Vrooman H A, Valstar E R, Brand G J, Admiraal D R, Rozing P M, Reiber J H
Department of Radiology, Leiden University Medical Center, The Netherlands.
J Biomech. 1998 May;31(5):491-8. doi: 10.1016/s0021-9290(98)00025-6.
Until recently, Roentgen Stereophotogrammetric Analysis (RSA) required the manual definition of all markers using a high-resolution measurement table. To automate this tedious and time-consuming process and to eliminate observer variabilities, an analytical software package has been developed and validated for the detection, identification, and matching of markers in RSA radiographs. The digital analysis procedure consisted of the following steps: (1) the detection of markers using a variant of the Hough circle-finder technique; (2) the identification and labeling of the detected markers; (3) the reconstruction of the three-dimensional position of the bone markers and the prosthetic markers; and (4) the computation of micromotion. To assess the influence of film digitization, the measurements obtained from nine phantom radiographs using two different film scanners were compared with the results obtained by manual processing. All markers in the phantom radiographs were automatically detected and correctly labeled. The best results were obtained with a Vidar VXR-12 CCD scanner, for which the measurement errors were comparable to the errors associated with the manual approach. To assess the in vivo reproducibility, 30 patient radiographs were analyzed twice with the manual as well as with the automated procedure. Approximately, 85% of all calibration markers and bone markers were automatically detected and correctly matched. The calibration errors and the rigid-body errors show that the accuracy of the automated procedure is comparable to the accuracy of the manual procedure. The rigid-body errors had comparable mean values for both techniques: 0.05 mm for the tibia and 0.06 mm for the prosthesis. The reproducibility of the automated procedure showed to be slightly better than that of the manual procedure. The maximum errors in the computed translation and rotation of the tibial component were 0.11 mm and 0.24, compared to 0.13 mm and 0.27 for the manual RSA procedure. The total processing time is less than 10 min per radiograph, including interactive corrections, compared to approximately 1 h for the manual approach. In conclusion, a new and widely applicable, computer-assisted technique has become available to detect, identify, and match markers in RSA radiographs and to assess the micromotion of endoprostheses. This new technique will be used in our clinic for our hip, knee, and elbow studies.
直到最近，X射线立体摄影测量分析（RSA）还需要使用高分辨率测量台手动定义所有标记物。为了使这个繁琐且耗时的过程自动化，并消除观察者的变异性，已经开发并验证了一个分析软件包，用于检测、识别和匹配RSA射线照片中的标记物。数字分析程序包括以下步骤：（1）使用霍夫圆查找技术的变体检测标记物；（2）对检测到的标记物进行识别和标记；（3）重建骨标记物和假体标记物的三维位置；（4）计算微动。为了评估胶片数字化的影响，将使用两种不同胶片扫描仪从九张体模射线照片中获得的测量结果与手动处理获得的结果进行了比较。体模射线照片中的所有标记物都被自动检测并正确标记。使用Vidar VXR - 12 CCD扫描仪获得了最佳结果，其测量误差与手动方法相关的误差相当。为了评估体内再现性，对30张患者射线照片分别使用手动和自动程序进行了两次分析。所有校准标记物和骨标记物中约85%被自动检测并正确匹配。校准误差和刚体误差表明，自动程序的准确性与手动程序相当。两种技术的刚体误差平均值相当：胫骨为0.05毫米，假体为0.06毫米。自动程序的再现性略优于手动程序。胫骨部件计算出的平移和旋转的最大误差分别为0.11毫米和0.24，而手动RSA程序为0.13毫米和0.27。包括交互式校正在内，每张射线照片的总处理时间不到10分钟，而手动方法约为1小时。总之，一种新的、广泛适用的计算机辅助技术已可用于检测、识别和匹配RSA射线照片中的标记物，并评估内置假体的微动。这项新技术将在我们的诊所用于髋、膝和肘部研究。
