Deakin University, Geelong, Vic, Australia, Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences; Western Australian Bone Research Collaboration, Perth, WA, Australia.
Western Australian Bone Research Collaboration, Perth, WA, Australia; Institute for Health Research, The University of Notre Dame Australia, Fremantle, WA, Australia.
J Clin Densitom. 2018 Apr-Jun;21(2):260-268. doi: 10.1016/j.jocd.2017.07.002. Epub 2017 Aug 8.
Most imaging methods, including peripheral quantitative computed tomography (pQCT), are susceptible to motion artifacts particularly in fidgety pediatric populations. Methods currently used to address motion artifact include manual screening (visual inspection) and objective assessments of the scans. However, previously reported objective methods either cannot be applied on the reconstructed image or have not been tested for distal bone sites. Therefore, the purpose of the present study was to develop and validate motion artifact classifiers to quantify motion artifact in pQCT scans. Whether textural features could provide adequate motion artifact classification performance in 2 adolescent datasets with pQCT scans from tibial and radial diaphyses and epiphyses was tested. The first dataset was split into training (66% of sample) and validation (33% of sample) datasets. Visual classification was used as the ground truth. Moderate to substantial classification performance (J48 classifier, kappa coefficients from 0.57 to 0.80) was observed in the validation dataset with the novel texture-based classifier. In applying the same classifier to the second cross-sectional dataset, a slight-to-fair (κ = 0.01-0.39) classification performance was observed. Overall, this novel textural analysis-based classifier provided a moderate-to-substantial classification of motion artifact when the classifier was specifically trained for the measurement device and population. Classification based on textural features may be used to prescreen obviously acceptable and unacceptable scans, with a subsequent human-operated visual classification of any remaining scans.
大多数成像方法，包括外周定量计算机断层扫描（pQCT），都容易受到运动伪影的影响，尤其是在多动的儿科人群中。目前用于解决运动伪影的方法包括手动筛选（目视检查）和扫描的客观评估。然而，以前报道的客观方法要么不能应用于重建图像，要么尚未在远端骨部位进行测试。因此，本研究的目的是开发和验证运动伪影分类器，以量化 pQCT 扫描中的运动伪影。是否可以在包含胫骨和桡骨干骺端和骺端 pQCT 扫描的 2 个青少年数据集上使用纹理特征来提供足够的运动伪影分类性能进行了测试。第一个数据集分为训练（样本的 66%）和验证（样本的 33%）数据集。视觉分类被用作地面实况。在验证数据集中，使用新的基于纹理的分类器观察到中等至较大的分类性能（J48 分类器，kappa 系数为 0.57 至 0.80）。在将相同的分类器应用于第二个横截面数据集时，观察到轻微至公平（κ=0.01-0.39）的分类性能。总体而言，当分类器专门针对测量设备和人群进行训练时，这种新的基于纹理分析的分类器可以对运动伪影进行中等至较大的分类。基于纹理特征的分类可以用于预筛选明显可接受和不可接受的扫描，然后对任何剩余的扫描进行人工操作的视觉分类。
