A Comparison of the Imaging Performance of High Resolution Ultrasound Scanners for Preclinical Imaging
Bibliographic record
Abstract
Nine ultrasound transducers from six ultrasound scanners were assessed for their utility for preclinical ultrasound imaging. The transducers were: L8-16, L10-22 (Diasus; Dynamic Imaging Ltd., Livingston, UK); L17-5, L15-7io (iU22; Philips, Seattle, WA, USA), HFL38/13-6 (MicroMaxx; Sonosite Inc., Bothell, WA, USA); il3Lv (Vivid 5; GE, Fairfield, CT, USA), RMV 704 (Vevo 770; Visualsonics Inc., Toronto, Canada) and MS550S, MS550D (Vevo 2100; Visualsonics Inc.). A quantitative analysis of the ultrasound images from all nine transducers employed measurements of the resolution integral as an indication of the versatility and technology of the ultrasound scanners. Two other parameters derived from the resolution integral, the characteristic resolution and depth of field, were used to characterise imaging performance. Six of these transducers were also assessed qualitatively by ultrasonically scanning 59 female common marmosets (Callithrix jacchus) yielding a total of 215 scans. The quantitative measurements for each of the transducers were consistent with the results obtained in the qualitative in vivo assessment. Over a 0-10 mm imaging depth, the values of the resolution integral, characteristic resolution and depth of field, measured using the Edinburgh Pipe Phantom, ranged in magnitude from 7-72, 93-930 μm and 3.3-9.2 mm respectively. The largest resolution integrals were obtained using the Vevo 770 and Vevo 2100 scanners. The Edinburgh Pipe Phantom provides a quantitative method of characterising the imaging performance of preclinical imaging scanners.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".