Design and construction of an optimized transmit/receive hybrid birdcage resonator to improve full body images of medium-sized animals in 7T scanner
Bibliographic record
Abstract
The purpose of this work was to develop an optimized transmit/receive birdcage coil to extend the possibilities of a 7T preclinical MRI system to conduct improved full body imaging in medium-sized animals, such as large New Zealand rabbits. The coil was designed by combining calculation and electromagnetic simulation tools. The construction was based on precise mechanical design and careful building practice. A 16-leg, 20 cm long, 16 cm inner diameter, shielded quadrature hybrid structure was selected. Coil parameters were assessed on the bench and images were acquired on phantoms and rabbits. The results were compared to simulations and data obtained with an available commercial coil. An inexpensive assembly with an increase of 2 cm in useful inner diameter and 50 Ω matching with larger animals was achieved. A reduction in radiofrequency (RF) power demand of 31.8%, an improvement in image uniformity of 18.5 percentage points and an increase in signal-to-noise ratio of up to 42.2% were revealed by phantom image acquisitions, which was confirmed by in vivo studies. In conclusion, the proposed coil extended the possibilities of a preclinical 7T system as it improved image studies in relatively large animals by reducing the RF power demand, and increasing image uniformity and signal-to-noise ratio. Shorter scans and time under anesthesia or reduced RF exposure, resulting in better images and lower animal health risk during in vivo experiments, were achieved.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".