Non-invasive high-frequency vascular ultrasound elastography
Bibliographic record
Abstract
Non-invasive vascular elastography (NIVE) was recently introduced to characterize mechanical properties of superficial arteries. In this paper, the feasibility of NIVE and its applicability in the context of high-frequency ultrasound imaging is investigated. First, experiments were performed in vitro on vessel-mimicking phantoms. Polyvinyl alcohol cryogel was used to create two double-layer vessels with different mechanical properties. In both cases, the stiffness of the inner layer was made softer. Radial stress was applied within the lumen of the phantoms by applying incremental static pressure steps with a column of a flowing mixture of water-glycerol. The vessel phantoms were insonified at 32 MHz with an ultrasound biomicroscope to provide cross-section sequences of radio-frequency (RF) ultrasound data. The Lagrangian speckle model estimator (LSME) was used to assess the two-dimensional-strain tensors, and the composite Von Mises elastograms were computed. A new implementation of the LSME based on the optical flow equations was introduced. Deformation parameters were estimated using an inversion algorithm. For each in vitro experiment, both layers of approximately 1 mm were distinguished. Second, the use of the method for the purpose of studying small vessels (MicroNIVE) in genetically engineered rodents was introduced. Longitudinal scans of the carotid artery were performed at 40 MHz. The in vivo results give confidence in the feasibility of MicroNIVE as a potential tool to non-invasively study the impact of targeted genes on vascular remodelling in rodents.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".