Improved MR venography using quantitative susceptibility-weighted imaging
Bibliographic record
Abstract
PURPOSE: To remove the geometry dependence of phase-based susceptibility weighting masks in susceptibility-weighted imaging (SWI) and to improve the visualization of the veins and microbleeds. MATERIALS AND METHODS: True SWI (tSWI) was generated using susceptibility-based masks. Simulations were used to evaluate the influence of the characteristic parameters defining the mask. In vivo data from three healthy adult human volunteers were used to compare the contrast-to-noise-ratios (CNRs) of the right septal vein and the left internal cerebral vein as measured from both tSWI and SWI data. A traumatic brain injury (TBI) patient dataset was used to illustrate qualitatively the proper visualization of microbleeds using tSWI. RESULTS: Compared with conventional SWI, tSWI improved the CNR of the two selected veins by a factor of greater than three for datasets with isotropic resolution and greater than 30% for datasets with anisotropic resolution. Veins with different orientations can be properly enhanced in tSWI. Furthermore, the blooming artifact due to the strong dipolar phase of microbleeds in conventional SWI was reduced in tSWI for the TBI case. CONCLUSION: The use of tSWI overcomes the geometric limitations of using phase and provides better visualization of the venous system, especially for data collected with isotropic resolution.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".