Breast ultrasound elastography using inverse finite element elasticity reconstruction
Bibliographic record
Abstract
Breast cancer is the most common cancer in women worldwide. Its early detection is paramount for its successful treatment outcome. Among imaging techniques developed for breast cancer diagnosis, elastography has shown good promise. In this presentation, a breast ultrasound elastography method will be described, and its application in breast cancer patients will be demonstrated. The method follows the quasi-static elastography approach where the breast is stimulated using regular ultrasound transducer. RF data are utilized within a dynamic programming minimization algorithm for tissue motion tracking, leading to 2D (axial + lateral) strain field. This field is processed within a novel inverse finite-element reconstruction framework to reconstruct the breast Young's modulus distribution. The framework uses Hooke's law to obtain the Young's modulus distribution. It is iterative where the stress distribution is updated using finite element method at the end of each reconstruction iteration. To ensure convergence, the Young's modulus was averaged within 5x5 finite element windows. A phantom study mimicking breast cancer was performed to validate the developed system which demonstrated high (93%) accuracy of Young's modulus reconstruction. The method was then applied to breast cancer patients where elastography images reconstructed using the proposed method showed its effectiveness in clinical setting. The only hardware required in the system is an ultrasound scanner. As such, it is a promising candidate for clinical cancer diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".