Imaging small dynamic lesions using positron emission tomography and computed tomography: an 18F-sodium fluoride valvular phantom study
Bibliographic record
Abstract
Abstract Aims 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) detects active microcalcification and predicts adverse outcomes including bioprosthetic valve deterioration. However, measuring small areas of 18F-NaF uptake within moving structures remains challenging, requiring further optimization. We developed a representative cardiac phantom to optimize 18F-NaF imaging of bioprosthetic valves. Methods and results We placed a bioprosthetic valve with two pockets sutured to the leaflets mimicking valvular lesions and a subvalvular ring mimicking the valve remnant into the phantom and injected each with 18F-radionuclide (1 μCi pockets, 4 μCi ring). We injected the cardiac chambers with iohexol and 18F-radionuclide (0.176 mCi) for background activity. PET and computed tomography (CT) images were acquired using a Siemens Biograph Vision high-resolution digital PET/CT scanner. We analysed target-to-background ratio (TBR) and signal-to-noise ratio (SNR) and subjective measures of image quality. We compared results with a human case of transcatheter aortic valve replacement. Initially the SNR and TBR in the phantom greatly exceeded those from human imaging. We reduced the scan duration used for reconstruction to 30 and 15 s, achieving comparable results (30 s vs. 15 s vs. patient: SNR 45.6 vs. 13.9 vs. 44.3, TBRmax 6.5 vs. 5.4 vs. 4.1, noise 10.2% vs. 8.8% vs. 12.0%). With motion correction, SNR and image quality improved in the phantom (30 s 135.8 vs. 45.6, 15 s 32.9 vs. 13.9) but remained similar in the human case (47.3 vs. 44.3). Conclusion A cardiac phantom can mimic clinical 18F-NaF valve bioprosthesis imaging, providing an opportunity to explore acquisition, reconstruction, and post-processing of 18F-NaF PET/CT for small mobile cardiac structures.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".