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Abstract #1839

A Deep Learning Approach to QSM Background Field Removal: Simulating Realistic Training Data Using a Reference Scan Ground Truth and Deformations

Oriana Vanesa Arsenov1, Karin Shmueli1, and Anita Karsa1
1Medical Physics and Biomedical Engineering, University College London, London, United Kingdom

Current techniques for background field removal (BGFR), essential for quantitative susceptibility mapping, leave residual background fields and inaccuracies near air-tissue interfaces. We propose a new deep learning method aiming for robust brain BGFR: we trained a 3D U-net with realistic simulated and in-vivo data augmented with spatial deformations. The network trained on synthetic data predicts accurate local fields when tested on synthetic data, (median RMSE = 49.5%), but is less accurate when tested on in-vivo data. The network trained and tested on in-vivo data performs better, suggesting our synthetic set did not fully capture the complexity found in vivo.

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