Sci—Thur PM: Imaging — 07: Assessment of iterative reconstruction algorithms in four commercial MDCT scanners: a phantom study
Bibliographic record
Abstract
We performed an objective phantom‐based image quality evaluation of five commercial iterative reconstruction methods available on four different multi‐detector CT (MDCT) scanners at different dose levels as well as the conventional filtered back‐projection (FBP) reconstruction. Using the Catphan500 phantom, we evaluated the CT uniformity, CT number accuracy, noise, modulation transfer function (MTF) and noise‐power spectrum (NPS). The reconstruction algorithms were evaluated over a CTDIvol range of 0.75 – 18.7 mGy on four major MDCT scanners: GE DiscoveryHD750 (ASIR™ and VEO™); Siemens Somatom Definition AS+ (SAFIRE™); Toshiba Aquilion64 (AIDR3D™); and Philips Ingenuity iCT256 (iDose4™). Images were reconstructed using FBP and the respective iterative reconstructions on all scanners. CT number accuracy and CT uniformity were not affected by the choice of reconstruction method on all scanners. In the dose range of 1.3 – 1.5 mGy, noise reduction compared to FBP using iterative reconstruction was 11% – 51% on GE; 10% – 52% on Siemens; 49% – 62% on Toshiba; and 13% – 44% on Philips scanners. Most algorithms did not affect the MTF, except for VEO™ which produced an increase in the limiting resolution of up to 30%. NPS peak shifted towards lower frequencies NPS amplitude decreased with all iterative algorithms. Compared to FBP, iterative algorithms reduce image noise and increase CNR at the same dose. Noise propagation is Poisson distributed with FBP and ASIR™, while VEO alters the noise dependence with dose. The iterative algorithms available on different scanners achieved different levels of noise reduction and spatial resolution improvements.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".