An algorithmic approach for quantitative motion artefact grading in HRpQCT medical imaging
An algorithmic approach for quantitative motion artefact grading in HRpQCT medical imaging
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. The 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 1 to 5 scale for movement artefact. 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 scan. This is approached by analysing the jumps and shifts present in the raw projection data produced by the HRpQCT instrument, 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.
Cox, Thomas Alexander
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Mahmoodi, Sasan
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Harvey, Nicholas
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Moon, Rebecca
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Ward, Kate
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Westbury, Leo
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February 2024
Cox, Thomas Alexander
867d6a4c-1007-4ad7-b928-5149758b9884
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Harvey, Nicholas
ce487fb4-d360-4aac-9d17-9466d6cba145
Moon, Rebecca
1b5f4325-2f84-4bbf-83fe-de4892481c4b
Ward, Kate
39bd4db1-c948-4e32-930e-7bec8deb54c7
Westbury, Leo
74411e83-e3ee-48ca-a6d0-4d4888f7bdd5
Cox, Thomas Alexander, Mahmoodi, Sasan, Harvey, Nicholas, Moon, Rebecca, Ward, Kate and Westbury, Leo
(2024)
An algorithmic approach for quantitative motion artefact grading in HRpQCT medical imaging.
13th International Conference on Pattern Recognition Applications and Methods, , Rome, Italy.
24 - 26 Feb 2024.
8 pp
.
Record type:
Conference or Workshop Item
(Paper)
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. The 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 1 to 5 scale for movement artefact. 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 scan. This is approached by analysing the jumps and shifts present in the raw projection data produced by the HRpQCT instrument, 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.
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ICPRAM Paper
- Author's Original
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Published date: February 2024
Venue - Dates:
13th International Conference on Pattern Recognition Applications and Methods, , Rome, Italy, 2024-02-24 - 2024-02-26
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Local EPrints ID: 488377
URI: http://eprints.soton.ac.uk/id/eprint/488377
PURE UUID: a5d657d3-de94-4168-879c-d047b368b05d
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Date deposited: 21 Mar 2024 17:33
Last modified: 22 Mar 2024 02:47
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Contributors
Author:
Thomas Alexander Cox
Author:
Sasan Mahmoodi
Author:
Rebecca Moon
Author:
Leo Westbury
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