Anatomically Corresponded Regional Analysis of Cartilage in Asymptomatic and Osteoarthritic Knees by Statistical Shape Modelling of the Bone
Anatomically Corresponded Regional Analysis of Cartilage in Asymptomatic and Osteoarthritic Knees by Statistical Shape Modelling of the Bone
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DOI：
10.1109/tmi.2010.2047653
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发表时间：
2010-08-01
影响因子：
10.6
通讯作者：
Taylor, Chris J.
中科院分区：
文献类型：
--
作者：
Williams, Tomos G.;Holmes, Andrew P.;Taylor, Chris J.
磁共振成像（MRI）正在成为测量骨关节炎（OA）软骨损失的首选方法，但目前的分析方法对于治疗性临床试验来说是不完善的。在本文中，我们提出并评估，在两个多中心多供应商的研究，一种新的方法，解剖学上对应的区域分析软骨（ACRAC），允许分析膝关节软骨形态在解剖学上对应的焦点区域定义在骨表面上。在我们的第一项研究中，从19名无症状的女性志愿者中获得了3-D膝关节MR图像，随后进行了骨和软骨的分割。最小描述长度（MDL）的统计形状模型（SSM）构建的分割骨表面，提供平均骨形状和一组密集的解剖学上对应的位置在每个单独的骨，其准确性进行了测量，使用重复图像从一个子集的志愿者。在这些位置沿沿着3-D法线测量骨表面的软骨厚度，产生相应的软骨厚度图。关节的功能分区定义的平均骨形状，并传播，使用的对应关系，每个人。ACRAC提高了再现性，特别是在中央，关节的承载子区域，与直接从分段软骨表面获得的体积的措施相比。在我们的第二项研究中，在基线和6个月时从31名患有膝关节OA的女性患者志愿者中获得MR图像。我们获得了手动分割的软骨，和自动分割的骨使用主动外观模型（AAM）建立从骨SSM的第一项研究。ACRAC能够检测到整个股骨（-5.57%，p = 0.01，年化）和内侧髁（-13.08%，p = 0.024，Bonferroni校正，年化）中心承重区域的显著厚度损失。我们的结论是，骨表面的统计形状建模定义了对单个关节大小或形状不变的对应关系，提供了与全隔室测量相比具有更高重现性的软骨焦点测量。它允许识别解剖学上等同的区域，并提供了识别关节的主要承重区域的能力，基于插补的发病前状态。该方法允许在一项小型研究中检测六个月以上软骨厚度的微小形态学变化，可能有助于OA疾病分析和治疗监测。
Magnetic resonance imaging (MRI) is emerging as the method of choice for measuring cartilage loss in osteoarthritis (OA), but current methods of analysis are imperfect for therapeutic clinical trials. In this paper, we present and evaluate, in two multicenter multivendor studies, a new method for anatomically corresponded regional analysis of cartilage (ACRAC) that allows analysis of knee cartilage morphology in anatomically corresponding focal regions defined on the bone surface. In our first study, 3-D knee MR Images were obtained from 19 asymptomatic female volunteers, followed by segmentations of the bone and cartilage. Minimum description length (MDL) statistical shape models (SSMs) were constructed from the segmented bone surfaces, providing mean bone shapes and a dense set of anatomically corresponding positions on each individual bone, the accuracy of which were measured using repeat images from a subset of the volunteers. Cartilage thicknesses were measured at these locations along 3-D normals to the bone surfaces, yielding corresponded cartilage thickness maps. Functional subregions of the joint were defined on the mean bone shapes, and propagated, using the correspondences, to each individual. ACRAC improved reproducibility, particularly in the central, load bearing subregions of the joint, compared with measures of volume obtained directly from the segmented cartilage surfaces. In our second study, MR Images were obtained from 31 female patient-volunteers with knee OA at baseline and six months. We obtained manual segmentations of the cartilage, and automatic segmentations of the bone using active appearance models (AAMs) built from the bone SSMs of the first study. ACRAC enabled the detection of significant thickness loss in the central, load-bearing regions of the whole femur (-5.57% p = 0.01,annualized) and the medial condyle (-13.08%, p = 0.024 Bonferroni corrected, annualized). We conclude that statistical shape modelling of bone surfaces defines correspondences invariant to individual joint size or shape, providing focal measures of cartilage with improved reproducibility compared to whole compartment measures. It permits the identification of anatomically equivalent regions, and provides the ability to identify the main load-bearing regions of the joint, based on the imputed premorbid state. The method permitted detection of tiny morphological change in cartilage thickness over six months in a small study, and may be useful for OA disease analysis and treatment monitoring.