Feasibility study of 3D cardiac imaging using a portable conebeam scanner
Bibliographic record
Abstract
While the Medtronic O-arm was developed for image-guidance applications during orthopedic procedures, it has potential to assist in cardiac surgical and electrophysiological applications; the purpose of this study was to evaluate the feasibility of using a mobile conebeam imaging system (O-arm) for gated cardiac imaging. In an in vivo study (two pigs), projection data from four independently acquired breath-held scans were combined to obtain cardiac gated 3D images. Projection images were acquired during the infusion of contrast agent and while tracking the ECG. Both standard and high-definition modes of the O-arm were evaluated. Projection data were retrospectively combined to generate images corresponding to systole and diastole; different acceptance windows were investigated. The contrast to noise ratio (CNR) between blood and myocardium was compared for the different gating strategies. Gated cardiac images were successfully reconstructed with as few as two scans combined (CNR = 2.5) and a window of 200 ms. Improved image quality was achieved when selecting views based on the minimum time from the selected phase point in the cardiac cycle, rather than a fixed window; in this case the effective temporal window increased to 475 ms for two scans. The O-arm has the potential to be used as a mobile cardiac imaging system, capable of three-dimensional 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".