Poster — Thur Eve — 04: Characterizations of image quality for CT with iterative reconstruction algorithm — development of a routine quality control metric
Bibliographic record
Abstract
Iterative reconstruction algorithms (IRA) are available on modern CT systems. We are investigating the feasibility of routinely and objectively characterizing the image quality of CT with IRA. Noise‐equivalent‐quanta (NEQ) conceptually and potentially can be the metric of image quality assessment for system performance. CT images of the third module of an ACR phantom were acquired on a GE LightSpeed VCT system using 5 different mA ranging from 25–400 with 6 different ASiR levels ranging from 0–100. MTF and NPS measurements were obtained from these CT images using the method described by Friedman et al1. It was found that MTF acquired using even low mA is reliable and consistent. MTF from data acquired using 400mA provides decreased MTF performance for the reason yet to be determined. There is incremental improvement in MTF by the incremental increase in ASiR level. While ASiR makes no improvements in NPS and NEQ at low spatial frequency, the ASiR increment level of 25 has the same effect of doubling the dose to the NPS and NEQ performances at frequency 0.5 lp/mm. A yet to be defined way of quantifying the spatial spectrum content shift by ASiR should be required for QC when ASiR is used for dose reduction. This study provides a preliminary understanding and characterization of objective image quality of CT with IRA. Further studies on the scene dependency of the image quality by IRA and different implementations of IRA by other CT manufactures are forthcoming.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".