Determination of the venous output function from MR signal phase: Feasibility for quantitative DCE-MRI in human brain
Bibliographic record
Abstract
For dynamic contrast-enhanced MRI studies in the human brain, it is useful to measure the venous output function (VOF). The purpose of this work was to explore the feasibility of measuring the VOF using the MR signal phase (in absolute units of gadolinium concentration) in the superior sagittal sinus. Phantom experiments were performed to validate the technique for different superior sagittal sinus angles (theta = 0-48 degrees relative to the main magnetic field), different curvatures (straight or radius = 45 mm), and different spatial resolutions (2.2-5.5 mm, to study partial-volume effects). Additionally, the technique was tested on three patients. The phantom experimental results (echo time = 5.5 ms, theta <or= 21 degrees) agreed with theoretical predictions to within 10%. For the patient studies, the measured VOFs had reasonable amplitude and shape characteristics and the patients' superior sagittal sinus angles (<15 degrees) and curvatures (radii approximately 40 mm) were within the range explored with phantoms. Our results suggest that partial-volume contributions to the VOF will be <5% and that the VOF can be evaluated in vivo to within 10% error. In conclusion, it is highly feasible to use MR signal phase to measure the VOF in the superior sagittal sinus for human dynamic contrast-enhanced MRI.
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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 | 0.004 |
| 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 | 0.001 |
| 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".