论文标题
探索深度神经网络训练QSM的域适应
Exploring domain adaptation for deep neural network trained QSM
论文作者
论文摘要
定量敏感性映射（QSM）是一种MRI技术，可估计组织磁敏感性。 QSM的产生需要解决一个具有挑战性的现场倒置问题。最近，已经提出了几种深度学习（DL）QSM技术，并证明了令人印象深刻的性能。由于固有的不存在基础真相QSM参考，这些技术使用宇宙图或合成数据进行网络培训。合成数据易于生成，但由于域移位而引起的体内数据通常很差。在这里，我们使用特定于域的批处理归一化引入了一种简单的域适应技术来解决此问题。
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.
