Cardiac gating with a pulse oximeter for dual-energy imaging
Bibliographic record
Abstract
The development and evaluation of a prototype cardiac gating system for double-shot dual-energy (DE) imaging is described. By acquiring both low- and high-kVp images during the resting phase of the cardiac cycle (diastole), heart misalignment between images can be reduced, thereby decreasing the magnitude of cardiac motion artifacts. For this initial implementation, a fingertip pulse oximeter was employed to measure the peripheral pulse waveform ('plethysmogram'), offering potential logistic, cost and workflow advantages compared to an electrocardiogram. A gating method was developed that accommodates temporal delays due to physiological pulse propagation, oximeter waveform processing and the imaging system (software, filter-wheel, anti-scatter Bucky-grid and flat-panel detector). Modeling the diastolic period allowed the calculation of an implemented delay, t(imp), required to trigger correctly during diastole at any patient heart rate (HR). The model suggests a triggering scheme characterized by two HR regimes, separated by a threshold, HR(thresh). For rates at or below HR(thresh), sufficient time exists to expose on the same heartbeat as the plethysmogram pulse [t(imp)(HR) = 0]. Above HR(thresh), a characteristic t(imp)(HR) delays exposure to the subsequent heartbeat, accounting for all fixed and variable system delays. Performance was evaluated in terms of accuracy and precision of diastole-trigger coincidence and quantitative evaluation of artifact severity in gated and ungated DE images. Initial implementation indicated 85% accuracy in diastole-trigger coincidence. Through the identification of an improved HR estimation method (modified temporal smoothing of the oximeter waveform), trigger accuracy of 100% could be achieved with improved precision. To quantify the effect of the gating system on DE image quality, human observer tests were conducted to measure the magnitude of cardiac artifact under conditions of successful and unsuccessful diastolic gating. Six observers independently measured the artifact in 111 patient DE images. The data indicate that successful diastolic gating results in a statistically significant reduction (p < 0.001) in the magnitude of cardiac motion artifact, with residual artifact attributed primarily to gross patient motion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".