Please don't move—Evaluating motion artifact from peripheral quantitative computed tomography scans using textural features

Document Type

Journal Article

Publication Title

Journal of Clinical Densitometry


Elsevier Inc


School of Medical and Health Sciences / Exercise Medicine Research Institute




Rantalainen, T., Chivers, P., Beck, B. R., Robertson, S., Hart, N. H., Nimphius, S., ... & Siafarikas, A. (2018). Please don't move—Evaluating motion artifact from peripheral quantitative computed tomography scans using textural features. Journal of Clinical Densitometry, 21(2), 260-268. doi:10.1016/j.jocd.2017.07.002

Available here.


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.



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