梯形掌骨(TMC)关节是机械复杂的关节,通常会受到诸如骨关节炎等肌肉骨骼疾病的影响。定量体内TMC关节生物力学,如关节角度,由于关节在手中的位置,传统的基于反射标记的方法可能很困难。动态计算机断层扫描(CT)可以通过随时间连续捕获三维体积来促进TMC关节运动的量化。然而,动态CT数据集的后处理可能是时间密集型的,并且需要自动化方法来减少处理时间以允许应用于更大的临床研究。这项工作的目的是介绍一种快速,半自动管道从TMC关节的动态CT扫描中量化关节角度,并通过再现性和可重复性研究评估关节角度和平移计算中的相关误差。
使用被动运动装置用动态CT扫描十只尸体手,以径向外展内收运动移动拇指。还采集了静态CT扫描和高分辨率外周定量CT扫描以生成高分辨率骨网格。绑架-内扣,屈伸,和轴向旋转角度使用关节坐标系计算。重复性和重复性使用组内相关系数进行评估,Bland-Altman分析,和均方根误差。计算目标配准误差以评估与图像配准相关的误差。
我们发现屈伸可重复性好,外展-内收,和轴向旋转角度。对于所有三个角度,再现性都是中等的。联合翻译显示出比再现性更高的可重复性。关节变性较大的标本重复性和可重复性较低。我们发现,所产生的关节角度和平移的差异可能是由于多个评估者之间的段坐标系定义的差异,而不是由于注册错误。
所提出的半自动处理流水线速度快,可重复,在量化TMC关节角度和平移时,具有中等可重复性。这项工作使用手动选择的解剖标志,为动态CT扫描的TMC关节角度提供了一系列误差。
The trapeziometacarpal (TMC) joint is a mechanically complex joint and is commonly affected by musculoskeletal diseases such as osteoarthritis. Quantifying in vivo TMC joint biomechanics, such as joint angles, with traditional reflective marker-based methods can be difficult due to the joint\'s location in the hand. Dynamic computed tomography (CT) can facilitate the quantification of TMC joint motion by continuously capturing three-dimensional volumes over time. However, post-processing of dynamic CT datasets can be time intensive and automated methods are needed to reduce processing times to allow for application to larger clinical studies. The purpose of this work is to introduce a fast, semi-automated pipeline to quantify joint angles from dynamic CT scans of the TMC joint and evaluate the associated error in joint angle and translation computation by means of a reproducibility and repeatability study.
Ten cadaveric hands were scanned with dynamic CT using a passive motion device to move thumbs in a radial abduction-adduction motion. Static CT scans and high-resolution peripheral quantitative CT scans were also acquired to generate high-resolution bone meshes. Abduction-adduction, flexion-extension, and axial rotation angles were computed using a joint coordinate system. Reproducibility and repeatability were assessed using intraclass correlation coefficients, Bland-Altman analysis, and root mean square errors. Target registration errors were computed to evaluate errors associated with image registration.
We found good repeatability for flexion-extension, abduction-adduction, and axial rotation angles. Reproducibility was moderate for all three angles. Joint translations exhibited greater repeatability than reproducibility. Specimens with greater joint degeneration had lower repeatability and reproducibility. We found that the difference in resulting joint angles and translations were likely due to differences in segment coordinate system definition between multiple raters, rather than due to registration errors.
The proposed semi-automatic processing pipeline was fast, repeatable, and moderately reproducible when quantifying TMC joint angles and translations. This work provides a range of errors for TMC joint angles from dynamic CT scans using manually selected anatomical landmarks.