Processing of Subharmonic Signals from Ultrasound Contrast Agents to Determine Ambient Pressures
Bibliographic record
Abstract
Subharmonic-aided pressure estimation (SHAPE) is a technique that utilizes the subharmonic emissions, occurring at half the insonation frequency, from ultrasound contrast agents to estimate ambient pressures. The purpose of this work was to compare the performance of different processing techniques for the raw radiofrequency (rf) data acquired for SHAPE. A closed loop flow system was implemented circulating reconstituted Sonazoid (GE Healthcare, Oslo, Norway; 0.2 ml for 750 ml diluent) and the beam-formed unprocessed rf data were obtained from a 4 mm diameter lumen of a Doppler flow phantom (ATS Laboratories, Inc., Bridgeport, CT) using a SonixRP scanner (Ultrasonix, Richmond, BC, Canada). The transmit frequency and incident acoustic pressures were set to 2.5 MHz and 0.22 MPa, respectively, in order to elicit Sonazoid subharmonic emissions that are ambient-pressure sensitive. The time-varying ambient pressures within the flow phantom were recorded by a Millar pressure catheter. Four techniques for extracting the subharmonic amplitude from the rf data were tested along with two noise filtering techniques to process this data. Five filter orders were tested for the noise removing filters. The performance was evaluated based on the least root-mean-square errors reported after linear least-square regression analyses of the subharmonic data and the pressure catheter data and compared using a repeated ANOVA. When the subharmonic amplitudes were extracted as the mean value within a 0.2 MHz bandwidth about 1.25 MHz and when the resulting temporally-varying subharmonic signal was median filtered with an order of 500, the filtered subharmonic signal significantly predicted the ambient pressures (r2 = 0.90; p < 0.001) with the least error. The resulting root mean square and mean absolute errors were 8.16 +/- 0.26 mmHg and 6.70 +/- 0.17 mmHg, respectively. Thus, median processing the subharmonic data extracted as the mean value within a 0.2 MHz bandwidth about the theoretical subharmonic frequency turned out to be the best technique to process acoustic data for SHAPE. The implementation of this technique on ultrasound scanners may permit real-time SHAPE applications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".