Calusi Silvia, Labanca Giusy, Zani Margherita, Casati Marta, Marrazzo Livia, Noferini Linhsia, Talamonti Cinzia, Fusi Franco, Desideri Isacco, Bonomo Pierluigi, Livi Lorenzo, Pallotta Stefania
Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy.
Medical Physics Unit, AOU Careggi, Florence, Italy.
J Appl Clin Med Phys. 2019 Apr;20(4):75-82. doi: 10.1002/acm2.12564. Epub 2019 Mar 28.
A quantitative evaluation of the performances of the deformable image registration (DIR) algorithm implemented in MIM-Maestro was performed using multiple similarity indices. Two phantoms, capable of mimicking different anatomical bending and tumor shrinking were built and computed tomography (CT) studies were acquired after applying different deformations. Three different contrast levels between internal structures were artificially created modifying the original CT values of one dataset. DIR algorithm was applied between datasets with increasing deformations and different contrast levels and manually refined with the Reg Refine tool. DIR algorithm ability in reproducing positions, volumes, and shapes of deformed structures was evaluated using similarity indices such as: landmark distances, Dice coefficients, Hausdorff distances, and maximum diameter differences between segmented structures. Similarity indices values worsen with increasing bending and volume difference between reference and target image sets. Registrations between images with low contrast (40 HU) obtain scores lower than those between images with high contrast (970 HU). The use of Reg Refine tool leads generally to an improvement of similarity parameters values, but the advantage is generally less evident for images with low contrast or when structures with large volume differences are involved. The dependence of DIR algorithm on image deformation extent and different contrast levels is well characterized through the combined use of multiple similarity indices.
使用多种相似性指标对MIM-Maestro中实现的可变形图像配准（DIR）算法的性能进行了定量评估。构建了两个能够模拟不同解剖弯曲和肿瘤缩小的体模，并在施加不同变形后获取计算机断层扫描（CT）研究数据。通过修改一个数据集的原始CT值，人为创建了内部结构之间三种不同的对比度水平。将DIR算法应用于具有不同变形和不同对比度水平的数据集之间，并使用Reg Refine工具进行手动优化。使用相似性指标（如：地标距离、骰子系数、豪斯多夫距离以及分割结构之间的最大直径差异）评估DIR算法在再现变形结构的位置、体积和形状方面的能力。随着参考图像集和目标图像集之间弯曲程度和体积差异的增加，相似性指标值会变差。低对比度（40 HU）图像之间的配准得分低于高对比度（970 HU）图像之间的配准得分。使用Reg Refine工具通常会导致相似性参数值的提高，但对于低对比度图像或涉及体积差异较大的结构时，这种优势通常不太明显。通过结合使用多种相似性指标，可以很好地表征DIR算法对图像变形程度和不同对比度水平的依赖性。
