High spatial resolution attenuation imaging on a breast tomography scanner based on deep learning
Bibliographic record
Abstract
Attenuation imaging has been used as an independent indictor for breast cancer in ultrasound imaging. Both quantitative ultrasound (QUS) and ultrasound tomography (USCT) can be used to create attenuation images. In QUS, the spectral log difference method is most often applied on the backscattered signal, generating a low spatial resolution image. In USCT, the full wave inversion method is used to reconstruct the imaginary part of the wavenumber, which is attenuation, but suffers from being poorly conditioned, leading to a lower image quality. To overcome these issues, we propose using deep learning (DL) to reconstruct attenuation images of the breast using an ultrasound tomography scanner, i.e., QTI Breast Acoustic CT Scanner. Our U-Net neural network uses both 60-angle RF data as the input and, considering the Kramers–Kronig relations, the sound speed image as an additional input. The attenuation image is the output. The network was trained on simulated breast phantoms, and tested on simulation, physical phantoms, and in vivo breast data. The results demonstrate that our method can generate a high spatial resolution attenuation image with accurate values, and the relationship between generated attenuation and sound speed can serve as a new indicator for breast cancer.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".