Building a Birdcage Resonator for Magnetic Resonance Imaging Studies of CNS Disorders
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) studies are currently being performed at the Health Sciences Center (HSC) in Winnipeg in order to understand better central ner-vous system (CNS) disorders. Using a 7-Tesla MRI system and rodent models of Alzheimer’s disease and Multiple Sclerosis, novel MRI techniques are being exploited to enhance the diagnosis of these disorders in all stages of their development. The studies require a variety of small-scale radio frequency (RF) resonators to achieve high quality images of rodents of different sizes. There are currently several sizes of RF resonators available for use at the HSC, but of those, there is not a suitable resonator for rodents between 25 mm and 30 mm in diameter. The purpose of this project is to construct and test a 28.5 mm inner diameter birdcage resonator intended for mice and rats of this size. The newly built resonator is compared to the next availably sized resonator to show the benefit of using RF resonators which are sample-size apropriate. Scans performed on an excised rat brain show that the 28.5 mm resonator provides a higher signal-to-noise ratio (SNR) than the previously used 33 mm resonator. We achieved an SNR of 30.3 with the 28.5 mm resonator compared with an SNR of 21.7 for the 33 mm resonator, clearly indicating that the newly built resonator is better suited for samples of 28.5 mm or less. Higher resolution images can now be acquired from rodents in this size range, leading to finer image details and a better understanding of the CNS disorders being studied.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".