Report of <scp>AAPM</scp> Task Group 162: Software for planar image quality metrology
Bibliographic record
Abstract
PURPOSE: The AAPM Task Group 162 aimed to provide a standardized approach for the assessment of image quality in planar imaging systems. This report offers a description of the approach as well as the details of the resultant software bundle to measure detective quantum efficiency (DQE) as well as its basis components and derivatives. METHODS: The methodology and the associated software include the characterization of the noise power spectrum (NPS) from planar images acquired under specific acquisition conditions, modulation transfer function (MTF) using an edge test object, the DQE, and effective DQE (eDQE). First, a methodological framework is provided to highlight the theoretical basis of the work. Then, a step-by-step guide is included to assist in proper execution of each component of the code. Lastly, an evaluation of the method is included to validate its accuracy against model-based and experimental data. RESULTS: The code was built using a Macintosh OSX operating system. The software package contains all the source codes to permit an experienced user to build the suite on a Linux or other *nix type system. The package further includes manuals and sample images and scripts to demonstrate use of the software for new users. The results of the code are in close alignment with theoretical expectations and published results of experimental data. CONCLUSIONS: The methodology and the software package offered in AAPM TG162 can be used as baseline for characterization of inherent image quality attributes of planar imaging systems.
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How this classification was reachedexpand
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".