Measuring coronary artery calcification using positron emission tomography-computed tomography attenuation correction images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: Cardiac computed tomography (CT) measured coronary artery calcium (CAC-CT) is a well-validated and accurate tool for estimating atherosclerotic burden and prognosis. Computed tomography attenuation correction (ACCT) obtained during cardiac positron emission tomography (PET) has been used to visually estimate CAC; however, quantification using a non-gated ACCT images has not been described. We sought to understand the relationship between CAC measured using cardiac computed tomography (CT) and CAC using ACCT images obtained during cardiac PET perfusion imaging. METHODS AND RESULTS: Patients with both CAC-CT and cardiac PET within 6 months of each other were identified. CAC-CT images were scored using the Agatston scoring method, while ACCT images were scored using different attenuation thresholds for calcium. CAC-CT and ACCT scores were compared. Between August 2007 and October 2010, 91 patients were included in the analysis. Interobserver reliability was excellent at all thresholds of detection tested. Pearson correlation was strongest between CAC-CT and ACCT at 50 HU threshold of detection (ACCT(50)). Implementing CAC categories (0, 1-100, 101-400, >400), there was a high degree of agreement between observers as well as between CAC-CT and ACCT(50). Correlation was best for lower CAC scores; however, as CAC-CT increased, ACCT(50) underestimated CAC. CONCLUSION: Quantifying CAC using ACCT images appears to be feasible and accurate. In a single cardiac PET examination, information regarding perfusion, LV function, flow quantification, and CAC can be obtained without additional radiation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 it