Loi Gianfranco, Dominietto Marco, Manfredda Irene, Mones Eleonora, Carriero Alessandro, Inglese Eugenio, Krengli Marco, Brambilla Marco
Medical Physics Department, Azienda Ospedaliera Maggiore della Carità, C.so Mazzini 18, 28100, Novara, Italy.
J Digit Imaging. 2008 Sep;21(3):329-37. doi: 10.1007/s10278-007-9042-7.
This note describes a method to characterize the performances of image fusion software (Syntegra) with respect to accuracy and robustness. Computed tomography (CT), magnetic resonance imaging (MRI), and single-photon emission computed tomography (SPECT) studies were acquired from two phantoms and 10 patients. Image registration was performed independently by two couples composed of one radiotherapist and one physicist by means of superposition of anatomic landmarks. Each couple performed jointly and saved the registration. The two solutions were averaged to obtain the gold standard registration. A new set of estimators was defined to identify translation and rotation errors in the coordinate axes, independently from point position in image field of view (FOV). Algorithms evaluated were local correlation (LC) for CT-MRI, normalized mutual information (MI) for CT-MRI, and CT-SPECT registrations. To evaluate accuracy, estimator values were compared to limiting values for the algorithms employed, both in phantoms and in patients. To evaluate robustness, different alignments between images taken from a sample patient were produced and registration errors determined. LC algorithm resulted accurate in CT-MRI registrations in phantoms, but exceeded limiting values in 3 of 10 patients. MI algorithm resulted accurate in CT-MRI and CT-SPECT registrations in phantoms; limiting values were exceeded in one case in CT-MRI and never reached in CT-SPECT registrations. Thus, the evaluation of robustness was restricted to the algorithm of MI both for CT-MRI and CT-SPECT registrations. The algorithm of MI proved to be robust: limiting values were not exceeded with translation perturbations up to 2.5 cm, rotation perturbations up to 10 degrees and roto-translational perturbation up to 3 cm and 5 degrees.
本笔记描述了一种关于图像融合软件（Syntegra）在准确性和稳健性方面性能特征的方法。从两个模体和10名患者身上获取了计算机断层扫描（CT）、磁共振成像（MRI）和单光子发射计算机断层扫描（SPECT）研究数据。由一名放射治疗师和一名物理学家组成的两对人员通过解剖标志叠加的方式独立进行图像配准。每对人员共同完成并保存配准。将这两个解决方案进行平均以获得金标准配准。定义了一组新的估计器，以独立于图像视野（FOV）中的点位置来识别坐标轴中的平移和旋转误差。所评估的算法包括用于CT-MRI的局部相关性（LC）、用于CT-MRI的归一化互信息（MI）以及CT-SPECT配准。为了评估准确性，将估计器值与在模体和患者中所采用算法的极限值进行比较。为了评估稳健性，对来自一名样本患者的不同图像对齐方式进行了处理并确定了配准误差。LC算法在模体的CT-MRI配准中结果准确，但在10名患者中有3名超过了极限值。MI算法在模体的CT-MRI和CT-SPECT配准中结果准确；在CT-MRI中有一例超过了极限值，而在CT-SPECT配准中从未达到极限值。因此，对于CT-MRI和CT-SPECT配准，稳健性评估仅限于MI算法。MI算法被证明是稳健的：在平移扰动高达2.5厘米、旋转扰动高达10度以及旋转平移扰动高达3厘米和5度的情况下，均未超过极限值。
