Blood Flow Imaging with Ultrafast Doppler
Bibliographic record
Abstract
The pulsed-Doppler effect is the main technique used in clinical echography to assess blood flow. Applied with conventional focused ultrasound Doppler modes, it has several limits. Firstly, a finely tuned signal filtering operation is needed to distinguish blood flows from surrounding moving tissues. Secondly, the operator must choose between localizing the blood flows or quantifying them. In the last two decades, ultrasound imaging has undergone a paradigm shift with the emergence of ultrafast ultrasound using unfocused waves. In addition to a hundredfold increase in framerate (up to 10000 Hz), this new technique also breaks the conventional quantification/localization trade-off, offering a complete blood flow mapping of the field of view and a simultaneous access to fine velocities measurements at the single-pixel level (down to 50 µm). This data continuity in both spatial and temporal dimensions strongly improves the tissue/blood filtering process, which results in an increase sensitivity to small blood flow velocities (down to 1 mm/s). In this method paper, we aim to introduce the concept of ultrafast Doppler as well as its main parameters. Firstly, we summarize the physical principles of unfocused wave imaging. Then, we present the Doppler signal processing main steps. Particularly, we explain the practical implementation of the critical tissue/blood flow separation algorithms and on the extraction of velocities from these filtered data. This theoretical description is supplemented by in vitro experiences. A tissue phantom embedding a canal with flowing blood-mimicking fluid is imaged with a research programmable ultrasound system. A blood flow image is obtained and the flow characteristics are displayed for several pixels in the canal. Finally, a review of in vivo applications is proposed, showing examples in several organs such as carotids, kidney, thyroid, brain and heart.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".