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

Timo Rantalainen1, Paola Chivers2, Belinda R Beck3

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Summary
This summary is machine-generated.

This study developed a texture-based classifier to detect motion artifacts in pediatric peripheral quantitative computed tomography (pQCT) scans. The classifier showed moderate to substantial performance, aiding in prescreening scans for improved accuracy.

Keywords:
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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Radiology

Background:

  • Motion artifacts are a significant challenge in pediatric peripheral quantitative computed tomography (pQCT) imaging.
  • Current methods for motion artifact detection, including manual screening and objective assessments, have limitations, especially for distal bone sites.

Purpose of the Study:

  • To develop and validate novel motion artifact classifiers for quantifying motion artifacts in pQCT scans.
  • To assess the performance of texture-based features for motion artifact classification in adolescent pQCT datasets.

Main Methods:

  • Development of a texture-based classifier using J48 algorithm.
  • Validation against visual classification as ground truth on two adolescent pQCT datasets (tibial and radial diaphyses and epiphyses).
  • Dataset 1 split into training (66%) and validation (33%) sets.

Main Results:

  • The texture-based classifier achieved moderate to substantial classification performance (kappa coefficients 0.57–0.80) in the validation dataset.
  • Performance was reduced (slight to fair, kappa 0.01–0.39) when applied to a second, independent cross-sectional dataset.
  • Classifier performance was dependent on specific measurement devices and populations.

Conclusions:

  • Texture-based feature analysis offers a viable method for classifying motion artifacts in pQCT scans.
  • The developed classifier can effectively prescreen scans, reducing the need for extensive manual review.
  • Further refinement may be needed for broader application across different devices and populations.