Susceptibility phase imaging with improved image contrast using moving window phase gradient fitting and minimal filtering
Bibliographic record
Abstract
PURPOSE: To enhance image contrast in susceptibility phase imaging using a new method of background phase removal. MATERIALS AND METHODS: A background phase removal method is proposed that uses the spatial gradient of the raw phase image to perform a moving window third-order local polynomial estimation and correction of the raw phase image followed by minimal high pass filtering. The method is demonstrated in simulation, 10 healthy volunteers, and 5 multiple sclerosis patients in comparison to a standard phase filtering approach. RESULTS: Compared to standard phase filtering, the new method increased phase contrast with local background tissue in subcortical gray matter, cortical gray matter, and multiple sclerosis lesions by 67% ± 33%, 13% ± 7%, and 48% ± 19%, respectively (95% confidence interval). In addition, the new method removed more phase wraps in areas of rapidly changing background phase. CONCLUSION: Local phase gradient fitting combined with minimal high pass filtering provides better tissue depiction and more accurate phase quantification than standard filtering.
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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) | 0.001 | 0.001 |
| 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) | 0.001 | 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".