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Abstract #4168

Successful generalization for data with higher or lower resolution than training data resolution in deep learning powered QSM reconstruction

Sooyeon Ji1, Juhyung Park1, Hyeong-Geol Shin2,3, Joonhyeok Yoon1, Minjun Kim1, and Jongho Lee1
1Department of Electrical Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Synopsis

Keywords: Susceptibility, Data ProcessingA pipeline to reconstruct multiple resolution QSM data using a QSM network trained at a single resolution is proposed. The local field map is re-sampled multiple times in different spatial locations, and the re-sampled local field maps are used to reconstruct QSM maps at training data resolution. The reconstructed maps are then combined, and corrected for using a procedure named “dipole compensation”. When compared to two scenarios to reconstruct different resolution data using network trained at a single resolution, the proposed pipeline demonstrated the best performance both qualitatively and quantitatively.

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Keywords