The Methods and Effects of Transducer Degradation on Image Quality and the Clinical Efficacy of Diagnostic Sonography
Bibliographic record
Abstract
In diagnostic ultrasound examinations, transducer “health” is key to diagnostic efficacy. It is known that individual transducer element integrity within an array is central to overall probe performance and over time, with normal use, elements can cease working or lose sensitivity, leading to a potentially negative impact on the clinical efficacy of the ultrasound examination. Investigating this issue, the authors evaluated transducers with selected elements disabled compared to fully functioning arrays, examined how dead elements affected ultrasound beams, acoustic parameters, flow phantom/tissue phantom results as well as human imaging. Results: As few as 2 consecutive dead elements can materially impact the beam profile; four or more can significantly reduce resolution and penetration, increase the noise floor, and cause Doppler peak velocity errors, flow ambiguity and spectral broadening. Tissue phantoms proved to be equivocal in spotting defective elements. Conclusion: array heath is critical to high-quality, efficacious ultrasound studies and the potential for misdiagnosis increases as array elements degrade.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".