B<sub>1</sub> mapping for bias‐correction in quantitative <i>T</i><sub>1</sub> imaging of the brain at 3T using standard pulse sequences
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Bibliographic record
Abstract
Purpose B 1 mapping is important for many quantitative imaging protocols, particularly those that include whole‐brain T 1 mapping using the variable flip angle (VFA) technique. However, B 1 mapping sequences are not typically available on many magnetic resonance imaging (MRI) scanners. The aim of this work was to demonstrate that B 1 mapping implemented using standard scanner product pulse sequences can produce B 1 (and VFA T 1 ) maps comparable in quality and acquisition time to advanced techniques. Materials and Methods Six healthy subjects were scanned at 3.0T. An interleaved multislice spin‐echo echo planar imaging double‐angle (EPI‐DA) B 1 mapping protocol, using a standard product pulse sequence, was compared to two alternative methods (actual flip angle imaging, AFI, and Bloch‐Siegert shift, BS). Single‐slice spin‐echo DA B 1 maps were used as a reference for comparison (Ref. DA). VFA flip angles were scaled using each B 1 map prior to fitting T 1 ; the nominal flip angle case was also compared. Results The pooled‐subject voxelwise correlation ( ρ ) for B 1 maps (BS/AFI/EPI‐DA) relative to the reference B 1 scan (Ref. DA) were ρ = 0.92/0.95/0.98. VFA T 1 correlations using these maps were ρ = 0.86/0.88/0.96, much better than without B 1 correction ( ρ = 0.53). The relative error for each B 1 map (BS/AFI/EPI‐DA/Nominal) had 95 th percentiles of 5/4/3/13%. Conclusion Our findings show that B 1 mapping implemented using product pulse sequences can provide excellent quality B 1 (and VFA T 1 ) maps, comparable to other custom techniques. This fast whole‐brain measurement (∼2 min) can serve as an excellent alternative for researchers without access to advanced B 1 pulse sequences. Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1673–1682.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it