Characterization of Tissue Scaffolds Using Synchrotron Radiation Microcomputed Tomography Imaging
Bibliographic record
Abstract
Distinguishing from other traditional imaging, synchrotron radiation microcomputed tomography (SR-μCT) imaging allows for the visualization of three-dimensional objects of interest in a nondestructive and/or in situ way with better spatial resolution, deep penetration, relatively fast speed, and/or high contrast. SR-μCT has been illustrated promising for visualizing and characterizing tissue scaffolds for repairing or replacing damaged tissue or organs in tissue engineering (TE), which is of particular advance for longitudinal monitoring and tracking the success of scaffolds once implanted in animal models and/or human patients. This article presents a comprehensive review on recent studies of characterization of scaffolds based on SR-μCT and takes scaffold architectural properties, mechanical properties, degradation, swelling and wettability, and biological properties as five separate sections to introduce SR-μCT wide applications. We also discuss and highlight the unique opportunities of SR-μCT in various TE applications; conclude this article with the suggested future research directions, including the prospective applications of SR-μCT, along with its challenges and methods for improvement in the field of TE. Synchrotron radiation microcomputed tomography (SR-μCT) imaging allows for visualizing and characterizing three-dimensional objects in a nondestructive and/or in situ way with outstanding spatial resolution, deep penetration, relatively fast speed, and/or high contrast for wide ranges of tissue engineering (TE) samples. This article presents a comprehensive review on the recent studies and development based on SR-μCT to visualize and characterize scaffolds' different properties characterization in TE. This article also provides with the fundamentals and practice of SR-μCT to examine and evaluate the tissue scaffolds once implanted in animal models or human patients, along with the challenges and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".