Tissue Scaffolds Characterization Using Synchrotron Radiation Micro-Computed Tomography with Helical Acquisition Mode
Bibliographic record
Abstract
In the field of tissue engineering, hydrogel scaffolds have gained significant attention due to their unique properties, due to their unique properties. Accurate imaging techniques are essential for studying the internal structure and properties of these scaffolds. Hydrogel scaffolds have very low density and synchrotron radiation micro-computed tomography (SR-μCT) shows high contrast with three-dimensional and non-invasive characterization. Despite many advantages, SR-μCT image quality for hydrogel still needs to be improved due to common ring artifacts resulted from systematic errors or defects on the scintillator, monochromator, or filters. Such artifacts usually reduce the accuracy when visualizing and charactering samples. Methods have been developed to reduce the ring artifacts, e.g., low-pass filtering algorithm, but these approaches suffer from limitations. This work integrates SR-μCT with the helical acquisition mode (SR-μHCT) to avoid the ring artifacts issues. SR-μHCT involves two motions, a horizontal rotation and a vertical motion which can spread the intensity of ring artifacts over larger regions in the vertical direction, therefore reducing the effects of artifacts.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".