Downs Charles, Sluijs P Matthijs van der, Cornelissen Sandra A P, Nijenhuis Frank Te, Zwam Wim H van, Gopalakrishnan Vivek, Zhang Xucong, Su Ruisheng, Walsum Theo van
Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Int J Comput Assist Radiol Surg. 2025 Jul;20(7):1451-1460. doi: 10.1007/s11548-025-03412-2. Epub 2025 May 23.
Stroke remains a leading cause of morbidity and mortality worldwide, despite advances in treatment modalities. Endovascular thrombectomy (EVT), a revolutionary intervention for ischemic stroke, is limited by its reliance on 2D fluoroscopic imaging, which lacks depth and comprehensive vascular detail. We propose a novel AI-driven pipeline for 3D CTA to 2D DSA cross-modality registration, termed DeepIterReg.
The proposed pipeline integrates neural network-based initialization with iterative optimization to align pre-intervention and peri-intervention data. Our approach addresses the challenges of cross-modality alignment, particularly in scenarios involving limited shared vascular structures, by leveraging synthetic data, vein-centric anchoring, and differentiable rendering techniques.
We assess the efficacy of DeepIterReg through quantitative analysis of capture ranges and registration accuracy. Results show that our method can accurately register 70% of a test set of 20 patients and can improve capture ranges when performing an initial pose estimation using a convolutional neural network.
DeepIterReg demonstrates promising performance for 3D-to-2D stroke intervention image registration, potentially aiding clinicians by improving spatial understanding during EVT and reducing dependence on manual adjustments.
尽管治疗方式有所进步，但中风仍然是全球发病和死亡的主要原因。血管内血栓切除术（EVT）是一种针对缺血性中风的革命性干预措施，它受到对二维荧光透视成像的依赖的限制，这种成像缺乏深度和全面的血管细节。我们提出了一种新颖的人工智能驱动的管道，用于从三维CT血管造影（3D CTA）到二维数字减影血管造影（2D DSA）的跨模态配准，称为深度迭代配准（DeepIterReg）。
所提出的管道将基于神经网络的初始化与迭代优化相结合，以对齐干预前和干预期间的数据。我们的方法通过利用合成数据、以静脉为中心的锚定和可微渲染技术，解决了跨模态对齐的挑战，特别是在共享血管结构有限的情况下。
我们通过对捕获范围和配准精度的定量分析来评估深度迭代配准的有效性。结果表明，我们的方法可以准确地对20名患者的测试集的70%进行配准，并且在使用卷积神经网络进行初始姿态估计时可以改善捕获范围。
深度迭代配准在三维到二维中风干预图像配准方面表现出有前景的性能，可能通过在血管内血栓切除术中改善空间理解并减少对手动调整的依赖来帮助临床医生。
