用分割正则化训练模型，跨域配准精准度提升，助力前列腺放疗评估
Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy
- 引入分割一致性损失，提升多域磁共振图像配准鲁棒性
- 膀胱配准DSC达0.86~0.88，直肠与靶区在跨域数据上表现更优
- 可支持放疗剂量累积，83.3%患者满足临床剂量约束
背景：精确的非刚性图像配准（DIR）是磁共振引导自适应放疗（MRgART）中轮廓传播和剂量累积的关键。本研究训练并评估了一种用于域不变性MR-MR配准的深度学习方法。方法：基于262对3T前列腺癌患者模拟扫描，采用加权分割一致性损失训练逐步优化的配准与分割（ProRSeg）方法。在同域（58对）、跨域（72对1.5T MR Linac）及混合域（42对MRSim-MRL）数据集上测试其对临床靶区（CTV）、膀胱和直肠的轮廓传播精度，并对42例接受5次分次MRgART的患者进行剂量累积。结果：ProRSeg在膀胱配准上表现出跨域一致性（DSC分别为0.88、0.87、0.86）；直肠与CTV在跨域MRL数据上表现更优（DSC为0.89），优于同域数据。模型优异的跨域性能促使我们探索其在剂量累积中的可行性。剂量累积结果显示，83.3%的患者满足靶区覆盖（D95 ≥ 40.0 Gy）和膀胱保护（D50 ≤ 20.0 Gy）要求。所有患者均达到最低平均靶区剂量（>40.4 Gy），但仅9.5%患者未超过上限（<42.0 Gy）。结论：ProRSeg在前列腺癌多域配准中表现良好，初步验证了其在评估治疗依从性方面的可行性。
原文摘要 · Abstract (English)
Background: Accurate deformable image registration (DIR) is required for contour propagation and dose accumulation in MR-guided adaptive radiotherapy (MRgART). This study trained and evaluated a deep learning DIR method for domain invariant MR-MR registration. Methods: A progressively refined registration and segmentation (ProRSeg) method was trained with 262 pairs of 3T MR simulation scans from prostate cancer patients using weighted segmentation consistency loss. ProRSeg was tested on same- (58 pairs), cross- (72 1.5T MR Linac pairs), and mixed-domain (42 MRSim-MRL pairs) datasets for contour propagation accuracy of clinical target volume (CTV), bladder, and rectum. Dose accumulation was performed for 42 patients undergoing 5-fraction MRgART. Results: ProRSeg demonstrated generalization for bladder with similar Dice Similarity Coefficients across domains (0.88, 0.87, 0.86). For rectum and CTV, performance was domain-dependent with higher accuracy on cross-domain MRL dataset (DSCs 0.89) versus same-domain data. The model's strong cross-domain performance prompted us to study the feasibility of using it for dose accumulation. Dose accumulation showed 83.3% of patients met CTV coverage (D95 >= 40.0 Gy) and bladder sparing (D50 <= 20.0 Gy) constraints. All patients achieved minimum mean target dose (>40.4 Gy), but only 9.5% remained under upper limit (<42.0 Gy). Conclusions: ProRSeg showed reasonable multi-domain MR-MR registration performance for prostate cancer patients with preliminary feasibility for evaluating treatment compliance to clinical constraints.
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