Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23298, USA.
Med Phys. 2012 Feb;39(2):573-80. doi: 10.1118/1.3673772.
To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping.
Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel.
The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties.
Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties.
开发一种用于变形图像配准中空间相关不确定性的统计抽样方法，然后使用该方法演示其对日常剂量映射的影响。
在分次外部束放射治疗前，连续采集每日 CT 研究以绘制解剖学变化图。将 CT 变形配准到计划 CT 以获得位移矢量场 (DVF)。使用 DVF 将每天给予的剂量累积到计划 CT 上。每个 DVF 都与其相关联具有空间相关的不确定性。对测量的 DVF 误差图应用主成分分析 (PCA) 以产生误差的去相关主成分模式。独立地对模式进行采样并进行重建以产生合成配准误差图。用通过变形配准映射的剂量对合成误差图进行卷积，以对剂量映射中的不确定性进行建模。将结果与由于从体素到体素随机变化的不相关 DVF 误差而导致的剂量映射不确定性进行比较。
所提出的误差抽样方法产生的合成 DVF 误差图在统计上与观察到的误差图无法区分。我们的程序建模的具有空间相关性的 DVF 不确定性会产生与随机分布不确定性引起的剂量映射误差不同的模式。
变形图像配准不确定性具有复杂的空间分布。作者已经开发并测试了一种方法来解相关空间不确定性并对高度相关的误差图进行统计抽样。抽样误差图可用于通过变形图像配准研究 DVF 不确定性对日常剂量映射的影响。该方法学的初步演示表明，剂量映射不确定性可能对 DVF 不确定性中的空间模式敏感。
