Multi-Modal Imaging to Assess the Interaction Between Inflammation and Bone Damage Progression in Inflammatory Arthritis
Bibliographic record
Abstract
Combining results from multiple imaging techniques (i.e., multi-modal imaging) through image registration can result in the better characterization of joint tissue characteristics. In the context of inflammatory arthritis conditions, high-resolution peripheral quantitative computed tomography (HR-pQCT) provides excellent bone contrast while magnetic resonance imaging (MRI) provides superior contrast and resolution of soft tissue and inflammatory characteristics. Superimposing these imaging results upon each other provides a robust characterization of the joint. In a preliminary study of nine rheumatoid arthritis (RA) participants in clinical remission, we acquired HR-pQCT and MR images of their 2nd and 3rd metacarpophalangeal (MCP) joints at two timepoints 6 months apart. We present the benefits of a multi-modal imaging approach, in which we demonstrate the ability to localize regions of inflammation with subtle changes in bone erosion volume. Using HR-pQCT and MRI to visualize bone damage and inflammation, respectively, will improve our understanding of the impact that subclinical inflammation has on bone damage progression, and demonstrating if bone repair occurs where inflammation is resolved. The presented multi-modal imaging technique has the potential to study the progression of bone damage in relation to inflammation that otherwise would not be possible with either imaging technique alone. The multi-modal image registration technique will be helpful to understanding the development and pathogenesis of RA-associated bone erosions. Additionally, multi-modal imaging may provide a technique to probe the tissue-level changes that occur as a result of treatment regimes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".