Pushing the Doppler envelope: From complex flow visualization to biomarker quantification
Bibliographic record
Abstract
Although Doppler ultrasound has been widely used clinically, the current technology is known to lack visual intuitiveness and is often erroneous. New ultrasound flow mapping solutions are needed to more effectively diagnose a variety of medical problems that the aging population is prone to, such as atherosclerosis. In the decade of 2010s, there is a major innovation drive in high-frame-rate (or ultrafast) ultrasound imaging that is characterized by frame rates of over 1000 fps. New techniques such as vector flow imaging have become mature over this period. Building upon this success, we have now entered the era of “High-Frame-Rate Ultrasound 2.0” that makes use of time-resolved mapping to derive quantitative biomarkers that are directly linked to diseases. One example is the development of wall shear mapping techniques for next-generation cardiovascular diagnostics. Here, wall shear stress is measured as the flow velocity gradient tangential to the arterial wall by performing (1) plane-wave data acquisition, (2) dynamic wall tracking, (3) vector flow estimation with near-wall velocity regularization, and (4) spatial velocity gradient estimation. These solutions have been implemented in real-time using open-platform ultrasound scanners and high-speed GPU computing platforms. With these imaging innovations, it becomes possible to track complex cardiovascular flow phenomena and, in turn, derive hemodynamic biomarkers that are important to atherosclerosis monitoring, such as wall shear stress.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".