基于可学习核的隐式可变形医学图像配准
Implicit Deformable Medical Image Registration with Learnable Kernels
AI总结：
本文提出一种基于可学习核的隐式医学图像配准框架，将配准重构为从稀疏关键点恢复稠密位移场的信号重建问题，采用由粗到细的层次化架构，在胸腹部零样本配准任务上取得与最先进方法相当且更可靠的形变结果。
摘要：
可变形医学图像配准是计算机辅助干预中的一项基本任务。该问题在肿瘤治疗中尤为重要，因为精确的图像对齐对于追踪肿瘤生长、评估治疗反应以及确保治疗的准确实施是必要的。近年来的人工智能方法在精度和速度上可以超越传统技术，但它们常常产生不可靠的形变，从而限制了其在临床中的应用。在这项工作中，我们针对这一挑战，提出了一种新颖的隐式配准框架，能够预测准确且可靠的形变。我们的核心思路是将图像配准重新表述为信号重建问题：我们学习一个核函数，能够从稀疏的关键点对应关系中恢复稠密的位移场。我们将该方法集成到一个新颖的层次化架构中，并以由粗到细的方式估计位移场。我们的公式还支持在测试时进行高效细化，使临床医生能够在需要时轻松调整配准结果。我们使用来自当地大学医院的公开和内部数据集，在具有挑战性的患者胸腔和腹腔零样本配准任务上验证了我们的方法。我们的方法不仅在精度上与最先进的方法具有竞争力，而且弥合了隐式配准技术与显式配准技术之间的泛化差距。特别是，我们的方法生成的形变能够更好地保持解剖关系，并达到了专门商业系统的性能水平，突显了其临床应用的潜力。
英文摘要：
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.
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