The impact of motion induced artifacts in the evaluation of HR-pQCT scans of the scaphoid bone: an assessment of inter- and intraobserver variability and quantitative parameters

Benedikt, Stefan; Horling, Lukas; Stock, Kerstin; Degenhart, Gerald; Pallua, Johannes; Schmidle, Gernot; Arora, Rohit

doi:10.21037/qims-22-345

Cited by 4 publications

(9 citation statements)

References 35 publications

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“…Imaging artifacts are major limitations of HR-pQCT measurements that can hamper the precision and reproducibility of HR-pQCT measurements by patient movements [ 13 , 14 , 15 , 16 , 17 , 18 ], particularly at the scaphoid bone. This was already confirmed in an earlier study revealing a considerable influence of motion on bone morphometry parameters of the scaphoid [ 19 ]. A certain amount of movement occurs in every individual (e.g., coughing, breathing, resting tremor, nervousness).…”

Section: Introductionsupporting

confidence: 80%

“…Especially in the scaphoid, motion artifacts are a severe problem, as they can lead to a significant bias among the quantitative measurements [ 19 ]. This can be explained by the anatomy of the scaphoid, which is different compared to the distal radius or the tibia.…”

Section: Discussionmentioning

confidence: 99%

“…Using the anterior–posterior scout view, the scaphoid was centered within the stacks ( Figure 2 ). To reduce patient movements during scanning, standard motion-restraining holders and the provided inflatable pads were used to immobilize the wrist in a thumb-up position [ 6 , 18 , 19 ]. Standard pre-settings were taken from the radius protocol provided by the manufacturer with a resolution of 60.7 µm isovoxels, an integration time of 46 ms, a current of 1460 µA and a voltage of 68 kV.…”

Section: Methodsmentioning

confidence: 99%

“…The manufacturer is well aware of this important issue and provides a visual grading scale (VGS), as described by Sode et al [ 14 ], to assess the extent of motion artifacts that classifies the artifacts into five quality grades ranging from “grade 1” for no visible motion artifacts to “grade 5” for severe motion artifacts. It is essential to correctly classify the artifacts, as any visual motion artifact causes significant falsification of the quantitative parameters [ 8 , 18 , 19 ]. For time reasons, however, operators usually focus on grading only specific slices of the HR-pQCT scan rather than the entire scan.…”

Section: Introductionmentioning

confidence: 99%

“…Poor image quality then influences the quantitative parameters of the scaphoid, with deviations of up to 20%. [ 19 ]…”

Section: Introductionmentioning

confidence: 99%

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Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid

Benedikt,

Zelger,

Horling

et al. 2024

Diagnostics

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In vivo high-resolution peripheral quantitative computed tomography (HR-pQCT) studies on bone characteristics are limited, partly due to the lack of standardized and objective techniques to describe motion artifacts responsible for lower-quality images. This study investigates the ability of such deep-learning techniques to assess image quality in HR-pQCT datasets of human scaphoids. In total, 1451 stacks of 482 scaphoid images from 53 patients, each with up to six follow-ups within one year, and each with one non-displaced fractured and one contralateral intact scaphoid, were independently graded by three observers using a visual grading scale for motion artifacts. A 3D-CNN was used to assess image quality. The accuracy of the 3D-CNN to assess the image quality compared to the mean results of three skilled operators was between 92% and 96%. The 3D-CNN classifier reached an ROC-AUC score of 0.94. The average assessment time for one scaphoid was 2.5 s. This study demonstrates that a deep-learning approach for rating radiological image quality provides objective assessments of motion grading for the scaphoid with a high accuracy and a short assessment time. In the future, such a 3D-CNN approach can be used as a resource-saving and cost-effective tool to classify the image quality of HR-pQCT datasets in a reliable, reproducible and objective way.

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Section: Introductionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Poor image quality then influences the quantitative parameters of the scaphoid, with deviations of up to 20%. [ 19 ]…”

Section: Introductionmentioning

confidence: 99%

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Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid

Benedikt,

Zelger,

Horling

et al. 2024

Diagnostics

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Influence of demographic factors on the occurrence of motion artefacts in HR-pQCT

Benedikt,

Rieser,

Schmidle

et al. 2023

Arch Osteoporos

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Summary The study shows a high incidence of motion artefacts in a central European population and a significant increase of those artefacts with higher age. These findings may impact on the design and conduct of future in vivo HR-pQCT studies or at least help to estimate the potential number of drop outs due to unusable image quality. Purpose Motion artefacts in high-resolution peripheral quantitative computed tomography (HR-pQCT) are challenging, as they introduce error into the resulting measurement data. The aim of this study was to assess the general occurrence of motion artefacts in healthy distal radius and to evaluate the influence of demographic factors. Methods The retrospective study is based on 525 distal radius second-generation HR-pQCT scans of 95 patients. All stacks were evaluated by two experienced observers and graded according to the visual grading scale recommended by the manufacturer, ranging from grade 1 (no visible motion artefacts) to grade 5 (severe motion artefacts). Correlations between demographic factors and image quality were evaluated using a linear mixed effects model analysis. Results The average visual grading was 2.7 (SD ± 0.7). Age and severity of motion artefacts significantly correlated (p = 0.026). Patients aged 65 years or above had an average image quality between grades 1 and 3 in 72.7% of cases, while patients younger than 65 had an average image quality between grades 1 and 3 in 91.9% of cases. Gender, smoking behaviour, and handedness had no significant influence on motion artefacts. Conclusion This study showed a high incidence of motion artefacts in a representative central European population, but also a significant increase of motion artefacts with higher age. This could impact further study designs by planning for a sufficiently large and if possible a more selective study population to gain a representative amount of high-quality image data.

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Research challenges and emerging futuristic evolution for 3D medical image processing

Upadhyaya,

Gupta

2024

Advances in Computers

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The impact of motion induced artifacts in the evaluation of HR-pQCT scans of the scaphoid bone: an assessment of inter- and intraobserver variability and quantitative parameters

Cited by 4 publications

References 35 publications

Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid

Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid

Influence of demographic factors on the occurrence of motion artefacts in HR-pQCT

Research challenges and emerging futuristic evolution for 3D medical image processing

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