Accurate intraoperative liver registration is challenging due to substantial soft-tissue deformation yet sparse intraoperative measurements. Biomechanical models regularize this ill-posedness with prior knowledge but exhibit persistent prediction bias due to simplifying assumptions, while data-driven learning solutions struggle with data efficiency, generalization, and physical plausibility. We propose a hybrid registration framework that adapts a biomechanical prior using sparse intraoperative correspondences. Rather than learning a full deformation field, we learn a residual deformation function that corrects linear biomechanical predictions, modeled as a graph neural diffusion function with geometry-aware attention over the 3D liver mesh. To enable long-range information transfer of sparse observations, we take a novel perspective of sparse intraoperative measurements as \textit{context} samples where input-output pairs of the residual deformation function are fully observed, casting the problem into learning-to-learn this residual function from intraoperative context samples with feedforward meta-learners. Experiments on a deformable liver phantom dataset demonstrate improved registration accuracy and generalization compared to rigid, biomechanical, and data-driven baselines, particularly for out-of-distribution geometries and deformations.

翻译：精确的术中肝脏配准因显著的软组织变形和稀疏的术中测量而极具挑战性。生物力学模型利用先验知识对病态问题进行正则化，但由于简化假设而存在持续预测偏差，而数据驱动学习方法在数据效率、泛化能力和物理合理性方面存在不足。我们提出了一种混合配准框架，利用稀疏的术中对应关系自适应调整生物力学先验。不同于学习完整的变形场，我们学习一个残差变形函数来校正线性生物力学预测，该函数建模为一种图神经扩散函数，在三维肝脏网格上具有几何感知注意力机制。为了实现稀疏观测的长程信息传递，我们提出了一种新颖视角，将稀疏的术中测量视为残差变形函数输入-输出对完全可观测的"上下文"样本，从而将该问题转化为通过前馈元学习器从术中上下文样本中学习残差函数的学习方法。在可变形肝脏体模数据集上的实验表明，与刚性配准、纯生物力学和数据驱动基线相比，本方法在配准精度和泛化能力上均有提升，尤其适用于非标准几何形状与变形情况。