Four-dimensional (4D) echocardiography: analysis of temporal jitter due to asynchronous image acquisition
Bibliographic record
Abstract
Accurate left ventricular (LV) volume quantification in cardiac patient management is considerably important. Two-dimensional (2D) echocardiography causes discrepancies in LV volume measurement due to assumptions on the LV structure and the imaging plane position. 4D echocardiography provides an accurate visual representation of cardiac dynamics in three dimensions. 2D images are reconstructed into 3D images at each cardiac phase, and concatenated to obtain a four dimensional (4D) image. However, current methods of asynchronous image acquisition result in temporal jitter due to random phase shifts in the 3D image. With in vitro studies, we investigated the extent of temporal jitter in 4D echocardiography. 3D images of a myocardial motion phantom were reconstructed and analyzed for different cardiac phases. Our algorithm to quantify temporal jitter consisted of three steps: First, distance maps of the phantom surface from a reference plane were computed. Second, surface variation maps were derived and finally, 2D jitter maps were plotted and color coded to provide a measure of temporal jitter in each 3D image for each cardiac phase. The jitter maps had a standard deviation of 3.5 mm at peak systole and 1.5 mm at end diastole. Jitter of more than 1 mm limits the ability to diagnose cardiovascular 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".