Exploring domain adaptation for deep neural network trained QSM

Juan Liu; Kevin Koch

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arXiv · 2206.13225

Exploring domain adaptation for deep neural network trained QSM

Juan Liu·Kevin Koch

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Abstract

Quantitative susceptibility mapping (QSM) is a MRI technique that estimates tissue magnetic susceptibility. The generation of QSM requires solving a challenging ill-posed field-to-source inversion problem. Recently, several deep learning (DL) QSM techniques have been proposed and demonstrated impressive performance. Due to the inherent non-existent ground-truth QSM references, these techniques used either COSMOS maps or synthetic data for network training. Synthetic data is easy to generate but often adapt poorly to in-vivo data due to domain shifts. Here, we introduce an easy domain adaptation technique using domain-specific batch normalization to address this problem.

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Juan Liu, Kevin Koch. 2022-06-24. Exploring domain adaptation for deep neural network trained QSM. https://arxiv.org/abs/2206.13225

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