TU‐H‐CAMPUS‐IeP2‐03: Development of 3D Printed Coronary Phantoms for In‐Vitro CT‐FFR Validation Using Data from 320‐ Detector Row Coronary CT Angiography
Bibliographic record
Abstract
Purpose: To validate Computed Tomography Fractional Flow Reserve (CT‐FFR) measurements with accurate 3D printed coronary phantoms. Methods: DICOM data from four phases in two patients imaged with a standard 320 × 0.5mm coronary CT acquisition (70–80% cardiac cycle) underwent semi‐automated segmentation using a research workstation. Both patients had a >50% stenosis from the clinical image interpretation. Each volume was saved as a Stereo Lithographic (STL) file with 250 micron resolution. The 3D geometries were qualitatively assessed; the best of the four phases was 3D printed using a Stratasys Eden260V printer in Tango+, a rubber‐like material that roughly emulates mechanical properties of human vasculature. We connected the model to a programmable pump and measured the pressure drop using pressure sensors embedded proximal and distal to the arterial stenosis. Next, the STL files used for the 3D printed models were uploaded in the ANSYS meshing tool (ICEM CFD 16.1). A standard meshing process was applied and the meshed geometry was directly imported in the ANSYS Fluent for Computational Flow Dynamics simulations. The CFD simulations were used to calculate the CT‐FFR and compared to the bench top FFR measured in the 3D printed phantoms. Results: FFR‐CT measurements and phantoms were completed in within an hour after the segmentation. Patient 1 had a 60% stenosis that resulted in a CT‐FFR of 0.68. The second case had a 50% stenosis and a CT‐FFR of 0.75. The average bench top FFR measurements were 0.72 and 0.80, respectively. Conclusion: This pilot investigation demonstrated the use of a bench‐top coronary model for CT‐FFR validation. The measurements and the CFD simulations agreed within 6%. Project supported by Support: Toshiba America Medical Systems Corp.and NIH grant R01‐EB002873. Project supported by Toshiba America Medical Systems Corp.and partial support from NIH grant R01‐EB002873
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".