发表机构
medPhoton GmbH; Technical University of Munich（medPhoton 有限公司; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于解剖感知隐式神经表示的形变CT-超声配准方法，利用CT先验和物理约束，在探头接触变形下将配准精度较刚性初始提升17%，优于经典形变方法。
AI 中文摘要
超声（US）与术前计算机断层扫描（CT）成像之间的切片到体积配准将增强许多微创介入手术，例如通过定位术中在CT中可辨别的软组织结构。虽然光学跟踪能够实现初始刚性配准，但探头接触会引起软组织变形，从而阻碍精确对齐。在这项工作中，我们引入了一个形变CT-超声配准框架，该框架结合了从CT中提取的解剖先验，以改善变形条件下的配准。首先使用机器人辅助光学跟踪系统建立刚性配准，然后使用每帧优化的正弦隐式神经表示（SIREN）估计形变变换。组织刚度从基于CT的HU值近似得到，并用作空间变化的正则化，抑制骨骼等刚性结构中的变形，同时允许软组织具有更大的灵活性。另外两个约束捕捉探头接触的物理特性：接触区位移先验，驱动位移场压缩探头面下方的组织；以及基于波束方向和凸面换能器视场的扇形几何正则化项。模型参数使用归一化梯度场（NGF）进行优化。所提出的方法在刚性初始化的基础上将对齐精度提高了17%，并优于经典的形变基线方法，同时保持接近零的拓扑折叠。
英文摘要
Slice-to-volume registration between ultrasound (US) and preoperative computed tomography (CT) imaging would enhance many minimally invasive interventions, for example by locating soft tissue structures intra-operatively that are discernible in CT. While optical tracking enables initial rigid registration, contact from the probe induces soft tissue deformations that inhibit accurate alignment. In this work, we introduce a deformable CT-ultrasound registration framework that incorporates anatomical priors derived from CT to improve registration under deformation. Rigid registration is first established using a robot-assisted optical tracking system, after which a deformable transformation is estimated using a sinusoidal implicit neural representation (SIREN) optimized per frame. Tissue stiffness is approximated from CT-based HU values and used as spatially varying regularization, suppressing deformation in rigid structures such as bone while allowing more flexibility in soft tissue. Two additional constraints capture the physics of probe contact: a contact-zone displacement prior that drives the displacement field to compress tissue below the probe face, and a fan-geometry regularization term based on beam direction and convex transducer field of view. Model parameters are optimized with a normalized gradient field (NGF). The proposed approach improves alignment over rigid initialisation by 17% and outperforms classical deformable baselines while maintaining near-zero topological folding.
Comments10 pages, 3 figures. Accepted at the 7th International Workshop on Advances in Simplifying Medical UltraSound (ASMUS 2026), held with MICCAI 2026; to appear in Springer LNCS 17276 (MICCAI 2026 Workshops and Challenges). Open-access camera-ready: https://papers.miccai.org/miccai-2026-sat/ASMUS_047.html
