Department of Radiology, University of Cagliari-Azienda Ospedaliero Universitaria di Cagliari, Polo di Monserrato SS 554, Monserrato, Cagliari, Italy.
Neuroradiology. 2012 Nov;54(11):1207-14. doi: 10.1007/s00234-012-1040-x. Epub 2012 May 6.
The purpose of this study was to evaluate the potentialities of a semi-automated technique in the detection and measurement of the carotid artery plaque.
Twenty-two consecutive patients (18 males, 4 females; mean age 62 years) examined with MDCTA from January 2011 to March 2011 were included in this retrospective study. Carotid arteries are examined with a 16-multi-detector-row CT system, and for each patient, the most diseased carotid was selected. In the first phase, the carotid plaque was identified and one experienced radiologist manually traced the inner and outer boundaries by using polyline and radial distance method (PDM and RDM, respectively). In the second phase, the carotid inner and outer boundaries were traced with an automated algorithm: level-set-method (LSM). Data were compared by using Pearson rho correlation, Bland-Altman, and regression.
A total of 715 slices were analyzed. The mean thickness of the plaque using the reference PDM was 1.86 mm whereas using the LSM-PDM was 1.96 mm; using the reference RDM was 2.06 mm whereas using the LSM-RDM was 2.03 mm. The correlation values between the references, the LSM, the PDM and the RDM were 0.8428, 0.9921, 0.745 and 0.6425. Bland-Altman demonstrated a very good agreement in particular with the RDM method.
Results of our study indicate that LSM method can automatically measure the thickness of the plaque and that the best results are obtained with the RDM. Our results suggest that advanced computer-based algorithms can identify and trace the plaque boundaries like an experienced human reader.
本研究旨在评估半自动技术在检测和测量颈动脉斑块中的潜力。
本回顾性研究纳入了 2011 年 1 月至 2011 年 3 月期间接受 MDCTA 检查的 22 例连续患者（18 名男性，4 名女性；平均年龄 62 岁）。使用 16 排多层 CT 系统检查颈动脉，每位患者选择最严重的颈动脉。在第一阶段，通过手动使用多段线和径向距离法（PDM 和 RDM）识别颈动脉斑块，并由一位有经验的放射科医生描绘内、外边界。在第二阶段，使用自动算法——水平集方法（LSM）追踪颈动脉内、外边界。使用 Pearson rho 相关性、Bland-Altman 和回归分析来比较数据。
共分析了 715 个层面。使用参考 PDM 测量的斑块平均厚度为 1.86mm，而使用 LSM-PDM 测量的厚度为 1.96mm；使用参考 RDM 测量的斑块平均厚度为 2.06mm，而使用 LSM-RDM 测量的厚度为 2.03mm。参考值、LSM、PDM 和 RDM 之间的相关值分别为 0.8428、0.9921、0.745 和 0.6425。Bland-Altman 分析表明，尤其是与 RDM 方法相比，LSM 方法具有非常好的一致性。
本研究结果表明，LSM 方法可以自动测量斑块厚度，而与 RDM 方法相比，可获得最佳结果。我们的结果表明，先进的基于计算机的算法可以像有经验的读者一样识别和追踪斑块边界。
