3 mm-thick UKB GRE data hampers to create high-quality χ-separation results.

Goal(s): Our goal is to develop a pipeline for generating high-quality χ-separation maps from the UKB dataset.

Approach: We developed a new neural network that improved $$$R_2 ^*$$$ quality of the UKB data. Additionally, a full data processing pipeline that utilized χ-sepnet-$$$R_2 ^*$$$ for the UKB data was proposed.

Results: $$$R_2 ^*$$$ network improved the quality of $$$R_2 ^*$$$, and the proposed pipeline showed successful susceptibility source separation outcomes.

Impact: This study proposes a processing pipeline for high-quality χ-separation in the UKB dataset. For high-quality χ-separation, B₀-field inhomogeneity artifact in $$$R_2 ^*$$$ was removed using a neural network. Our pipeline enables us to investigate the large cohort UKB data.

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