Whole-Body MR Imaging in Children: Principles, Technique, Current Applications, and Future Directions
Bibliographic record
Abstract
In whole-body magnetic resonance (MR) imaging, the entire body from the vertex to the toes is imaged in one or more planes with one or multiple sequences to allow evaluation of multisystem diseases in a single examination. Whole-body MR imaging is particularly useful for examining children because it does not involve exposure to radiation and allows a complete work-up for disease staging within a single session of sedation or anesthesia. At whole-body MR imaging with a sliding table platform, a body coil may be used, but the resultant images have a low signal-to-noise ratio (SNR) and low resolution; use of a combination of phased-array coils results in images with an improved SNR and higher resolution. As whole-body MR imaging techniques undergo further refinement, the role of the modality in oncologic and nononcologic imaging continues to expand. Its use in the staging of lymphoma and other malignancies has been studied extensively. Whole-body MR imaging does not provide functional information and cannot yet be used to differentiate benign from malignant lymphadenopathy. However, whole-body MR imaging performed with integrated diffusion-weighted sequences may complement or replace positron emission tomography, which involves substantial radiation exposure. Other promising avenues for future research include whole-body MR imaging at 3 T and the combination of molecular imaging or positron emission tomography with whole-body MR imaging.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".