Cardinal Marie-Hélène Roy, Meunier Jean, Soulez Gilles, Maurice Roch L, Therasse Eric, Cloutier Guy
Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital's Research Center, 2099 Alexandre de Sève, Montreal, QC H2L 2W5, Canada.
IEEE Trans Med Imaging. 2006 May;25(5):590-601. doi: 10.1109/TMI.2006.872142.
Intravascular ultrasound (IVUS) is a catheter based medical imaging technique particularly useful for studying atherosclerotic disease. It produces cross-sectional images of blood vessels that provide quantitative assessment of the vascular wall, information about the nature of atherosclerotic lesions as well as plaque shape and size. Automatic processing of large IVUS data sets represents an important challenge due to ultrasound speckle, catheter artifacts or calcification shadows. A new three-dimensional (3-D) IVUS segmentation model, that is based on the fast-marching method and uses gray level probability density functions (PDFs) of the vessel wall structures, was developed. The gray level distribution of the whole IVUS pullback was modeled with a mixture of Rayleigh PDFs. With multiple interface fast-marching segmentation, the lumen, intima plus plaque structure, and media layers of the vessel wall were computed simultaneously. The PDF-based fast-marching was applied to 9 in vivo IVUS pullbacks of superficial femoral arteries and to a simulated IVUS pullback. Accurate results were obtained on simulated data with average point to point distances between detected vessel wall borders and ground truth <0.072 mm. On in vivo IVUS, a good overall performance was obtained with average distance between segmentation results and manually traced contours <0.16 mm. Moreover, the worst point to point variation between detected and manually traced contours stayed low with Hausdorff distances <0.40 mm, indicating a good performance in regions lacking information or containing artifacts. In conclusion, segmentation results demonstrated the potential of gray level PDF and fast-marching methods in 3-D IVUS image processing.
血管内超声（IVUS）是一种基于导管的医学成像技术，对研究动脉粥样硬化疾病特别有用。它能生成血管的横截面图像，可对血管壁进行定量评估，提供有关动脉粥样硬化病变的性质以及斑块形状和大小的信息。由于超声斑点、导管伪影或钙化阴影，对大量IVUS数据集进行自动处理是一项重大挑战。一种基于快速行进法并使用血管壁结构灰度概率密度函数（PDF）的新型三维（3-D）IVUS分割模型被开发出来。用瑞利PDF混合模型对整个IVUS回撤图像的灰度分布进行建模。通过多界面快速行进分割，可同时计算血管壁的管腔、内膜加斑块结构和中膜层。基于PDF的快速行进法应用于9例股浅动脉的体内IVUS回撤图像以及一个模拟的IVUS回撤图像。在模拟数据上获得了准确结果，检测到的血管壁边界与真实值之间的平均点对点距离<0.072毫米。在体内IVUS图像上，分割结果与手动追踪轮廓之间的平均距离<0.16毫米，整体性能良好。此外，检测到的轮廓与手动追踪轮廓之间的最差点对点变化保持在较低水平，豪斯多夫距离<0.40毫米，表明在缺乏信息或包含伪影的区域表现良好。总之，分割结果证明了灰度PDF和快速行进法在3-D IVUS图像处理中的潜力。
