GW24-e2927 Low dose quantitative myocardial CT perfusion with adaptive statistical iterative reconstruction: a microspheres validation study
Bibliographic record
Abstract
Objectives We validated the effectiveness of adaptive statistical iterative reconstruction (ASIR, GE Healthcare (GE)) for minimising image noise in low dose quantitative myocardial perfusion (MP) imaging against microspheres MP measurement. Methods Iodinated contrast (Isovue 370, 0.7 mgI × kg-1) was injected at 3 to 4 mls-1 into 68 ± 25 kg normal pigs via an ear vein and the heart was scanned using a GE Discovery 750HD scanner with a prospectively ECG triggered dynamic protocol (Snapshot Pulse (SSP), GE): axial scan every 1–2 heart beats for 22 scans using 140 kV, 0.35 s gantry period and 80 mA (normal dose). MP measurement was repeated with the x-ray tube current reduced to 20 mA (low dose). The normal- and low-dose SSP images were reconstructed using filtered backprojection (FBP) (SSP80) and both FBP (SSP20FBP) and ASIR (SSP20ASIR) respectively. All images were corrected for beam hardening from which MP maps were generated using CT Perfusion (GE). After the CT perfusion studies, fluorescent microspheres were injected into the left atrial appendage of the heart to measure MP. Mean MP measured with microspheres and the three CT image sets in 45 segments from the lateral, apical and septal wall in 15 slices from three pigs were compared using linear regression and Bland-Altman analysis. Effective dose (ED) of each SSP protocol was estimated from the dose-length product provided by the scanner. Results SSP80 images exhibited the highest correlation with microspheres (R = 0.69) compared to SSP20ASIR (R = 0.60) and SSP20FBP (R = 0.57). SSP80 images also showed the smallest difference in mean MP from microspheres and narrowest limits of agreement with microspheres (7.0 and -32.9 to 46.8 ml × min-1 × 100g-1 (80)) compared to SSP20ASIR (11.3 and -35.3 to 57.8 (93)) and SSP20FBP (15.7 and -32.8 to 64.1 (97)). ED of the SSP80 and SSP20 protocols were 4.5 and 1.1 mSv respectively. Conclusions Noise in low dose SSP images reconstructed with FBP was excessive which led to less accurate and reproducible MP estimation with CT Perfusion but such errors could be reduced with ASIR. With the proposed image acquisition and reconstruction approaches, MP measurement with low dose CT Perfusion is a feasible alternative to MRI and SPECT for studying ischemic heart disease.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".