Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world clinical applications, such as the MR-to-ultrasound registration for prostate intervention of interest in this work, but also provides an explainable means of understanding the organ motion and spatial correspondence establishment. This work instantiates the recently-proposed physics-informed neural networks (PINNs) to a 3D linear elastic model for modelling prostate motion commonly encountered during transrectal ultrasound guided procedures. To overcome a widely-recognised challenge in generalising PINNs to different subjects, we propose to use PointNet as the nodal-permutation-invariant feature extractor, together with a registration algorithm that aligns point sets and simultaneously takes into account the PINN-imposed biomechanics. The proposed method has been both developed and validated in both patient-specific and multi-patient manner.

翻译：软组织生物力学建模提供了一种非数据驱动的方法来约束医学图像配准，使得估计的空间变换在生物物理学上被认为是合理的。该方法不仅已应用于实际的临床场景（例如本文关注的前列腺介入中的磁共振-超声配准），还提供了一种可解释的手段来理解器官运动及空间对应关系的建立。本文将近期提出的物理信息神经网络（PINNs）实例化为三维线性弹性模型，用于建模经直肠超声引导过程中常见的前列腺运动。为解决将PINNs推广至不同受试者这一公认挑战，我们提出采用PointNet作为节点排列不变特征提取器，并配合一种同时考虑PINNs强加生物力学特性的点集对齐配准算法。所提出的方法已通过患者特异性及多患者两种方式进行了开发和验证。