A novel fast full inversion based breast ultrasound elastography technique
Bibliographic record
Abstract
Cancer detection and classification have been the focus of many imaging and therapeutic research studies. Elastography is a non-invasive technique to visualize suspicious soft tissue areas where tissue stiffness is used as image contrast mechanism. In this study, a breast ultrasound elastography system including software and hardware is proposed. Unlike current elastography systems that image the tissue strain and present it as an approximation to relative tissue stiffness, this system is capable of imaging the breast absolute Young's modulus in fast fashion. To improve the quality of elastography images, a novel system consisting of two load cells has been attached to the ultrasound probe. The load cells measure the breast surface forces to be used for calculating the tissue stress distribution throughout the breast. To facilitate fast imaging, this stress calculation is conducted by an accelerated finite element method. Acquired tissue displacements and surface force data are used as input to the proposed Young's modulus reconstruction technique. Numerical and tissue mimicking phantom studies were conducted for validating the proposed system. These studies indicated that fast imaging of breast tissue absolute Young's modulus using the proposed ultrasound elastography system is feasible. The tissue mimicking phantom study indicated that the system is capable of providing reliable absolute Young's modulus values for both normal tissue and tumour as the maximum Young's modulus reconstruction error was less than 6%. This demonstrates that the proposed system has a good potential to be used for clinical breast cancer assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".