In vivo monitoring of longitudinal changes in bone micro-architecture using high-resolution peripheral computed tomography
Bibliographic record
Abstract
Osteoporosis is a disease characterized by low bone quality and increased risk of fracture. In order to improve osteoporosis treatment, it is essential to monitor bone quality and its changes over time in healthy, diseased and treated bones. With the recent development of in vivo high-resolution peripheral quantitative computed tomography (HR-pQCT) it became possible to capture bone micro-architecture, an important determinant of bone quality, in humans. The aim of this dissertation was to propose new ways to analyze the resulting time series of three-dimensional (3D) image data to gain novel insight into bone behaviour. In a first step, a novel method for tracking and predicting micro-architectural changes using deformable image registration was validated. Applied to an osteoporotic and healthy pre-clinical model, this study demonstrated successful prediction of 3D architecture based on a time series of images without knowledge of disease state. Prior to extending the monitoring of changes to human bone, the problem of subject motion artifacts in HR-pQCT imaging was addressed. An automatic, fast and objective method was developed to quantify three separate components of subject motion using projection data. With this tool, guidelines for image quality management in the presence of subject motion were established. Understanding and managing these artifacts is pivotal for guaranteeing consistent image quality in large multi-centre studies. In addition to motion quantification, a novel method for compensating movement artifacts was developed. The proposed method for motion compensation paves the way for future research into improving image quality, potentially increasing viable data benefiting drug trials and studies of rare diseases with small sample sizes. Lastly, in order to monitor bone micro-architecture changes in humans, an automated registration methodology was devised to align 3D HR-pQCT images and techniques to visualize local architectural changes were developed. It was possible to visualize local changes due to normal bone remodelling, and in response to osteoporosis treatment, aiding interpretation of changes in traditional bone quality parameters. The developed methods form the foundation for tracking bone adaptation over time, ultimately furthering our understanding of bone mechanisms in humans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".