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Authors: Thomas Cox 1 ; Sasan Mahmoodi 1 ; Elizabeth Curtis 2 ; Nicholas Fuggle 2 ; Rebecca Moon 2 ; 3 ; Kate Ward 2 ; Leo Westbury 2 and Nicholas Harvey 2 ; 4

Affiliations: 1 Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, University Road, Southampton, U.K. ; 2 MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, U.K. ; 3 Paediatric Endocrinology, Southampton Children’s Hospital, University Hospital Southampton NHS Foundation Trust, Southampton, U.K. ; 4 National Institute for Health Research (NIHR) Southampton Biomedical Research Centre, University of Southampton, and University Hospital Southampton NHS Foundation Trust, U.K.

Keyword(s): Medical Imaging, HRpQCT, High Resolution Peripheral Computed Tomography, Computed Tomography, Motion Artefact, Artefact Detection.

Abstract: High Resolution Peripheral Quantitative Computed Tomography (HRpQCT) is a modern form of medical imaging that is used to extract detailed internal texture and structure information from non-invasive scans. This greater resolution means HRpQCT images are more vulnerable to motion artefact than other existing bone imaging methods. Current practice is for scan images to be manually reviewed and graded on a one to five scale for movement artefact, where analysis of scans with the most severe grades of movement artefact may not be possible. Various approaches to automatically detecting motion artefact in HRpQCT images have been described, but these typically rely on classifying scans based on the qualitative manual gradings instead of determining the amount of artefact. This paper describes research into quantitatively calculating the degree of motion affecting an HRpQCT. This is approached by analysing the jumps and shifts present in the raw projection data produced by the HRpQCT instrum ent scanner, rather than using the reconstructed cross-sectional images. The motivation and methods of this approach are described, and results are provided, along with comparisons to existing work. (More)

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Paper citation in several formats:
Cox, T.; Mahmoodi, S.; Curtis, E.; Fuggle, N.; Moon, R.; Ward, K.; Westbury, L. and Harvey, N. (2024). An Algorithmic Approach for Quantitative Motion Artefact Grading in HRpQCT Medical Imaging. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 833-840. DOI: 10.5220/0012434900003654

@conference{icpram24,
author={Thomas Cox. and Sasan Mahmoodi. and Elizabeth Curtis. and Nicholas Fuggle. and Rebecca Moon. and Kate Ward. and Leo Westbury. and Nicholas Harvey.},
title={An Algorithmic Approach for Quantitative Motion Artefact Grading in HRpQCT Medical Imaging},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={833-840},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012434900003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - An Algorithmic Approach for Quantitative Motion Artefact Grading in HRpQCT Medical Imaging
SN - 978-989-758-684-2
IS - 2184-4313
AU - Cox, T.
AU - Mahmoodi, S.
AU - Curtis, E.
AU - Fuggle, N.
AU - Moon, R.
AU - Ward, K.
AU - Westbury, L.
AU - Harvey, N.
PY - 2024
SP - 833
EP - 840
DO - 10.5220/0012434900003654
PB - SciTePress