标题： 具有可学习核的隐式可变形医学图像配准 显示英文标题

标题： Implicit Deformable Medical Image Registration with Learnable Kernels

摘要： 可变形医学图像配准是在计算机辅助干预中一项至关重要的任务。 这一问题尤其与肿瘤治疗相关，在这些治疗中，精确的图像对齐对于跟踪肿瘤生长、评估治疗反应以及确保准确的疗法递送至关重要。 近期的AI方法在准确性和速度上可以超越传统技术，但它们常常产生不可靠的变形，这限制了它们在临床中的应用。 在这项工作中，我们解决了这一挑战，并引入了一种新的隐式配准框架，能够预测出准确且可靠的变形。 我们的见解是将图像配准重新表述为信号重建问题：我们学习一种核函数，可以从稀疏的关键点对应关系中恢复密集的位移场。 我们将该方法整合到一种新颖的分层架构中，并以粗到细的方式估计位移场。 我们的公式还允许在测试时进行高效的细化，使临床医生在需要时能够轻松调整配准。 我们在具有挑战性的患者内胸部和腹部零样本配准任务上验证了我们的方法，使用了当地大学医院的公开和内部数据集。 我们的方法不仅显示出与最先进的方法具有竞争力的准确性，而且还弥合了隐式和显式配准技术之间的泛化差距。 特别是，我们的方法生成的变形更好地保留了解剖学关系，并且达到了专门商业系统的性能，突显了其在临床应用中的潜力。 显示英文摘要

摘要： Deformable medical image registration is an essential task in computer-assisted interventions. This problem is particularly relevant to oncological treatments, where precise image alignment is necessary for tracking tumor growth, assessing treatment response, and ensuring accurate delivery of therapies. Recent AI methods can outperform traditional techniques in accuracy and speed, yet they often produce unreliable deformations that limit their clinical adoption. In this work, we address this challenge and introduce a novel implicit registration framework that can predict accurate and reliable deformations. Our insight is to reformulate image registration as a signal reconstruction problem: we learn a kernel function that can recover the dense displacement field from sparse keypoint correspondences. We integrate our method in a novel hierarchical architecture, and estimate the displacement field in a coarse-to-fine manner. Our formulation also allows for efficient refinement at test time, permitting clinicians to easily adjust registrations when needed. We validate our method on challenging intra-patient thoracic and abdominal zero-shot registration tasks, using public and internal datasets from the local University Hospital. Our method not only shows competitive accuracy to state-of-the-art approaches, but also bridges the generalization gap between implicit and explicit registration techniques. In particular, our method generates deformations that better preserve anatomical relationships and matches the performance of specialized commercial systems, underscoring its potential for clinical adoption.