Pulse inversion sequences for mechanically scanned transducers
Bibliographic record
Abstract
Mechanically scanned transducers are currently used for tissue harmonic imaging (THI) and nonlinear microbubble imaging at high frequencies. The pulse inversion (PI) technique is widely used for suppressing the fundamental signal, but its effectiveness is reduced by relative tissue/transducer motion. In this paper, we investigate multipulse inversion (MPI) sequences that achieve a significant improvement on the fundamental suppression for mechanically scanned single-element transducers. MPI was subsequently applied on simulated and measured RF-data and relative fundamental suppression was compared with the 2-pulse PI technique. Simulations showed, for example, an increased fundamental suppression of 6 and 10 dB for MPI-sequences that combined 3 and 7 pulses, respectively, for a rotating intravascular ultrasound transducer with an interpulse angle of 0.15 degrees. Initial application of MPI sequences on RF-data from in vivo acquisitions resulted in similar fundamental suppression levels. The investigated MPI technique will help to reduce relative tissue/transducer motion effects and might lead to improved sensitivity and spatial resolution in nonlinear tissue imaging and improved microbubble detection in contrast imaging for mechanically scanned transducers.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".