Susceptibility mapping as a means to visualize veins and quantify oxygen saturation
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To create an orientation-independent, 3D reconstruction of the veins in the brain using susceptibility mapping. MATERIALS AND METHODS: High-resolution, high-pass filtered phase images usually used for susceptibility weighted imaging (SWI) were used as a source for local magnetic field behavior. These images were subsequently postprocessed using an inverse procedure to generate susceptibility maps of the veins. Regularization and interpolation of the data in k-space of the phase images were used to reduce reconstruction artifacts. To understand the effects of artifacts, and to fine-tune the methodology, simulations of blood vessels were performed with and without noise. RESULTS: With sufficient resolution, major veins in the brain could be visualized with this approach. The usual geometry-dependent phase dipole effects are removed by this processing, leaving basically images of the veins. Different sized vessels show a different level of contrast depending on their partial volume effects. Vessels that are 8 mm or 16 mm in size show quantitative values expected for normal oxygen saturation levels. Smaller vessels show smaller values due to errors in the methodology and due to partial volume effects. Larger vessels show a bias toward a reduced susceptibility approaching 90% of the expected value. Limitations of the method and artifacts related to different sources of errors are demonstrated. CONCLUSION: Susceptibility maps can successfully create venograms of the brain with varying levels of contrast-to-noise depending on the size of the vessel. Partial volume effects render this approach more useful as an imaging tool or a visualization tool, although certain larger vessels have measured susceptibilities close to expected values associated with normal blood oxygen saturation levels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it