In vivo multiple‐mouse MRI at 7 Tesla
Bibliographic record
Abstract
We developed a live high-field multiple-mouse magnetic resonance imaging method to increase the throughput of imaging studies involving large numbers of mice. Phantom experiments were performed in 7 shielded radiofrequency (RF) coils for concurrent imaging on a 7 Tesla MRI scanner outfitted with multiple transmit and receive channels to confirm uniform signal-to-noise ratio and minimal ghost artifacts across images from the different RF coils. Grid phantoms were used to measure image distortion in different positions in the coils. The brains of 7 live mice were imaged in 3D in the RF coil array, and a second array of 16 RF coils was used to 3D image the whole bodies of 16 fixed, contrast agent-perfused mice. The images of the 7 live mouse brains at 156 microm isotropic resolution and the 16 whole fixed mice at 100 microm isotropic resolution were of high quality and free of artifacts. We have thus shown that multiple-mouse MRI increases throughput for live and fixed mouse experiments by a factor equaling the number of RF coils in the scanner.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".