An MRI‐compatible system for focused ultrasound experiments in small animal models
Bibliographic record
Abstract
The development of novel MRI-guided therapeutic ultrasound methods including potentiated drug delivery and targeted thermal ablation requires extensive testing in small animals such as rats and mice due to the widespread use of these species as models of disease. An MRI-compatible, computer-controlled three-axis positioning system was constructed to deliver focused ultrasound exposures precisely to a target anatomy in small animals for high-throughput preclinical drug delivery studies. Each axis was constructed from custom-made nonmagnetic linear ball stages driven by piezoelectric actuators and optical encoders. A range of motion of 5 x 5 x 2.5 cm3 was achieved, and initial bench top characterization demonstrated the ability to deliver ultrasound to the brain with a spatial accuracy of 0.3 mm. Operation of the positioning system within the bore of a clinical 3 T MR imager was feasible, and simultaneous motion and MR imaging did not result in any mutual interference. The system was evaluated in its ability to deliver precise sonications within the mouse brain, linear scanned exposures in a rat brain for blood barrier disruption, and circular scans for controlled heating under MR temperature feedback. Initial results suggest that this is a robust and precise apparatus for use in the investigation of novel ultrasound-based therapeutic strategies in small animal preclinical models.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".