Brain iron detected by SWI high pass filtered phase calibrated with synchrotron X‐ray fluorescence
Bibliographic record
Abstract
PURPOSE: To test the ability of susceptibility weighted images (SWI) and high pass filtered phase images to localize and quantify brain iron. MATERIALS AND METHODS: Magnetic resonance (MR) images of human cadaver brain hemispheres were collected using a gradient echo based SWI sequence at 1.5T. For X-ray fluorescence (XRF) mapping, each brain was cut to obtain slices that reasonably matched the MR images and iron was mapped at the iron K-edge at 50 or 100 microm resolution. Iron was quantified using XRF calibration foils. Phase and iron XRF were averaged within anatomic regions of one slice, chosen for its range of iron concentrations and nearly perfect anatomic correspondence. X-ray absorption spectroscopy (XAS) was used to determine if the chemical form of iron was different in regions with poorer correspondence between iron and phase. RESULTS: Iron XRF maps, SWI, and high pass filtered phase data in nine brain slices from five subjects were visually very similar, particularly in high iron regions. The chemical form of iron could not explain poor matches. The correlation between the concentration of iron and phase in the cadaver brain was estimated as c(Fe) [microg/g tissue] = 850Deltavarpi + 110. CONCLUSION: The phase shift Deltavarpi was found to vary linearly with iron concentration with the best correspondence found in regions with high iron content.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".