Detection and correction of aliasing in ultrasonic measurement of blood flows with Ultrasonic-Measurement-Integrated simulation
Bibliographic record
Abstract
Detailed information of real blood flows is essential to develop an accurate diagnosis or treatment for serious circulatory diseases such as aortic aneurysms. Ultrasonic-Measurement-Integrated (UMI) simulation, in which feedback signals from the ultrasonic measurement make the simulation converge to the real blood flow, is a key to solving this problem. However, aliasing in the ultrasonic blood velocity measurement causes UMI simulation to converge to an erroneous result. In this paper, we have investigated the detection and the correction of aliasing in UMI simulation. The artificial force in the feedback of UMI simulation can be used as an index to detect the aliasing. We have proposed two ways for the correction of the aliasing. Correction A, in which measurement velocity is replaced with the computational one at the monitoring point where the aliasing is detected, substantially improves the accuracy of UMI simulation. Correction B, in which measurement velocity is replaced with an estimated Doppler velocity, can provide exactly the same result as that of UMI simulation using the nonaliased standard solution. Although correction B gives the most accurate result, correction A seems more robust and, therefore, a beneficial choice considering the other artifacts in the measurement.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".