X-Ray Diffraction Enhanced Imaging as a Novel Method to Visualize Low-Density Scaffolds in Soft Tissue Engineering
Bibliographic record
Abstract
Scaffold visualization is challenging yet essential to the success of various tissue engineering applications. The aim of this study was to explore the potential of X-ray diffraction enhanced imaging (DEI) as a novel method for the visualization of low density engineered scaffolds in soft tissue. Imaging of the scaffolds made from poly(L-lactide) (PLLA) and chitosan was conducted using synchrotron radiation-based radiography, in-line phase-contrast imaging (in-line PCI), and DEI techniques as well as laboratory-based radiography. Scaffolds were visualized in air, water, and rat muscle tissue. Compared with the images from X-ray radiography and in-line PCI techniques, DEI images more clearly show the structure of the low density scaffold in air and have enhanced image contrast. DEI was the only technique able to visualize scaffolds embedded in unstained muscle tissue; this method could also define the microstructure of muscle tissue in the boundary areas. At a photon energy of 20 KeV, DEI had the capacity to image PLLA/chitosan scaffolds in soft tissue with a sample thickness of up to 4 cm. The DEI technique can be applied at high X-ray energies, thus facilitating lower in vivo radiation doses to tissues during imaging as compared to conventional radiography.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".