An algorithmic approach for quantitative motion artefact grading in HRpQCT medical imaging

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 instrument 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.

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