In‐plane visibility of lesions using breast tomosynthesis and digital mammography
Bibliographic record
Abstract
PURPOSE: The purpose of this work was to evaluate the visibility of simulated lesions in 2D digita mammography (DM) and breast tomosynthesis (BT) images of patients. METHODS: Images of the same women were acquired on both a DM system (Mammomat Novation, Siemens Healthcare, Erlangen, Germany) and a BT prototype system adapted from the same type of DM system. Using the geometrical properties of the two systems, simulated lesions were projected and added to each DM image as well as to each BT projection image prior to 3D reconstruction. The same beam quality and approximately the same total absorbed dose to the glandular tissue were used for each breast image acquisition on the two systems. A series of four-alternative forced choice human observer experiments was conducted for each of five simulated lesion diameters: 0.2, 1, 3, 8, and 25 mm. An additional experiment was conducted for the 0.2 mm lesion in BT only at twice the dose level (BT2x). Threshold signal was defined as the lesion signal intensity required for a detectability index (d') of 2.5. Four medical physicists participated in all experiments. One experiment, consisting of 60 cases, was conducted per test condition (i.e., lesion size and signal combination). RESULTS: For the smallest lesions (0.2 mm), the threshold signal for DM was 21% lower than for BT at equivalent dose levels, and BT2x was 26% lower than DM. For the lesions larger than 1 mm, the threshold signal increased linearly (in log space) with the lesion diameter for both DM and BT, with DM requiring around twice the signal as BT. The difference in the threshold signal between BT and DM at each lesion size was statistically significant, except for the 0.2 mm lesion between BT2x and DM. CONCLUSIONS: The results of this study indicate that low-signal lesions larger than 1.0 mm may be more visible in BT compared to DM, whereas 0.2 mm lesions may be better visualized with DM compared to BT, when compared at equal dose.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".