IEEE Trans Med Imaging. 2021 Apr;40(4):1290-1302. doi: 10.1109/TMI.2021.3052523. Epub 2021 Apr 1.
Image-guided intervention for soft tissue organs depends on the accuracy of deformable registration methods to achieve effective results. While registration techniques based on elastic theory are prevalent, no methods yet exist that can prospectively estimate registration uncertainty to regulate sources and mitigate consequences of localization error in deforming organs. This paper introduces registration uncertainty metrics based on dispersion of strain energy from boundary constraints to predict the proportion of target registration error (TRE) remaining after nonrigid elastic registration. These uncertainty metrics depend on the spatial distribution of intraoperative constraints provided to registration with relation to patient-specific organ geometry. Predictive linear and bivariate gamma models are fit and cross-validated using an existing dataset of 6291 simulated registration examples, plus 699 novel simulated registrations withheld for independent validation. Average uncertainty and average proportion of TRE remaining after elastic registration are strongly correlated ( r = 0.78 ), with mean absolute difference in predicted TRE equivalent to 0.9 ± 0.6 mm (cross-validation) and 0.9 ± 0.5 mm (independent validation). Spatial uncertainty maps also permit localized TRE estimates accurate to an equivalent of 3.0 ± 3.1 mm (cross-validation) and 1.6 ± 1.2 mm (independent validation). Additional clinical evaluation of vascular features yields localized TRE estimates accurate to 3.4 ± 3.2 mm. This work formalizes a lower bound for the inherent uncertainty of nonrigid elastic registrations given coverage of intraoperative data constraints, and demonstrates a relation to TRE that can be predictively leveraged to inform data collection and provide a measure of registration confidence for elastic methods.
基于变形的配准方法对于软组织器官的影像引导介入至关重要，其准确性是实现有效结果的关键。虽然基于弹性理论的配准技术较为常见，但目前尚无方法可以前瞻性地估计配准不确定性，以调节变形器官中定位误差的来源并减轻其后果。本文介绍了基于边界约束应变能分散的配准不确定性度量方法，以预测非刚性弹性配准后目标配准误差（TRE）的剩余比例。这些不确定性度量方法取决于术中约束的空间分布，以及与特定于患者的器官几何形状相关的配准。使用现有的 6291 个模拟配准示例数据集以及 699 个新的保留用于独立验证的模拟配准，拟合并交叉验证了预测线性和双变量伽马模型。平均不确定性和弹性配准后 TRE 剩余的平均比例具有很强的相关性（r = 0.78），预测 TRE 的平均绝对差异相当于 0.9 ± 0.6 毫米（交叉验证）和 0.9 ± 0.5 毫米（独立验证）。空间不确定性图还允许对局部 TRE 进行准确估计，精度可达 3.0 ± 3.1 毫米（交叉验证）和 1.6 ± 1.2 毫米（独立验证）。对血管特征的额外临床评估可实现局部 TRE 估计的精度为 3.4 ± 3.2 毫米。这项工作为给定术中数据约束覆盖范围的非刚性弹性配准的固有不确定性规定了下限，并证明了与 TRE 的关系，该关系可被预测性地利用来告知数据采集并为弹性方法提供配准置信度的度量。
