In-Vivo Validation of 3D Multi-Frequency Liver Shear Wave Absolute Vibro-Elastography with an xMATRIX Array
Bibliographic record
Abstract
Liver fibrosis arises from chronic liver diseases, such as hepatitis B, C, and nonalcoholic steatohepatitis and can result in cirrhosis and death. Magnetic resonance elastography (MRE) is commonly regarded as the imaging-based gold-standard for fibrosis staging. With the aid of a state-of-the-art matrix array transducer, our previous 3D ultrasound shear wave absolute vibro-elastography (S-WAVE) imaging was able to generate hepatic stiffness measurements which are comparable to MRE. In this work, we introduce multi-frequency S-WAVE imaging with a matrix array transducer, to provide more robust and reliable measurements by shorter overall exam time. The system was characterized with three liver tissue phantoms of different elasticity using the MRE results as the ground truth. Six healthy volunteers and six patients who have chronic liver diseases were imaged. Our results indicate that measurements from multi-frequency 3D S-WAVE with a matrix array transducer correlated better to MRE, compared to the readings from the transient elastography method (FibroScan, Echosens).
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".