Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explicitly impose smoothness constraints such as the $\mathcal{L}_2$ norm on the displacement vector field (DVF), while usually ignoring biomechanical feasibility in the transformation. Other geometric constraints either regularize specific regions of interest such as imposing incompressibility on the myocardium or introduce additional steps such as training a separate network-based regularizer on physically simulated datasets. In this work, we propose an explicit biomechanics-informed prior as regularization on the predicted DVF in modeling a more generic biomechanically plausible transformation within all cardiac structures without introducing additional training complexity. We validate our methods on two publicly available datasets in the context of 2D MRI data and perform extensive experiments to illustrate the effectiveness and robustness of our proposed methods compared to other competing regularization schemes. Our proposed methods better preserve biomechanical properties by visual assessment and show advantages in segmentation performance using quantitative evaluation metrics. The code is publicly available at \url{https://github.com/Voldemort108X/bioinformed_reg}.
翻译:在图像中,心血管运动中的空间-时空学学习时空对应物对于理解心脏解剖结构的基本动态很重要。许多方法明确对迁移矢量场(DVF)施加了光滑限制,例如$mathcal{L ⁇ 2$规范,但通常忽视改造过程中的生物机能可行性。其他几何限制要么使特定感兴趣的区域正规化,例如对心肌梗塞施加不压缩,要么采取其他步骤,例如对物理模拟数据集进行以网络为基础的单独定序器培训。在这项工作中,我们提议在预测的DVF进行正规化之前,以明确的生物机能信息为基础,在不引入额外培训复杂性的情况下,在所有心脏结构中模拟一种更通用的生物机理合理的转变。我们验证了在2D MRI数据中两种公开可用的数据集上的方法,并进行了广泛的实验,以说明我们拟议方法与其他相互竞争的规范化计划相比的有效性和稳健性。我们提出的方法通过视觉评估更好地保存生物机能特性,并显示利用定量评价指标进行分解功能的优势。代码在108/urlbioam_VL_greath@gthom_gath@gycomstomtaltaltaltal