Institute for Numerical Simulation, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany.
Med Phys. 2013 Sep;40(9):091703. doi: 10.1118/1.4816675.
The intraprocedural tracking of respiratory motion has the potential to substantially improve image-guided diagnosis and interventions. The authors have developed a sparse-to-dense registration approach that is capable of recovering the patient's external 3D body surface and estimating a 4D (3D + time) surface motion field from sparse sampling data and patient-specific prior shape knowledge.
The system utilizes an emerging marker-less and laser-based active triangulation (AT) sensor that delivers sparse but highly accurate 3D measurements in real-time. These sparse position measurements are registered with a dense reference surface extracted from planning data. Thereby a dense displacement field is recovered, which describes the spatio-temporal 4D deformation of the complete patient body surface, depending on the type and state of respiration. It yields both a reconstruction of the instantaneous patient shape and a high-dimensional respiratory surrogate for respiratory motion tracking. The method is validated on a 4D CT respiration phantom and evaluated on both real data from an AT prototype and synthetic data sampled from dense surface scans acquired with a structured-light scanner.
In the experiments, the authors estimated surface motion fields with the proposed algorithm on 256 datasets from 16 subjects and in different respiration states, achieving a mean surface reconstruction accuracy of ± 0.23 mm with respect to ground truth data-down from a mean initial surface mismatch of 5.66 mm. The 95th percentile of the local residual mesh-to-mesh distance after registration did not exceed 1.17 mm for any subject. On average, the total runtime of our proof of concept CPU implementation is 2.3 s per frame, outperforming related work substantially.
In external beam radiation therapy, the approach holds potential for patient monitoring during treatment using the reconstructed surface, and for motion-compensated dose delivery using the estimated 4D surface motion field in combination with external-internal correlation models.
术中呼吸运动的跟踪有可能显著改善图像引导诊断和介入治疗。作者开发了一种稀疏到密集的配准方法，能够从稀疏采样数据和患者特定的先验形状知识中恢复患者的外部 3D 体表面，并估计 4D（3D+时间）表面运动场。
该系统利用一种新兴的无标记和基于激光的主动三角测量（AT）传感器，实时提供稀疏但高度精确的 3D 测量。这些稀疏位置测量与从规划数据中提取的密集参考表面进行配准。由此恢复了密集的位移场，该位移场描述了完整患者体表面的时空 4D 变形，具体取决于呼吸的类型和状态。它生成了即时患者形状的重建和用于呼吸运动跟踪的高维呼吸替代物。该方法在 4D CT 呼吸体模上进行了验证，并在 AT 原型的真实数据和使用结构光扫描仪获取的密集表面扫描的合成数据上进行了评估。
在实验中，作者使用所提出的算法对来自 16 个受试者的 256 个数据集进行了表面运动场估计，在不同的呼吸状态下，相对于真实数据，平均表面重建精度为±0.23mm，初始表面不匹配的平均值为 5.66mm。注册后局部残差网格到网格距离的 95%百分位不超过 1.17mm，适用于任何受试者。平均而言，我们的概念验证 CPU 实现的总运行时间为每帧 2.3 秒，大大优于相关工作。
在外部束放射治疗中，该方法有可能在治疗期间使用重建的表面进行患者监测，并使用估计的 4D 表面运动场结合外部-内部相关模型进行运动补偿剂量传递。
