Method for B0 off‐resonance mapping by non‐iterative correction of phase‐errors (B0‐NICE)
Bibliographic record
Abstract
PURPOSE: To develop and evaluate a multiecho phase-unwrapping-based B0 mapping method. METHODS: The proposed method estimates a B0 map by Non-Iterative Correction of phase-Errors (B0-NICE). The B0-NICE method generates an initial B0 map from a "pseudo in-phase" data set by introducing a bias frequency shift to the multipeak fat model, followed by correcting the phase errors using both phase and magnitude information. The performance of the B0-NICE method was evaluated with all data cases from the 2012 ISMRM Challenge. RESULTS: The B0 field estimates from B0-NICE were compared with those generated by GlObally Optimal Surface Estimation (GOOSE). In the presence of large B0 inhomogeneity, the B0-NICE method was able to generate more realistic B0 maps from multiecho data, compared with GOOSE. Accurate estimation of fat-fraction (FF) map was also achieved using the proposed algorithm. CONCLUSION: The primary finding of the present study is that accurate FF and B0 maps are achievable if magnitude data is processed independently and used to correct phase errors existing in B0 maps generated by phase unwrapping. The B0-NICE software is freely available to the scientific community.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".