CT imaging with ultra-high-resolution: Opportunities for cardiovascular imaging in clinical practice
Bibliographic record
Abstract
Cardiovascular computed tomography (CT) angiography has become an established alternative to invasive catheter angiography. However, imaging artifacts due to partial volume effects with current systems hinder accurate evaluation of calcified or stented segments. Increased spatial resolution may allow to overcome these barriers to precise delineation of vascular disease. Recent developments in CT hardware and reconstruction have enabled CT angiography with ultra-high spatial resolution (UHRCT). In this review we aim to describe the methods to achieve greater spatial resolution in CT that are either in clinical or preclinical stage. In addition, we provide an overview of the available clinical evidence including diagnostic accuracy studies supporting improved vascular assessment with this technology. The benefits that can be gleaned from the initial experiences with UHRCT are promising. Using UHRCT, more patients may receive non-invasive characterization of coronary atherosclerosis by overcoming the limitations of current CT spatial resolution in visualizing and quantifying calcified, stented or small diameter segments. UHRCT may potentially impact existing management pathways as well as contribute to better understanding of the underlying pathophysiology of both macro- and microvascular disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".