Abstract
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.
Abstract (translated)
背景：在磁共振引导的自适应放射治疗（MRgART）中，准确的可变形图像配准（DIR）对于轮廓传播和剂量积累是必需的。本研究训练并评估了一种用于领域不变的MRI-MRI注册的深度学习DIR方法。 方法：通过使用加权分割一致性损失，在262对前列腺癌患者的3T MR模拟扫描上训练了逐步细化的注册与分割（ProRSeg）方法。该方法在相同域（58对）、跨域（72个1.5T MR Linac对）和混合域（42个MRSim-MRL对）的数据集上进行了测试，评估临床靶区体积（CTV）、膀胱和直肠轮廓传播的准确性。对于接受5次分割MRgART治疗的42名患者进行了剂量积累。 结果：ProRSeg在不同领域中展示了良好的泛化性能，尤其是在膀胱的Dice相似系数方面，在各个域内均保持稳定（分别为0.88、0.87和0.86）。对于直肠和CTV而言，其表现依赖于域别，跨域MRL数据集上的准确性更高（DSCs为0.89）与同一领域的数据相比。模型的强跨领域性能促使我们研究使用该模型进行剂量积累的可行性。在42名患者中，有83.3%的人满足CTV覆盖（D95 >= 40.0 Gy）和膀胱保护（D50 <= 20.0 Gy）的约束条件。所有患者均达到了最低平均靶区剂量（>40.4 Gy），但只有9.5%的患者低于上限（<42.0 Gy）。 结论：对于前列腺癌患者，ProRSeg在多领域MRI-MRI配准方面展示了合理的性能，并初步证明了其用于评估治疗是否符合临床限制条件的可能性。
URL
https://arxiv.org/abs/2507.06966
