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J Clin Densitom. 2018 Apr - Jun;21(2):260-268. doi: 10.1016/j.jocd.2017.07.002. Epub 2017 Aug 8.

Please Don't Move-Evaluating Motion Artifact From Peripheral Quantitative Computed Tomography Scans Using Textural Features.

Author information

1
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. Electronic address: t.rantalainen@deakin.edu.au.
2
Western Australian Bone Research Collaboration, Perth, WA, Australia; Institute for Health Research, The University of Notre Dame Australia, Fremantle, WA, Australia.
3
Menzies Health Institute Queensland, Bone Densitometry Research Laboratory, School of Allied Health Sciences, Griffith University, Gold Coast, Qld, Australia.
4
Institute for Sport, Exercise & Active Living, Victoria University, Melbourne, Vic, Australia.
5
Western Australian Bone Research Collaboration, Perth, WA, Australia; Exercise Medicine Research Institute, Edith Cowan University, Perth, WA, Australia.
6
Western Australian Bone Research Collaboration, Perth, WA, Australia; School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia.
7
Western Australian Bone Research Collaboration, Perth, WA, Australia; School of Health Sciences, The University of Notre Dame Australia, Fremantle, WA, Australia.
8
Western Australian Bone Research Collaboration, Perth, WA, Australia; School of Health Sciences, The University of Notre Dame Australia, Fremantle, WA, Australia; Department of Endocrinology, Princess Margaret Hospital, Perth, WA, Australia; School of Paediatrics and Child Health, University of Western Australia, Nedlands, WA, Australia.

Abstract

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.

KEYWORDS:

Bone QCT; machine learning; morphology; precision; repeatability

PMID:
28801168
DOI:
10.1016/j.jocd.2017.07.002

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