Paul Scherrer Institute, Center for Proton Therapy, Switzerland.
Department of Physics, ETH Zurich, Switzerland.
Phys Med Biol. 2021 May 10;66(10). doi: 10.1088/1361-6560/abf8f5.
Deformable image registration (DIR) is an important component for dose accumulation and associated clinical outcome evaluation in radiotherapy. However, the resulting deformation vector field (DVF) is subject to unavoidable discrepancies when different algorithms are applied, leading to dosimetric uncertainties of the accumulated dose. We propose here an approach for proton therapy to estimate dosimetric uncertainties as a consequence of modeled or estimated DVF uncertainties. A patient-specific DVF uncertainty model was built on the first treatment fraction, by correlating the magnitude differences of five DIR results at each voxel to the magnitude of any single reference DIR. In the following fractions, only the reference DIR needs to be applied, and DVF geometric uncertainties were estimated by this model. The associated dosimetric uncertainties were then derived by considering the estimated geometric DVF uncertainty, the dose gradient of fractional recalculated dose distribution and the direction factor from the applied reference DIR of this fraction. This estimated dose uncertainty was respectively compared to the reference dose uncertainty when different DIRs were applied individually for each dose warping. This approach was validated on seven NSCLC patients, each with nine repeated CTs. The proposed model-based method is able to achieve dose uncertainty distribution on a conservative voxel-to-voxel comparison within ±5% of the prescribed dose to the 'reference' dosimetric uncertainty, for 77% of the voxels in the body and 66%-98% of voxels in investigated structures. We propose a method to estimate DIR induced uncertainties in dose accumulation for proton therapy of lung tumor treatments.
形变图像配准（DIR）是放射治疗中剂量积累和相关临床结果评估的重要组成部分。然而，当应用不同的算法时，所得到的变形向量场（DVF）会不可避免地存在差异，导致积累剂量的剂量学不确定性。在这里，我们提出了一种用于质子治疗的方法，以估计由于模型化或估计的 DVF 不确定性而导致的剂量学不确定性。在第一个治疗分数上构建了一个患者特异性的 DVF 不确定性模型，通过将每个体素的五个 DIR 结果的幅度差异与任何单个参考 DIR 的幅度相关联来实现。在随后的分数中，只需应用参考 DIR，并且可以通过该模型估计 DVF 几何不确定性。然后，通过考虑估计的几何 DVF 不确定性、分次重新计算剂量分布的剂量梯度以及来自该分数的应用参考 DIR 的方向因子，得出相关的剂量学不确定性。分别比较了当为每个剂量变形应用不同的 DIR 时，不同方法的估计剂量不确定性与参考剂量不确定性的关系。该方法在 7 名 NSCLC 患者中进行了验证，每个患者有 9 次重复 CT。所提出的基于模型的方法能够在体素到体素的保守比较中实现剂量不确定性分布，对于“参考”剂量学不确定性的±5%的规定剂量范围内，对于身体的 77%的体素和研究结构的 66%-98%的体素。我们提出了一种方法来估计质子治疗肺肿瘤治疗中剂量积累的 DIR 诱导不确定性。
