A Dynamic Phantom Model for Research and Quality Control in Cardiac Imaging
Bibliographic record
Abstract
Abstract Background. Accurate myocardial function and chamber quantification is of major interest. Lack of standardization between the different vendors, difficulties in performing validation studies and absence of reference systems for calibration have slowed down the expansion of such technologies in clinical practice. The goal of this study was to build a dynamic cardiac phantom to enable in vitro assessment of echo software algorithms. Methods. Using a polyvinyl alcoholic gel, we built a multimodality phantom model. Three pneumatic cylinders and a computer-driven control system allowed a 3D deformation capability. Sonomicrometer crystals were positioned on the phantom and used as reference for strain. The transducer tip was successively fixed at the apex of the gel for longitudinal strain assessment. Peaks of strain obtained by echo were then compared to the strain recorded by the sonomicrometers. The phantom was also scanned using an ultrasound machine with 3D capabilities and an MRI machine. MRI-volumes were compared to those obtained by 3D-echo. Results. We were able to apply various levels of longitudinal strain (-5 to -22%), and there was a strong and significant correlation between strain measured by tissue Doppler and sonomicrometers (R2 = 0.91, P = 0.0001) as well as between measurements by speckle tracking and sonomicrometers (R2 = 0.97, P < 0.0001). There was also a significant correlation between the volumes assessed by 3D-echo and MRI (R² = 0.94, P < 0.0001). Conclusion. This cardiac phantom model demonstrates realistic and complex deformation and is a promising tool to improve new echo algorithms, test their accuracy and standardize the measurements between different providers.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".