Low-density tissue scaffold imaging by synchrotron radiation propagation-based imaging computed tomography with helical acquisition mode
Bibliographic record
Abstract
Visualization of low-density tissue scaffolds made from hydrogels is important yet challenging in tissue engineering and regenerative medicine (TERM). For this, synchrotron radiation propagation-based imaging computed tomography (SR-PBI-CT) has great potential, but is limited due to the ring artifacts commonly observed in SR-PBI-CT images. To address this issue, this study focuses on the integration of SR-PBI-CT and helical acquisition mode ( i.e. SR-PBI-HCT) to visualize hydrogel scaffolds. The influence of key imaging parameters on the image quality of hydrogel scaffolds was investigated, including the helical pitch ( p ), photon energy ( E ) and the number of acquisition projections per rotation/revolution ( N p ), and, on this basis, those parameters were optimized to improve image quality and to reduce noise level and artifacts. The results illustrate that SR-PBI-HCT imaging shows impressive advantages in avoiding ring artifacts with p = 1.5, E = 30 keV and N p = 500 for the visualization of hydrogel scaffolds in vitro . Furthermore, the results also demonstrate that hydrogel scaffolds can be visualized using SR-PBI-HCT with good contrast while at a low radiation dose, i.e. 342 mGy (voxel size of 26 µm, suitable for in vivo imaging). This paper presents a systematic study on hydrogel scaffold imaging using SR-PBI-HCT and the results reveal that SR-PBI-HCT is a powerful tool for visualizing and characterizing low-density scaffolds with a high image quality in vitro . This work represents a significant advance toward the non-invasive in vivo visualization and characterization of hydrogel scaffolds at a suitable radiation dose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".