Time-Efficient Breath-Hold Abdominal MRI at 3.0 T
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to increase the allowed number of acquired slices per unit time (i.e., time efficiency) for high-power deposition breath-hold abdominal acquisitions at 3.0 T. MATERIALS AND METHODS: Abdominal MRI protocols include various T1-weighted, T2-weighted, and contrast-enhanced acquisitions that require extended spatial coverage and resolution. Ideally, each acquisition is completed within one breath-hold. At 3.0 T, power deposition (i.e., specific absorption rate [SAR]) concerns can limit achieving these conflicting needs because conventional sequences are based on 6-minute time-average SAR requirements. We optimized abdominal-specific sequences based on an approved short-term 10-second time-average SAR criterion and added a delay time after breath-holding to fulfill the long-term 6-minute time-average power deposition regulation. RESULTS: Using our strategy, image acquisition time efficiency at 3.0 T was increased approximately twofold compared with conventional abdominal breath-hold pulse sequences for 2D dual-echo gradient-recalled echo, single-shot fast spin-echo, and 3D steady-state free precession sequences. Volunteers experienced a slight sensation of warmth for the single-shot fast spin-echo implementation, the most SAR-intensive sequence. CONCLUSION: Our optimization strategy is not vendor-specific, is easily implemented for all conventional scanners (provided one can access and modify the pulse sequences directly, or the vendors can make the necessary changes), yields a higher slice-per-unit-time imaging efficiency, and still satisfies all the regulatory power deposition requirements.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".