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.
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.
How this classification was reachedexpand
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".