Characterization of cardiac quiescence from retrospective cardiac computed tomography using a correlation‐based phase‐to‐phase deviation measure
Bibliographic record
Abstract
PURPOSE: Accurate knowledge of cardiac quiescence is crucial to the performance of many cardiac imaging modalities, including computed tomography coronary angiography (CTCA). To accurately quantify quiescence, a method for detecting the quiescent periods of the heart from retrospective cardiac computed tomography (CT) using a correlation-based, phase-to-phase deviation measure was developed. METHODS: Retrospective cardiac CT data were obtained from 20 patients (11 male, 9 female, 33-74 yr) and the left main, left anterior descending, left circumflex, right coronary artery (RCA), and interventricular septum (IVS) were segmented for each phase using a semiautomated technique. Cardiac motion of individual coronary vessels as well as the IVS was calculated using phase-to-phase deviation. As an easily identifiable feature, the IVS was analyzed to assess how well it predicts vessel quiescence. Finally, the diagnostic quality of the reconstructed volumes from the quiescent phases determined using the deviation measure from the vessels in aggregate and the IVS was compared to that from quiescent phases calculated by the CT scanner. Three board-certified radiologists, fellowship-trained in cardiothoracic imaging, graded the diagnostic quality of the reconstructions using a Likert response format: 1 = excellent, 2 = good, 3 = adequate, 4 = nondiagnostic. RESULTS: Systolic and diastolic quiescent periods were identified for each subject from the vessel motion calculated using the phase-to-phase deviation measure. The motion of the IVS was found to be similar to the aggregate vessel (AGG) motion. The diagnostic quality of the coronary vessels for the quiescent phases calculated from the aggregate vessel (PAGG) and IVS (PIV S) deviation signal using the proposed methods was comparable to the quiescent phases calculated by the CT scanner (PCT). The one exception was the RCA, which improved for PAGG for 18 of the 20 subjects when compared to PCT (PCT = 2.48; PAGG = 2.07, p = 0.001). CONCLUSIONS: A method for quantifying the motion of specific coronary vessels using a correlation-based, phase-to-phase deviation measure was developed and tested on 20 patients receiving cardiac CT exams. The IVS was found to be a suitable predictor of vessel quiescence. The diagnostic quality of the quiescent phases detected by the proposed methods was comparable to those calculated by the CT scanner. The ability to quantify coronary vessel quiescence from the motion of the IVS can be used to develop new CTCA gating techniques and quantify the resulting potential improvement in CTCA image quality.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".