标题： 基于关键点的脑移位配准的深度生物力学引导插值 显示英文标题

标题： Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration

摘要： 准确补偿脑移位对于在神经外科手术中保持神经导航的可靠性至关重要。 虽然基于关键点的配准方法对大变形和拓扑变化具有鲁棒性，但它们通常依赖于简单的几何插值器，忽略了组织生物力学以生成密集的位移场。 在本工作中，我们提出了一种新的深度学习框架，可以从稀疏匹配的关键点估计密集且物理上合理的脑变形。 我们首先使用生物力学模拟生成大量合成脑变形数据集。 然后，训练一个残差3D U-Net，将标准插值估计细化为生物力学引导的变形。 在大量模拟位移场上的实验表明，我们的方法显著优于传统插值器，在推理时引入可忽略的计算开销的同时，将均方误差减少了一半。 代码可在以下位置获得： \href{https://github.com/tiago-assis/Deep-Biomechanical-Interpolator}{https://github.com/tiago-assis/Deep-Biomechanical-Interpolator} 显示英文摘要

摘要： Accurate compensation of brain shift is critical for maintaining the reliability of neuronavigation during neurosurgery. While keypoint-based registration methods offer robustness to large deformations and topological changes, they typically rely on simple geometric interpolators that ignore tissue biomechanics to create dense displacement fields. In this work, we propose a novel deep learning framework that estimates dense, physically plausible brain deformations from sparse matched keypoints. We first generate a large dataset of synthetic brain deformations using biomechanical simulations. Then, a residual 3D U-Net is trained to refine standard interpolation estimates into biomechanically guided deformations. Experiments on a large set of simulated displacement fields demonstrate that our method significantly outperforms classical interpolators, reducing by half the mean square error while introducing negligible computational overhead at inference time. Code available at: \href{https://github.com/tiago-assis/Deep-Biomechanical-Interpolator}{https://github.com/tiago-assis/Deep-Biomechanical-Interpolator}.