Time-resolved Doppler vortography in the left ventricle
Bibliographic record
Abstract
Previous studies have suggested that vortex flow in the left ventricle can be used as an indicator of the heart function. Thereby from a clinical point of view, studying the left ventricle vortex during cardiac cycles could be helpful for early diagnosis. What we propose here is the combination of a method called vortography with ultrafast Doppler echocardiography. Ultrafast ultrasound imaging is a promising field that can increase significantly the frame rate of the acquisitions. The use of ultrafast imaging can therefore provide a sufficient number of frames to describe the whole intra-cardiac blood flow during an entire cardiac cycle which cannot be achieved with conventional echography. Doppler vortography is based on recognition of the antisymmetric pattern in a vortex velocity field. The reliability of vortography has already been presented with conventional Doppler imaging. In this study we present the results from in vitro experiments on a spinning disc from which we were able to fix the angular velocity and deduce the ground truth vorticity. We observed a strong correlation between the vorticity measured with ultrafast vortography and the ground truth vorticity (r2= 0.99). We then applied the method in vivo (5 healthy volunteers) and we were able to track the main vortex in the left ventricle during the cardiac cycle.
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 | 0.001 |
| 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 | 0.000 | 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".