Wiener filter for filtered back projection in digital breast tomosynthesis
Bibliographic record
Abstract
Conventional filtered back projection (FBP) reconstruction for digital breast tomosynthesis (DBT) can suffer from a low signal to noise ratio. Because of the strong amplification by the reconstruction filters (ramp, apodization and slice thickness), noise at high spatial frequencies can be greatly increased. Image enhancement by Wiener filtering is investigated as a possible method to improve image quality. A neighborhood wavelet coefficient window technique is used to estimate the noise content of projection images and a Wiener filter is applied to the projection images. The neighborhood wavelet coefficient window is a non-linear technique, which may cause the Wiener filters estimated before and after the application of the reconstruction filters to be different. Image quality of a FBP reconstruction with and without Wiener filtering is investigated using a Fourier-based observer detectability metric ( d' ) for evaluation. Simulations of tomosynthesis are performed in both homogeneous and anatomic textured backgrounds containing lowcontrast masses or small microcalcifications. Initial results suggest that improvements in detectability can be achieved when the Wiener filter is applied, especially when the Wiener filter is estimated for the reconstruction filtered projections.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".