Photon-counting computed tomography: a revolution in cardiac imaging
Bibliographic record
Abstract
Photon-counting detector computed tomography (PCD-CT) is an emerging advanced CT technology that differs from conventional energy-integrating detector CT (EID-CT) scanners in its ability to directly convert incident X-ray photon energies into electrical signals. Since its commercial market introduction in 2021, several studies have identified advantages of this new technology in the field of cardiovascular imaging, including improved image quality due to an enhanced contrast-to-noise ratio, superior spatial resolution, reduced artefacts, and a reduced radiation dose. Nonetheless, radiation exposure with PCD-CT can vary depending on the acquisition mode and protocol used, highlighting the importance of tailored optimization in clinical practice. In particular, this new technology appears feasible in patients with a high plaque burden independent of morphology, unravelling new phenotypes of plaque, in patients with stents due to the improved visualization of the coronary in-stent lumen, potentially expanding the scope of CT. Early studies and clinical experience support these potential applications of PCD-CT in cardiovascular diagnostics, suggesting workflow optimization and improved patient management. In this review, the authors aim to describe the role of PCD-CT not only in the exclusion of coronary artery disease, grading of coronary stenosis and plaque imaging, but also in evaluation of cardiac chambers and myocardium for tissue characterization trying to understand whether PCD-CT has yet led to a true revolution and significant progress in cardiovascular imaging.
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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 | 0.005 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".