Abdominal MR Imaging in Children: Motion Compensation, Sequence Optimization, and Protocol Organization
Bibliographic record
Abstract
Familiarity with basic sequence properties and their trade-offs is necessary for radiologists performing abdominal magnetic resonance (MR) imaging. Acquiring diagnostic-quality MR images in the pediatric abdomen is challenging due to motion, inability to breath hold, varying patient size, and artifacts. Motion-compensation techniques (eg, respiratory gating, signal averaging, suppression of signal from moving tissue, swapping phase- and frequency-encoding directions, use of faster sequences with breath holding, parallel imaging, and radial k-space filling) can improve image quality. Each of these techniques is more suitable for use with certain sequences and acquisition planes and in specific situations and age groups. Different T1- and T2-weighted sequences work better in different age groups and with differing acquisition planes and have specific advantages and disadvantages. Dynamic imaging should be performed differently in younger children than in older children. In younger children, the sequence and the timing of dynamic phases need to be adjusted. Different sequences work better in smaller children and in older children because of differing breath-holding ability, breathing patterns, field of view, and use of sedation. Hence, specific protocols should be maintained for younger children and older children. Combining longer-higher-resolution sequences and faster-lower-resolution sequences helps acquire diagnostic-quality images in a reasonable time.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".