Synchrotron Imaging Techniques for Bone and Cartilage Tissue Engineering: Potential, Current Trends, and Future Directions
Bibliographic record
Abstract
Biomedical imaging is crucial to the success of bone/cartilage tissue engineering (TE) by providing detailed three-dimensional information on tissue-engineered scaffolds and associated bone/cartilage growth during the healing process. Synchrotron radiation (SR)-based biomedical imaging is an emerging technique for this purpose that has been drawing considerable recent attention. Due to the unique properties of synchrotron light, SR biomedical imaging can provide information that conventional X-ray imaging is not able to capture. SR biomedical imaging techniques notably differ from conventional imaging in both physics and implementation, thus varying with regard to both capability and popularity for biomedical imaging applications. In the earlier decade, synchrotron-based imaging was used in bone/cartilage TE to characterize bone/cartilage scaffolds and tissues as well as the varying degrees of success in reconstruction. However, several key issues should be addressed through research before SR biomedical imaging can be advanced to a noninvasive method for application to live animals and eventually to human patients. This review briefly presents recent developments in this area, focusing on different synchrotron-based biomedical imaging techniques and their advantages and limitations, as well as reported applications to bone and cartilage TE. Key issues and challenges are also identified and discussed along with recommendations for future research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".