Deformability of ascending thoracic aorta aneurysms assessed using ultrafast ultrasound and a principal strain estimator: In vitro evaluation and in vivo feasibility
Bibliographic record
Abstract
BACKGROUND: Noninvasive vascular strain imaging under conventional line-by-line scanning has a low frame rate and lateral resolution and depends on the coordinate system. It is thus affected by high deformations due to image decorrelation between frames. PURPOSE: To develop an ultrafast time-ensemble regularized tissue-Doppler optical-flow principal strain estimator for aorta deformability assessment in a long-axis view. METHODS: This approach alleviated the impact of lateral resolution using image compounding and that of the coordinate system dependency using principal strain. Accuracy and feasibility were evaluated in two aorta-mimicking phantoms first, and then in four age-matched individuals with either a normal aorta or a pathological ascending thoracic aorta aneurysm (TAA). RESULTS: Instantaneous aortic maximum and minimum principal strain maps and regional accumulated strains during each cardiac cycle were estimated at systolic and diastolic phases to characterize the normal aorta and TAA. In vitro, principal strain results matched sonomicrometry measurements. In vivo, a significant decrease in maximum and minimum principal strains was observed in TAA cases, whose range was respectively 7.9 ± 6.4% and 8.2 ± 2.6% smaller than in normal aortas. CONCLUSIONS: The proposed principal strain estimator showed an ability to potentially assess TAA deformability, which may provide an individualized and reliable evaluation method for TAA rupture risk assessment.
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 | 0.002 |
| 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 | 0.000 |
| 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 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".