用深度学习实现PET/CT图像自动对齐，精度超传统方法。
Unsupervised Learning of Multi-modal Affine Registration for PET/CT
- 用核密度估计替代互信息，构建新相似性度量训练网络。
- 多尺度渐进优化，提升对齐精度，平均Dice系数达0.870。
- 适合需要高精度配准的临床影像分析场景。
在PET/CT成像中，将功能性的PET图像与解剖性的CT图像对齐至关重要，但因模态差异大而困难。尽管深度学习在医学影像中前景广阔，其在多模态PET/CT仿射配准中的应用仍较少。本文提出一种基于深度学习的配准方法，采用帕尔真窗法近似相关比作为图像相似性度量，用于训练神经网络。同时引入多尺度、实例特定的优化策略，在多个图像分辨率下迭代细化网络生成的仿射参数。在包含合成仿射变换的大规模公开FDG-PET/CT数据集上，本方法对比了常用的互信息度量和ANTs工具包中的优化方法，取得了0.870的平均骰子相似系数（DSC），优于对比方法，验证了其在多模态图像配准中的有效性。
原文摘要 · Abstract (English)
Affine registration plays a crucial role in PET/CT imaging, where aligning PET with CT images is challenging due to their respective functional and anatomical representations. Despite the significant promise shown by recent deep learning (DL)-based methods in various medical imaging applications, their application to multi-modal PET/CT affine registration remains relatively unexplored. This study investigates a DL-based approach for PET/CT affine registration. We introduce a novel method using Parzen windowing to approximate the correlation ratio, which acts as the image similarity measure for training DNNs in multi-modal registration. Additionally, we propose a multi-scale, instance-specific optimization scheme that iteratively refines the DNN-generated affine parameters across multiple image resolutions. Our method was evaluated against the widely used mutual information metric and a popular optimization-based technique from the ANTs package, using a large public FDG-PET/CT dataset with synthetic affine transformations. Our approach achieved a mean Dice Similarity Coefficient (DSC) of 0.870, outperforming the compared methods and demonstrating its effectiveness in multi-modal PET/CT image registration.
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