Method of <scp>B</scp>0 mapping with magnitude‐based correction for bipolar two‐point <scp>D</scp>ixon cardiac <scp>MRI</scp>
Bibliographic record
Abstract
PURPOSE: The conventional two-point (2pt) Dixon technique explicitly estimates B0 map by performing phase unwrapping. When signal loss, phase singularity, artifacts, or spatially isolated regions corrupt the measured phase images, this unwrapping-based technique will face difficulty. This work aims to improve the reliability of B0 mapping by performing unwrapping error correction. METHOD: To detect the unwrapping-caused phase errors, we determined a magnitude-based fat/water mask and used it as reference to identify pixels being mismatched by the phase-based mask, which was derived from the B0-corrected phase term of the Hermitian product between echoes. Then, we corrected the afore-determined phase error on a region-by-region basis. We tested the developed method with nine patients' data, and the results were compared with a well-established region-growing technique. RESULTS: By adding the step to correct unwrapping-caused error, we improved the robustness of B0 mapping, resulting in better fat-water separation when compared with the conventional 2pt and the phasor-based region-growing techniques. CONCLUSION: We showed the feasibility of B0 mapping with bipolar 2pt human cardiac data. The software is freely available to the scientific community. Magn Reson Med 78:1862-1869, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".