Wicklein Julia, Kunze Holger, Kalender Willi A, Kyriakou Yiannis
Institute of Medical Physics, University of Erlangen-Nürnberg, Henkestraße 91, 91052 Erlangen, Germany.
Med Phys. 2012 Aug;39(8):4918-31. doi: 10.1118/1.4736532.
Misalignment artifacts are a serious problem in medical flat-detector computed tomography. Generally, the geometrical parameters, which are essential for reconstruction, are provided by preceding calibration routines. These procedures are time consuming and the later use of stored parameters is sensitive toward external impacts or patient movement. The method of choice in a clinical environment would be a markerless online-calibration procedure that allows flexible scan trajectories and simultaneously corrects misalignment and motion artifacts during the reconstruction process. Therefore, different image features were evaluated according to their capability of quantifying misalignment.
Projections of the FORBILD head and thorax phantoms were simulated. Additionally, acquisitions of a head phantom and patient data were used for evaluation. For the reconstruction different sources and magnitudes of misalignment were introduced in the geometry description. The resulting volumes were analyzed by entropy (based on the gray-level histogram), total variation, Gabor filter texture features, Haralick co-occurrence features, and Tamura texture features. The feature results were compared to the back-projection mismatch of the disturbed geometry.
The evaluations demonstrate the ability of several well-established image features to classify misalignment. The authors elaborated the particular suitability of the gray-level histogram-based entropy on identifying misalignment artifacts, after applying an appropriate window level (bone window).
Some of the proposed feature extraction algorithms show a strong correlation with the misalignment level. Especially, entropy-based methods showed very good correspondence, with the best of these being the type that uses the gray-level histogram for calculation. This makes it a suitable image feature for online-calibration.
在医学平板探测器计算机断层扫描中，错位伪影是一个严重问题。通常，重建所必需的几何参数由先前的校准程序提供。这些程序耗时且后续使用存储的参数对外部影响或患者移动很敏感。临床环境中的首选方法将是一种无标记在线校准程序，该程序允许灵活的扫描轨迹，并在重建过程中同时校正错位和运动伪影。因此，根据不同图像特征量化错位的能力对其进行了评估。
模拟了FORBILD头部和胸部体模的投影。此外，还使用了头部体模的采集数据和患者数据进行评估。在几何描述中引入了不同来源和大小的错位用于重建。通过熵（基于灰度直方图）、总变差、Gabor滤波器纹理特征、Haralick共生特征和Tamura纹理特征对所得体积进行分析。将特征结果与受干扰几何的反投影不匹配进行比较。
评估表明几种成熟的图像特征具有对错位进行分类的能力。作者阐述了在应用适当的窗宽（骨窗）后，基于灰度直方图的熵在识别错位伪影方面的特殊适用性。
一些提出的特征提取算法与错位水平有很强的相关性。特别是，基于熵的方法显示出很好的一致性，其中最好的是使用灰度直方图进行计算的类型。这使其成为适合在线校准的图像特征。
