用解剖结构信息初始化，提升放疗中患者间CT配准精度与速度。
An anatomically-informed correspondence initialisation method to improve learning-based registration for radiotherapy
- 基于学习模型预测器官对应点，用薄板样条变形预初始化扫描
- 使深度学习配准结果接近传统迭代算法，误差降低1.8mm（含结构）和0.6mm（不含）
- 适合需快速高精度配准的临床放疗场景，兼顾效率与准确性
我们提出一种基于解剖结构信息的跨患者CT非刚性配准（NRR）初始方法，利用学习模型预测器官结构间的对应关系。采用薄板样条（TPS）变形构建初始形变场，在第二步配准前对扫描图像进行预初始化。对比两种成熟方法：基于B样条迭代优化的算法和基于深度学习的方法。通过传播结构的相似性评估配准性能。结果显示，该初始方法使学习型配准性能显著提升，与传统迭代算法更接近：包含在TPS中的结构平均距离-一致性降低1.8mm，未包含结构降低0.6mm，同时保持显著速度优势（5秒 vs. 72秒）。
原文摘要 · Abstract (English)
We propose an anatomically-informed initialisation method for interpatient CT non-rigid registration (NRR), using a learning-based model to estimate correspondences between organ structures. A thin plate spline (TPS) deformation, set up using the correspondence predictions, is used to initialise the scans before a second NRR step. We compare two established NRR methods for the second step: a B-spline iterative optimisation-based algorithm and a deep learning-based approach. Registration performance is evaluated with and without the initialisation by assessing the similarity of propagated structures. Our proposed initialisation improved the registration performance of the learning-based method to more closely match the traditional iterative algorithm, with the mean distance-to-agreement reduced by 1.8mm for structures included in the TPS and 0.6mm for structures not included, while maintaining a substantial speed advantage (5 vs. 72 seconds).
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