An MRI-compatible three-axis focused ultrasound system for performing drug delivery studies in small animal models
Bibliographic record
Abstract
MRI-guided focused-ultrasound is a non-invasive technique that can enhance the delivery of therapeutic agents. The objective of this work was to develop a focused-ultrasound system for preclinical research in small animals that is capable of sonicating with high spatial precision within a closed-bore MRI. The system features a computer-controlled, non-magnetic, three-axis positioning system that uses piezoelectric actuators and linear optical encoders to position a focused-ultrasound transducer to targeted tissues under MRI guidance. The actuator and encoder signals are transmitted through low-pass-filtered connectors on a grounded RF-penetration panel to prevent artifacts during image acquisition. The transducer is attached to the positioning system by a rigid arm and is submerged within a closed water tank. The arm passes into the tank through flexible bellows to ensure that the system remains sealed. An RF coil acquires high-resolution images in the vicinity of the target tissue. An aperture on the water tank, centered about the RF coil, provides an access point for target sonication. Registration between ultrasound and MRI coordinates involves sonicating a temperature-sensitive phantom and measuring the centroid of the thermal focal zone in 3D with MR thermometry. Linear distances of 5 cm with a positioning resolution of 0.05 mm can be achieved for each axis. The system was operated successfully on MRI scanners from different vendors at both 1.5 and 3.0 T, and simultaneous motion and imaging was possible without any mutual interference or imaging artifacts. This system is used for high-throughput small-animal experiments to study the efficacy of ultrasound-enhanced drug delivery.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".