Solenoid coil for mouse-model MRI with a clinical 3-Tesla imager: body imaging
Bibliographic record
Abstract
A solenoid coil was built for magnetic resonance imaging of the mice. A coil prototype composed of 5 turns, with a length of 4 cm and 2.5 cm radius was developed to acquire (whole) body mouse magnetic resonance images at 130 MHz and an insertable gradient coil set. Coil performance was measured using the Q factor for both the loaded and unloaded cases were 161.67 and 178.03, respectively. These Q factors compare very well with those values reported in the literature. The images were acquired with a clinical 3 T system equipped with a custom-built gradient insert coil and gradient echo image sequence. Both phantom and in vivo images showed good signal-to-noise ratio and uniformity. The electromagnetic interaction between the insertable coils and the solenoidal coil is poor, and no image artefacts are present in the whole-body image of the mouse. This preliminary experience has shown that consistent high quality MR images of the mice can be obtained using this particular hardware configuration, making it a promising method for acquisition of high-spatial resolution MR images of mice. Volume coils are still a good choice when combined with high field MR imagers and standard gradient echo sequences for the magnetic resonance imaging of the mouse.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".