Development of an ultrasonic nonlinear frequency compounding method with applications in tissue thermometry
Bibliographic record
Abstract
Frequency compounding is an ultrasound imaging technique used to improve signal-to-noise ratio (SNR). In this work, a nonlinear frequency compounding (NLFC) method was introduced, and its application in noninvasive tissue thermometry investigated. The NLFC method was used to produce two-dimensional maps of the temperature sensitive change in backscattered energy of acoustic harmonics (hCBE), during heating of ex vivo porcine tissue with a low intensity focused ultrasound transducer. A hCBE-to-temperature calibration was performed, and temperature maps produced and compared with a theoretical COMSOL based model. Last, a comparative study of the NLFC and previously used nonlinear single frequency (NLSF) methods was completed to quantify the improvement in SNR of the produced hCBE maps. When using the NLFC method, a SNR of 6.06 ± 1.28 was found. SNR values of 3.70 ± 0.53 and 4.38 ± 0.84 were found while using central frequencies of 4.31 and 5.43 MHz, respectively, with the NLSF method. This translates to an improvement of (64.13 ± 4.16)% over the 4.31 MHz NLSF, and (38.72 ± 2.97)% over the 5.43 MHz NLSF methods overall. It was concluded that the NLFC method can produce hCBE and temperature maps with superior image SNR over the NLSF method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".