High Frame Rate Vector Flow Imaging: Development as a New Diagnostic Mode on a Clinical Scanner
Bibliographic record
Abstract
In this paper, development of high frame rate vector flow imaging (HFR-VFI) on a clinical scanner is presented. Four tough problems encountered during the development, in terms of (i) parallel calculation and streamline working process, (ii) data handling, (iii) image quality assurance for real clinical use, and (iv) high frame rate dynamic display, have been demonstrated with corresponding solutions. The quantitative measurements based on the HFR-VFI, including velocity related parameters, flow turbulence quantification, volume flow and wall shear stress have been introduced and many examples of them are shown as well. Velocity and volume flow are measured by the implemented HFR-VFI and conventional PW with angle corrections using a moving string phantom and a Doppler flow phantom, respectively. The implemented HFR-VFI on the clinical scanner, named as V Flow, gives better agreement in accuracy studies of velocity and volume flow than the conventional PW.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".