Combined magnetic resonance and bioluminescence imaging of live mice
Bibliographic record
Abstract
We perform combined magnetic resonance and bioluminescence imaging of live mice for the purpose of improving the accuracy of bioluminescence tomography. The imaging is performed on three live nude mice in which tritium-powered light sources are surgically implanted. High-resolution magnetic resonance images and multispectral, multiview bioluminescence images are acquired in the same session. An anatomical model is constructed by segmenting the magnetic resonance images for all major tissues. The model is subsequently registered with nonlinear transformations to the 3-D light exittance (exiting intensity) surface map generated from the luminescence images. A Monte Carlo algorithm, along with a set of tissue optical properties obtained from in vivo measurements, is used to solve the forward problem. The measured and simulated light exittance images are found to differ by a factor of up to 2. The greatest cause of this moderate discrepancy is traced to the small errors in source positioning, and to a lesser extent to the optical properties used for the tissues. Discarding the anatomy and using a homogeneous model leads to a marginally worse agreement between the simulated and measured data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".