SU‐E‐U‐02: The Development of a Practical Ultrasonic System for Cross‐Sectional Imaging of Small Animals
Bibliographic record
Abstract
Purpose: To test the feasibility of developing a practical medium frequency ultrasound tomography method for small animal imaging. The ability to produce cross‐sectional or full body images of a live small animal using a low‐cost tabletop ultrasound scanner without any special license would be very beneficial to long term biological studies, where repeated scanning is often required over an extended period of time. Methods: The cross sectional images were produced by compounding multiple B‐scans of a laboratory phantom or an animal acquired at different projection angles. Two imaging systems were used to test the concept. The first system included a programmable 64‐channel phased array controller driving a 128‐channel, 5–10 MHz linear probe to produce 143 B‐Mode projections of the spinning object. The second system designed and manufactured in house, produced 64 or 128 B‐Mode projections with a single unfocused 8 MHz transducer scanning with a 0.116 mm step size. Results: The phased array system provided good penetration through the phantoms/mice (with the exception of the lungs) and allowed to acquire data in a very short time. The cross‐sectional images have enough resolution and dynamic range to detect both high‐ and low‐contrast organs. The single transducer system takes longer to scan, and the data require more sophisticated processing. To date, our images allow seeing details as small as 1–2 mm in the phantoms and in small animals, with the contrast mostly due to highly reflecting bones and air inclusions. Conclusion: The work indicates that very detailed and anatomically correct images can be created by relatively simple and inexpensive means. With more advanced algorithms and improved system design, scan time can be reduced considerably, enabling high‐resolution full 3D imaging. This will allow for quick and easy scans that can help monitor tumor growth and/or regression without contributing any dose to the animal. The authors would like to acknowledge the financial and engineering support from Tessonics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".