Minimal medical imaging can accurately reconstruct geometric bone models for musculoskeletal models
Minimal medical imaging can accurately reconstruct geometric bone models for musculoskeletal models
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最小的医学成像可以准确地重建肌肉骨骼模型的几何骨模型
DOI：
10.1371/journal.pone.0205628
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
T. Savage
中科院分区：
文献类型：
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作者：
Edin K. Suwarganda;L. Diamond;D. Saxby;D. Lloyd;A. Bryce;T. Savage
下肢肌肉骨骼模型中骨骼解剖结构的准确表达对于人体运动分析和仿真具有重要意义。数学方法可以利用骨骼的不完全成像，通过变形骨骼模型模板来重建几何骨骼模型，但这些方法的有效性尚未得到充分的探讨。本研究的目的是确定精确重建几何骨模型的最低成像要求。利用磁共振图像分割技术重建了14例健康成人骨盆和股骨的完整几何模型。根据每个完整的骨分割，创建三组不完整的分割（第1组是最不完整的），以测试成像不完整性对重建精度的影响。几何骨骼模型重建从完整的集合，三个不完整的集合，和两个运动捕捉为基础的方法。使用统计形状建模，然后通过Musculoskeletal Atlas项目客户端进行主机网格和局部网格拟合，从（内）完整集合中重建。基于运动捕获的方法的重建使用来自放置在解剖标志顶部的皮肤表面标记的位置数据和估计的关节中心位置作为统计形状建模和线性缩放的目标点。通过完整骨分割和重建骨模型之间的距离误差（mm）和重叠体积相似性（%）评价准确性，并使用重复测量方差分析进行统计学比较（p<0.05）。基于运动捕捉的方法产生了显着更高的距离误差比重建（在）完整的集合。骨盆体积相似性随不完整程度显著降低：完整集（92.70±1.92%）、集3（85.41±1.99%）、集2（81.22±3.03%）、集1（62.30±6.17%）、基于运动捕捉的统计形状建模（41.18±9.54%）和基于运动捕捉的线性缩放（26.80±7.19%）。股骨体积相似性也观察到类似趋势。结果表明，与完整的分割骨模型相比，对两个相关骨区域进行成像产生> 80%的重叠体积相似性。这些发现对改善特定对象肌肉骨骼模型的运动分析和模拟具有重要意义。
Accurate representation of subject-specific bone anatomy in lower-limb musculoskeletal models is important for human movement analyses and simulations. Mathematical methods can reconstruct geometric bone models using incomplete imaging of bone by morphing bone model templates, but the validity of these methods has not been fully explored. The purpose of this study was to determine the minimal imaging requirements for accurate reconstruction of geometric bone models. Complete geometric pelvis and femur models of 14 healthy adults were reconstructed from magnetic resonance imaging through segmentation. From each complete bone segmentation, three sets of incomplete segmentations (set 1 being the most incomplete) were created to test the effect of imaging incompleteness on reconstruction accuracy. Geometric bone models were reconstructed from complete sets, three incomplete sets, and two motion capture-based methods. Reconstructions from (in)complete sets were generated using statistical shape modelling, followed by host-mesh and local-mesh fitting through the Musculoskeletal Atlas Project Client. Reconstructions from motion capture-based methods used positional data from skin surface markers placed atop anatomic landmarks and estimated joint centre locations as target points for statistical shape modelling and linear scaling. Accuracy was evaluated with distance error (mm) and overlapping volume similarity (%) between complete bone segmentation and reconstructed bone models, and statistically compared using a repeated measure analysis of variance (p<0.05). Motion capture-based methods produced significantly higher distance error than reconstructions from (in)complete sets. Pelvis volume similarity reduced significantly with the level of incompleteness: complete set (92.70±1.92%), set 3 (85.41±1.99%), set 2 (81.22±3.03%), set 1 (62.30±6.17%), motion capture-based statistical shape modelling (41.18±9.54%), and motion capture-based linear scaling (26.80±7.19%). A similar trend was observed for femur volume similarity. Results indicate that imaging two relevant bone regions produces overlapping volume similarity > 80% compared to complete segmented bone models. These findings have implications for improving movement analysis and simulation with subject-specific musculoskeletal models.