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Abstract #3800

Radial Approach for Dipole Inversion (RADI): A deep learning-based 2D approach to quantitative susceptibility mapping

Tomohiro Wataya1,2, Shotaro Fuchibe3,4, Hiroto Takahashi2, Hiroki Kato4, Masatoshi Hori5, Shoji Kido5, Noriyuki Tomiyama2, and Yoshiyuki Watanabe1
1Department of Radiology, Shiga University of Medical Science, Otsu, Japan, 2Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Japan, 3Research Division, GE HealthCare, Hino, Japan, 4Department of Advanced Radioisotope Medicine, Institute for Radiation Sciences, Osaka University, Suita, Japan, 5Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine, Suita, Japan

Synopsis

Keywords: Bioeffects & Magnetic Fields, Quantitative Susceptibility mapping, Deep learning; Dipole inversion, Ill-posed problem

Motivation: Previous methods are time-consuming or require a lot of computational resources to solve the dipole inversion problem in Quantitative Susceptibility Mapping (QSM).

Goal(s): To propose a 2D U-Net-based approach called “Radial Approach for Dipole Inversion (RADI)”.

Approach: In RADI, planes orthogonal to the B0 direction are radially sampled. A 2D U-Net-based model was trained to output 2D artificially simulated susceptibility from the calculated local field map. The model was used to process planes from brain images.

Results: The correlation coefficient between RADI and Morphology Enabled Dipole Inversion (MEDI) was 0.680. In RADI, streak and susceptibility artifacts were suppressed.

Impact: We propose a 2D deep learning-based approach to the dipole inversion in QSM called “Radial Approach for Dipole Inversion (RADI)”. RADI can accelerate the calculation process of QSM and reduce artifacts that appear in conventional methods.

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