CT investigation of patient-specific phantoms with coronary artery disease
Bibliographic record
Abstract
Purpose: To develop coronary phantoms that mimic patient geometry and coronary blood flow conditions for CT imaging optimization and software validation. Materials and Methods: Five patients with varying degrees of coronary artery disease underwent 320-detector row coronary CT angiography (Aquilion ONE, Canon Medical Systems). The aorta and coronary arteries were segmented using a Vitrea Workstation (Vital Images). Patient anatomy was manipulated in Autodesk Meshmixer and 3D printed in Tango+, a flexible polymer, using an Eden260V printer (Stratasys). Phantoms were connected to a pump that simulates physiologic pulsatile flow waveforms, correlated with a simulated ECG signal. Distal resistance was optimized for all three coronary vessels until physiologically accurate flow rates and pressure were observed. Phantoms underwent coronary CT Angiography (CTA) using a standard acquisition protocol and contrast mixed in the flow loop. Image data from the phantoms were input to a CT-FFR research software and compared to those derived from the clinical data. Results: All five patient-specific phantoms were successfully imaged with CTA and the images were analyzed by the CTFFR software. The phantom CT-FFR results had a mean difference of -5.4% compared to the patient CT-FFR results. Patient and phantom CT-FFR agreed for all three coronary vessels, with Pearson correlations r = 0.83, 0.68, 0.62 (LAD, LCX, RCA). Conclusions: 3D printed patient-specific phantoms can be manipulated through material properties, flow regulations, and a pulsatile waveform to create accurate flow conditions for CT based experimentation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".