Detection and correction of aliasing in ultrasonic measurement of blood flows with Ultrasonic-Measurement-Integrated simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it