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
Bibliographic record
Abstract
Purpose B1 mapping is important for many quantitative imaging protocols, particularly those that include whole‐brain T1 mapping using the variable flip angle (VFA) technique. However, B1 mapping sequences are not typically available on many magnetic resonance imaging (MRI) scanners. The aim of this work was to demonstrate that B1 mapping implemented using standard scanner product pulse sequences can produce B1 (and VFA T1) 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) B1 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 B1 maps were used as a reference for comparison (Ref. DA). VFA flip angles were scaled using each B1 map prior to fitting T1; the nominal flip angle case was also compared. Results The pooled‐subject voxelwise correlation (ρ) for B1 maps (BS/AFI/EPI‐DA) relative to the reference B1 scan (Ref. DA) were ρ = 0.92/0.95/0.98. VFA T1 correlations using these maps were ρ = 0.86/0.88/0.96, much better than without B1 correction (ρ = 0.53). The relative error for each B1 map (BS/AFI/EPI‐DA/Nominal) had 95th percentiles of 5/4/3/13%. Conclusion Our findings show that B1 mapping implemented using product pulse sequences can provide excellent quality B1 (and VFA T1) 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 B1 pulse sequences. Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1673–1682.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".