Poster — Thur Eve — 05: Objective phantom‐based and porcine model comparison of filtered back projection, adaptive statistical iterative reconstruction and model based iterative reconstruction algorithms
Bibliographic record
Abstract
We performed objective image quality evaluation of three commercial reconstruction algorithms available on a GE DiscoveryCT750HD multi‐detector CT (MDCT) scanner at different dose levels. Using a Catphan500 phantom and a freshly euthanized pig carcass (∼ 36kg), we evaluated the noise and contrast‐to‐noise ratio (CNR) for the three reconstruction algorithms: filtered back‐projection (FBP), adaptive statistical iterative reconstruction (ASIR™) and model based iterative reconstruction (code named VEO™). At a dose of ∼ 1.5 mGy, ASIR™ offers noise reduction up to 54% with the phantom, while VEO™ reduces noise by up to 70% with the porcine model. At low doses, VEO™ offers better noise reduction capabilities over FBP and ASIR™, depending on helical pitch. The level of noise reduction increases as pitch increases. Similar trend was observed with CNR increases as VEO™ offered significantly better CNR over FBP and ASIR™. Compared to FBP, iterative algorithms reduce image noise and increase CNR. The model‐based iterative algorithm (VEO™) produced better noise and contrast resolution in both phantom and porcine images compared to FBP and ASIR™.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".