SHARP edges: Recovering cortical phase contrast through harmonic extension
Bibliographic record
Abstract
PURPOSE: To recover local phase contrast at the edges of the brain (e.g., cortex), where it is otherwise unavailable with the conventional form of the technique sophisticated harmonic artifact reduction for phase data (SHARP). METHODS: A harmonic potential field, such as the magnetic "background" field, is an analytic field and can thus be represented by a convergent power series. Using SHARP to obtain an initial estimate of the harmonic background field over a reduced inner portion of the brain, a three-dimensional Taylor expansion was performed to extend field coverage to the brain edges. The method, called Extended-SHARP, was quantitatively assessed through a numerical field-forward simulation and qualitatively demonstrated in vivo. RESULTS: Using a typical spherical kernel (6 mm radius), Extended-SHARP recovered on average 26% more in vivo brain volume than SHARP. When applied to the numerical model, local field contrast around an otherwise lost edge source was recovered, with the resulting error comparable to that of conventional SHARP. CONCLUSION: The lost field values near the edges of the brain can be recovered through an easily implemented adaptation to conventional SHARP.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".