Department of Electrical and Computer Engineering, The University of Iowa, 4016 Seamans Center for the Engineering Arts and Sciences, lowa City, lowa 52242, USA.
Phys Med Biol. 2013 Feb 7;58(3):715-33. doi: 10.1088/0031-9155/58/3/715. Epub 2013 Jan 15.
To present a new method of estimating 3D positions of the ipsi-lateral hemi-diaphragm apex (IHDA) from 2D projection images of mega-voltage cone beam CT (MVCBCT). The detection framework reconstructs a 3D volume from all the 2D projection images. An initial estimated 3D IHDA position is determined in this volume based on an imaging processing pipeline, including Otsu thresholding, connected component labeling and template matching. This initial position is then projected onto each 2D projection image to create a region of interest (ROI). To accurately detect the IHDA position in 2D projection space, two methods, dynamic Hough transform (DHT) and a tracking approach based on a joint probability density function (PDF) are developed. Both methods utilize a double-parabola model to fit the 2D diaphragm boundary. The 3D IHDA motion in the superior-inferior (SI) direction is estimated from the initial static 3D position and the detected 2D positions in projection space. The two Hough-based detection methods are tested on 35 MVCBCT scans from 15 patients. The detection is compared to manually identified IHDA positions in 2D projection space by three clinicians. An average and standard deviation of 4.252 ± 3.354 and 2.485 ± 1.750 mm was achieved for DHT and tracking-based approaches respectively, compared with the inter-expert variance among three experts of 1.822 ± 1.106 mm. Based on the results of the scans, the PDF tracking-based approach appears more robust than the DHT. The combination of the automatic ROI localization and the tracking-based approach is a quicker and more accurate method of extracting 3D IHDA motion from 2D projection images.
提出一种从兆伏锥形束 CT(MVCBCT)的 2D 投影图像估计对侧半膈肌顶点(IHDA)3D 位置的新方法。该检测框架从所有 2D 投影图像重建 3D 体积。基于成像处理管道,包括 Otsu 阈值处理、连通分量标记和模板匹配,在该体积中确定初始估计的 3D IHDA 位置。然后,将该初始位置投影到每个 2D 投影图像上,以创建感兴趣区域(ROI)。为了在 2D 投影空间中准确检测 IHDA 位置,开发了两种方法,即动态霍夫变换(DHT)和基于联合概率密度函数(PDF)的跟踪方法。这两种方法都利用双抛物线模型来拟合 2D 膈肌边界。根据初始静态 3D 位置和在投影空间中检测到的 2D 位置,估计 3D IHDA 在上下(SI)方向上的运动。在 15 名患者的 35 个 MVCBCT 扫描中测试了两种基于霍夫的检测方法。通过三位临床医生在 2D 投影空间中与手动识别的 IHDA 位置进行比较。DHT 和基于跟踪的方法分别实现了 4.252 ± 3.354 和 2.485 ± 1.750mm 的平均值和标准差,而三位专家之间的专家间方差为 1.822 ± 1.106mm。基于扫描结果,基于 PDF 的跟踪方法似乎比 DHT 更稳健。自动 ROI 定位和基于跟踪的方法相结合,是从 2D 投影图像中提取 3D IHDA 运动的更快、更准确的方法。