Angular spatial compounding of diffraction corrected images improves ultrasound attenuation measurements
Bibliographic record
Abstract
Breast cancer is a leading cause of death for women. Quantitative ultrasound (QUS) and ultrasound computed tomography (USCT) are quantitative imaging techniques that have been investigated for management of breast cancer. QUS and USCT can generate ultrasound attenuation images. In QUS, the spectral log difference (SLD) is a technique that can provide estimates of the attenuation coefficient slope. Full angular spatial compounding (FASC) can be used with SLD to generate attenuation maps with better spatial resolution and lower estimate variance. In USCT, high quality speed of sound (SOS) images can be generated using full wave inversion (FWI) method, but attenuation images created using FWI are often of inferior quality. With the QTI Breast Acoustic CTTM Scanner (QT Imaging, Inc., Novato, CA), raw in-phase and quadrature data were used to implement SLD combined with FASC. The capabilities of SLD were compared with FWI through simulations, phantom experiments, and in vivo breast experiments. Results show the SLD resulted in improved accuracy in estimating lesion sizes compared to FWI. Further, SLD images had lower variance and mean absolute error (MAE) compared to FWI of the same samples with respect to the attenuation values (reducing MAE by three times) in the tissue mimicking phantoms.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".