TH-E-L100J-01: Ultrasound Reflectivity Imaging with a Split-Step Fourier Propagator for Cancer Detection and Diagnosis in Heterogeneous Breasts
Bibliographic record
Abstract
Purpose: To improve resolution and reduce speckle in ultrasound breast images by accounting for ultrasound scattering from breast heterogeneities during reflectivity image reconstruction. Method and Materials: X-ray mammography often fails to detect cancers in dense breasts, while breast ultrasound has the potential to detect them. Breast heterogeneities, particularly in dense breasts, generate significant ultrasound scattering. Properly handling ultrasound scattering is critical for reliable cancer detection and diagnosis in dense breasts. Ultrasound wave propagation in the breast is governed by the acoustic-wave equation in heterogeneous media, which can be decomposed into two one-way wave equations describing wave propagation in opposite directions. A split-step Fourier solution of a one-way wave equation is used for backpropagation of reflected ultrasound waves. The backpropagation consists of two steps: one phase-shift step in the frequency-wavenumber domain, and another phaseshift step in the frequency-space domain. During the backpropagation of ultrasound wavefields, heterogeneous breast sound-speed models obtained from transmission ultrasound tomography are used to approximately account for ultrasound wave scattering. The reflectivity imaging method based on the split-step Fourier propagator is applied to computer-generated ultrasound data and in-vivo ultrasound breast data acquired using a ring transducer array. The ultrasound images are compared with those obtained using a uniform sound-speed model. Results: Comparison of ultrasound reflectivity images obtained using heterogeneous breast sound-speed models with those obtained with a uniform model shows that ultrasound scattering of breast heterogeneities needs to be taken into account to obtain high-resolution and high-quality breast images. Conclusion: Using heterogeneous sound-speed models for ultrasound wave backpropagation during reflectivity image reconstruction significantly improves image resolution and reduces speckle. The resolution and quality of ultrasound reflectivity images are further enhanced with increasing accuracy and resolution of transmission ultrasound tomography.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".