Model-Based Sparse-to-Dense Image Registration for Realtime Respiratory Motion Estimation in Image-Guided Interventions
Model-Based Sparse-to-Dense Image Registration for Realtime Respiratory Motion Estimation in Image-Guided Interventions
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DOI：
10.1109/tbme.2018.2837387
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发表时间：
2019-02-01
影响因子：
4.6
通讯作者：
Heinrich, Mattias P.
中科院分区：
文献类型：
--
作者：
Ha, In Young;Wilms, Matthias;Heinrich, Mattias P.
目的：介入内呼吸运动估计正在成为现代放射治疗递送或高强度聚焦超声系统中的重要组成部分。使用基于磁共振（MR）或超声（US）成像技术的实时运动跟踪，可以极大地提高治疗质量。然而，目前的实践通常依赖于外部呼吸指标的间接测量，其具有固有的有限精度。在这项工作中，我们提出了一种新的方法，适用于具有挑战性的实时成像方式，如MR直线加速器扫描仪和三维超声采用对比度不变的特征描述符。研究方法：我们结合了联合收割机GPU加速的基于图像的稀疏分布的特征点的实时跟踪和密集的患者特定的运动模型，在一个统一的优化框架内进行正则化和稀疏到密集的插值。结果如下：我们实现了高度准确的运动预测，MRI的标志性误差约为1 mm（US约为2 mm），并在经典的模板跟踪策略上有了实质性的改进。结论：我们的技术可以更真实地模拟生理呼吸运动，特别是肺部对胸腔的滑动。重要性：我们的基于模型的稀疏到密集图像配准方法允许在图像引导干预中进行准确和实时的呼吸运动跟踪。
Objective: Intra-interventional respiratory motion estimation is becoming a vital component in modern radiation therapy delivery or high intensity focused ultrasound systems. The treatment quality could tremendously benefit from more accurate dose delivery using real-time motion tracking based on magnetic-resonance (MR) or ultrasound (US) imaging techniques. However, current practice often relies on indirect measurements of external breathing indicators, which has an inherently limited accuracy. In this work, we present a new approach that is applicable to challenging real-time capable imaging modalities like MR-Linac scanners and 3D-US by employing contrast-invariant feature descriptors. Methods: We combine GPU-accelerated image-based realtime tracking of sparsely distributed feature points and a dense patient-specific motion-model for regularisation and sparse-to-dense interpolation within a unified optimization framework. Results: We achieve highly accurate motion predictions with landmark errors of approximate to 1 mm for MRI (and approximate to 2 mm for US) and substantial improvements over classical template tracking strategies. Conclusion: Our technique can model physiological respiratory motion more realistically and deals particularly well with the sliding of lungs against the rib cage. Significance: Our model-based sparse-to-dense image registration approach allows for accurate and realtime respiratory motion tracking in image-guided interventions.