Weistrand Ola, Svensson Stina
RaySearch Laboratories AB, Sveavägen 44, SE-11134 Stockholm, Sweden.
Med Phys. 2015 Jan;42(1):40-53. doi: 10.1118/1.4894702.
The purpose of this work was to describe a versatile algorithm for deformable image registration with applications in radiotherapy and to validate it on thoracic 4DCT data as well as CT/cone beam CT (CBCT) data.
ANAtomically CONstrained Deformation Algorithm (ANACONDA) combines image information (i.e., intensities) with anatomical information as provided by contoured image sets. The registration problem is formulated as a nonlinear optimization problem and solved with an in-house developed solver, tailored to this problem. The objective function, which is minimized during optimization, is a linear combination of four nonlinear terms: 1. image similarity term; 2. grid regularization term, which aims at keeping the deformed image grid smooth and invertible; 3. a shape based regularization term which works to keep the deformation anatomically reasonable when regions of interest are present in the reference image; and 4. a penalty term which is added to the optimization problem when controlling structures are used, aimed at deforming the selected structure in the reference image to the corresponding structure in the target image.
To validate ANACONDA, the authors have used 16 publically available thoracic 4DCT data sets for which target registration errors from several algorithms have been reported in the literature. On average for the 16 data sets, the target registration error is 1.17 ± 0.87 mm, Dice similarity coefficient is 0.98 for the two lungs, and image similarity, measured by the correlation coefficient, is 0.95. The authors have also validated ANACONDA using two pelvic cases and one head and neck case with planning CT and daily acquired CBCT. Each image has been contoured by a physician (radiation oncologist) or experienced radiation therapist. The results are an improvement with respect to rigid registration. However, for the head and neck case, the sample set is too small to show statistical significance.
ANACONDA performs well in comparison with other algorithms. By including CT/CBCT data in the validation, the various aspects of the algorithm such as its ability to handle different modalities, large deformations, and air pockets are shown.
本研究旨在描述一种适用于放射治疗的可变形图像配准通用算法，并在胸部4DCT数据以及CT/锥形束CT（CBCT）数据上对其进行验证。
解剖约束变形算法（ANACONDA）将图像信息（即强度）与轮廓图像集提供的解剖信息相结合。配准问题被表述为一个非线性优化问题，并使用针对该问题自行开发的求解器来解决。在优化过程中最小化的目标函数是四个非线性项的线性组合：1. 图像相似性项；2. 网格正则化项，旨在保持变形后的图像网格平滑且可逆；3. 基于形状的正则化项，当参考图像中存在感兴趣区域时，该项用于使变形在解剖学上合理；4. 惩罚项，当使用控制结构时添加到优化问题中，旨在将参考图像中选定的结构变形为目标图像中的相应结构。
为了验证ANACONDA，作者使用了16个公开可用的胸部4DCT数据集，文献中已报道了几种算法的目标配准误差。对于这16个数据集，平均目标配准误差为1.17±0.87毫米，两肺的骰子相似系数为0.98，通过相关系数测量的图像相似性为0.95。作者还使用两个盆腔病例和一个头颈部病例的计划CT以及每日获取的CBCT对ANACONDA进行了验证。每个图像均由医生（放射肿瘤学家）或经验丰富的放射治疗师进行轮廓勾画。结果相对于刚性配准有改进。然而，对于头颈部病例，样本集太小，无法显示统计学意义。
与其他算法相比，ANACONDA表现良好。通过在验证中纳入CT/CBCT数据，展示了该算法的各个方面，如处理不同模态、大变形和气囊的能力。
