基于Transformer的多模态图像配准局部特征匹配
Transformer-Based Local Feature Matching for Multimodal Image Registration
AI总结：
本文提出一种基于Transformer的2D超声与3D CT多模态图像配准方法，采用LoFTR进行密集特征匹配并引入完全可微的姿态估计优化，在离体猪肾数据集上验证了无跟踪器术中超声姿态估计的有效性，改善了手术指导。
摘要：
超声成像是一种具有成本效益且无辐射的模态，能够实时可视化解剖结构，使其成为指导外科干预的理想选择。然而，其有限的视野、散斑噪声和成像伪影使得缺乏经验的用户难以解读图像。在本文中，我们提出了一种新的2D超声到3D CT配准方法，以改善超声引导干预期间的手术指导。我们的方法将一种名为LoFTR的密集特征匹配方法应用于我们的多模态配准问题。我们学习使用基于Transformer的架构预测密集的从粗到细的对应关系，以估计2D超声帧和CT扫描之间稳健的刚性变换。此外，引入了一种完全可微的姿态估计方法，在训练期间基于姿态估计误差优化LoFTR。在离体猪肾的多模态数据集上进行的实验表明，该方法在术中、无跟踪器超声姿态估计方面取得了有希望的结果。通过将2D超声帧映射到3D CT体积空间，该方法提供术中指导，潜在地改善手术流程和图像解读。
英文摘要：
Ultrasound imaging is a cost-effective and radiation-free modality for visualizing anatomical structures in real-time, making it ideal for guiding surgical interventions. However, its limited field-of-view, speckle noise, and imaging artifacts make it difficult to interpret the images for inexperienced users. In this paper, we propose a new 2D ultrasound to 3D CT registration method to improve surgical guidance during ultrasound-guided interventions. Our approach adopts a dense feature matching method called LoFTR to our multimodal registration problem. We learn to predict dense coarse-to-fine correspondences using a Transformer-based architecture to estimate a robust rigid transformation between a 2D ultrasound frame and a CT scan. Additionally, a fully differentiable pose estimation method is introduced, optimizing LoFTR on pose estimation error during training. Experiments conducted on a multimodal dataset of ex vivo porcine kidneys demonstrate the method's promising results for intraoperative, trackerless ultrasound pose estimation. By mapping 2D ultrasound frames into the 3D CT volume space, the method provides intraoperative guidance, potentially improving surgical workflows and image interpretation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
