Poster — Wed Eve—08: Effectiveness RMI‐156 Mammography Accreditation Phantom in Evaluating Digital Mammography Systems
Bibliographic record
Abstract
PURPOSE: Evaluation of the Effectiveness RMI‐156 Mammography Accreditation Phantom in Digital Mammography METHOD AND MATERIALS: Modulation Transfer Function (MTF) and Signal Difference‐to‐Noise Ratio (SDNR) are considered to be the quantitative image quality matrices that can be used to characterize image quality in digital mammography. However there is no established information regarding the acceptable values for these quantities. This is further complicated by the fact that different detector technologies (pixel size, DQE etc.) are used in digital mammography. Recent experience with two different digital mammography systems (incorporating different digital detectors) from the same manufacturer indicated that the RMI‐156 phantom image quality is better suited for clinical evaluation and comparison of digital mammography systems. RESULTS: SDNR values for a 1 mm disk placed on a 4 cm Lucite phantom for Siemens Novation (70 μm pixel) and Inspiration (85 μm pixel) were calculated to be 3.0 and 3.2 respectively during acceptance testing. However, the evaluation of RMI‐156 phantom image from both systems indicated the image from Inspiration system with higher SDNR had inferior image quality. The Inspiration system was therefore re‐calibrated to increase exposure factors by 33% leading to a new SDNR value of 3.5. This clearly demonstrated that SDNR and MTF measures are not yet established for digital mammography systems for acceptance testing and that RMI‐156 phantom can still be used for digital mammography system evaluations. CONCLUSION: RMI‐156 mammography accreditation phantom is found to be a better image quality indicator in comparing and calibrating digital mammography systems than SDNR and MTF estimates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".