Current clinical applications and potential perspective of micro-computed tomography in cardiovascular imaging: A systematic scoping review
Bibliographic record
Abstract
Micro-computed tomography (micro-CT) constitutes an emerging imaging technique, which can be utilized in cardiovascular medicine to study in-detail the microstructure of heart and vessels. This paper aims to systematically review the clinical utility of micro-CT in cardiovascular imaging and propose future applications of micro-CT imaging in cardiovascular research. A systematic scoping review was conducted by searching for original studies written in English according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews. Medline, Scopus, ClinicalTrials.gov, and the Cochrane library were systematically searched through December 11, 2020 to identify publications concerning micro-CT applications in cardiovascular imaging. Preclinical-animal studies and case reports were excluded. The Newcastle-Ottawa assessment scale for observational studies was used to evaluate study quality. In total, the search strategy identified 30 studies that report on micro-CT-based cardiovascular imaging and satisfy our eligibility criteria. Across all included studies, the total number of micro-CT scanned specimens was 1,227. Six studies involved postmortem 3D-reconstruction of congenital heart defects, while eleven studies described atherosclerotic vessel (coronary or carotid) characteristics. Thirteen other studies employed micro-CT for the assessment of medical devices (mainly stents or prosthetic valves). In conclusion, micro-CT is a novel imaging modality, effectively adapted for the 3D visualization and analysis of cardiac soft tissues and devices at high spatial resolution. Its increasing use could make significant contributions to our improved understanding of the histopathophysiology of cardiovascular diseases, and, thus, has the potential to optimize interventional procedures and technologies, and ultimately improve patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".