Poster — Thur Eve — 29: Determination of Contrast Agent Concentration in Tortuous Blood Vessels Using Measured MRI Phase Changes and Fourier‐Based Field Inhomogeneity Equations
Bibliographic record
Abstract
There has been considerable research effort into obtaining quantitative measures of perfusion using dynamic contrast‐enhanced MRI. Absolute quantification of the arterial input function (AIF) and/or venous output function (VOF) in major blood vessels improves perfusion estimates, however reliable techniques for doing so are lacking. Using changes in phase (Δφ) in blood vessels is thought to be the best way to quantify the AIF or VOF. However, it is not yet clear how best to deal with the susceptibility physics when the blood vessels have a complex geometry. We propose a methodology for obtaining absolute quantification of the AIF or VOF using a Fourier‐based calculation of field inhomogeneities, in order to convert Δφ to absolute contrast agent concentration, regardless of the blood vessel geometry. This methodology was tested in an aqueous phantom system. The experimentally measured Δφ was divided by the expected Δφ on a pixel by pixel basis. Ideally, this ratio should be one. Considering all pixels in the phantom tubing, the measured Δφ divided by expected Δφ had a mean value of 1.02 and standard deviation of 0.172. The Fourier‐based calculation can therefore successfully predict Δφ and thus can be used to account for the effect of the vessel geometry and orientation in the conversion of MR phase to contrast agent concentration. The methodology is thus promising for making absolute measurements of the AIF or VOF.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".