Three-dimensional quantitative elastography using micro-ultrasound: Proof of concept
Bibliographic record
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed cancers worldwide, yet there are still limitations to overcome in its diagnostic workflow. Some of these limitations may be addressed by quantitative elastography and micro-ultrasound (microUS). This paper presents the first implementation of volumetric shear wave absolute vibro-elastography (S-WAVE) using the ExactVu™ microUS system. This implementation uses minimal additional hardware, making it conducive to insertion into the clinical workflow. In order to accurately recreate phasors with a frame rate below the Nyquist sampling rate for the excitation frequencies, a bandpass sampling strategy is employed for tissue motion tracing. This method is validated using a commercial quality assurance phantom, with inclusions of varying stiffness levels within a homogeneous background. All four inclusions could be visualized with the expected relative stiffness values, and the background stiffness could be measured repeatedly. This demonstrates the effectiveness of microUS-based S-WAVE imaging for the future detection of PCa lesions.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".