标题： 结构和光滑性约束的双网络用于磁共振偏置场校正 显示英文标题

标题： Structure and Smoothness Constrained Dual Networks for MR Bias Field Correction

摘要： 磁共振成像技术对疾病诊断非常有益。 然而，由于磁共振设备的限制，成像结果中经常存在显著的强度不均匀性，这阻碍了定性和定量的医学分析。 最近，一些基于无监督深度学习的模型被提出用于改善磁共振图像。 然而，这些模型仅关注全局外观学习，而忽略了图像结构和偏差场平滑性的约束，导致校正结果失真。 本文提出了一种新的结构和平滑性约束双网络，称为S2DNets，旨在进行自监督的偏差场校正。 S2DNets在网络训练中引入了分段结构约束和偏差场的平滑性，以有效消除非均匀强度并保留更多的结构细节。 在临床和模拟磁共振数据集上进行的大量实验表明，所提出的模型优于其他传统和基于深度学习的模型。 除了在视觉指标上的比较外，还使用了下游磁共振图像分割任务来评估所提出模型的影响。 源代码可在以下网址获取：https://github.com/LeongDong/S2DNets\}\{https://github.com/LeongDong/S2DNets. 显示英文摘要

摘要： MR imaging techniques are of great benefit to disease diagnosis. However, due to the limitation of MR devices, significant intensity inhomogeneity often exists in imaging results, which impedes both qualitative and quantitative medical analysis. Recently, several unsupervised deep learning-based models have been proposed for MR image improvement. However, these models merely concentrate on global appearance learning, and neglect constraints from image structures and smoothness of bias field, leading to distorted corrected results. In this paper, novel structure and smoothness constrained dual networks, named S2DNets, are proposed aiming to self-supervised bias field correction. S2DNets introduce piece-wise structural constraints and smoothness of bias field for network training to effectively remove non-uniform intensity and retain much more structural details. Extensive experiments executed on both clinical and simulated MR datasets show that the proposed model outperforms other conventional and deep learning-based models. In addition to comparison on visual metrics, downstream MR image segmentation tasks are also used to evaluate the impact of the proposed model. The source code is available at: https://github.com/LeongDong/S2DNets}{https://github.com/LeongDong/S2DNets.