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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".