论文标题
基于型号的定量敏感性映射的学习
Model-based Learning for Quantitative Susceptibility Mapping
论文作者
论文摘要
定量敏感性映射（QSM）是一种磁共振成像（MRI）技术，该技术估算了Larmor频率偏移测量的磁敏感性。 QSM的产生需要解决一个具有挑战性的现场倒置问题。野外倒置不准确，通常会导致大量的敏感性估计误差，这些估计误差似乎是QSM中的伪影，尤其是在大量的出血区域中。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DL技术要么通过多重取向采样（COSMOS）映射（COSMOS）映射（COSMOS）映射或合成数据来计算网络训练。因此，它们受到宇宙图的可用性和准确性的限制，或者在训练和测试域不同时遭受性能下降。为了解决这些限制，我们提出了一种基于模型的DL方法，称为UQSM。在不访问QSM标签的情况下，UQSM是使用完善的物理模型训练的。在评估多取向QSM数据集时，与TKD，TV-Fansi，MEDI和DIP方法相比，UQSM可实现更高的定量准确性。当对单向数据集进行定性评估时，UQSM优于其他方法和重建的高质量QSM。
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that estimates magnetic susceptibility of tissue from Larmor frequency offset measurements. The generation of QSM requires solving a challenging ill-posed field-to-source inversion problem. Inaccurate field-to-source inversion often causes large susceptibility estimation errors that appear as streaking artifacts in the QSM, especially in massive hemorrhagic regions. 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 DL techniques used either calculation of susceptibility through multiple orientation sampling (COSMOS) maps or synthetic data for network training. Therefore, they were constrained by the availability and accuracy of COSMOS maps, or suffered from performance drop when the training and testing domains were different. To address these limitations, we present a model-based DL method, denoted as uQSM. Without accessing to QSM labels, uQSM is trained using the well-established physical model. When evaluating on multi-orientation QSM datasets, uQSM achieves higher levels of quantitative accuracy compared to TKD, TV-FANSI, MEDI, and DIP approaches. When qualitatively evaluated on single-orientation datasets, uQSM outperforms other methods and reconstructed high quality QSM.
