Doppler vortography - Detection and quantification of the vortices in the left ventricle
Bibliographic record
Abstract
It has been speculated that the natural swirling flow that occurs in the normal left ventricle (LV) during LV filling is optimized in terms of fluid energy dissipation. In vivo findings also revealed the formation of additional counter-rotating vortices in the presence of cardiac disease. Such unnatural vortices may significantly impair the LV function due to important kinetic dissipation. We propose a new method for quantification of intracardiac vorticity - Doppler vortography - based on conventional Doppler images only. Doppler vortography relies on the centrosymmetric properties of the vortices. Such properties induce particular symmetries in the Doppler flow data which can be exploited to describe the vortices quantitatively. For this purpose, a kernel filter was developed to derive a parameter, the blood vortex signature (BVS), that allows detecting the main intracardiac vortices and estimating their core vorticities. The reliability of BVS measured by Doppler vortography was assessed in mock Doppler vortical fields and compared with the ground-truth vorticity mapping. Doppler vortography was also tested in healthy subjects and patients. The simulation and in vitro results demonstrated that Doppler vortography is a highly reliable technique for the detection and quantification of the intraventricular vortices. The diastolic BVS measured in the echographic laboratory, at the end of the LV early filling, revealed the presence of retrograde vortices in patients with cardiac disease. Doppler vortography is a promising echocardiographic tool for quantification of vortex flow in the left ventricle. Our findings suggest that Doppler vortography potentially has relevant clinical interest for the assessment of LV diastolic function.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".