Robust arterial input function surrogate measurement from the superior sagittal sinus complex signal for fast dynamic contrast‐enhanced MRI in the brain
Bibliographic record
Abstract
PURPOSE: Accurately estimating the arterial input function for dynamic contrast-enhanced MRI is challenging. An arterial input function is typically determined from signal magnitude changes related to a contrast agent, often leading to underestimation of peak concentrations. Alternatively, signal phase recovers the accurate peak concentration for straight vessels but suffers from high noise. A recent method proposed to fit the signal in the complex plane by combining the advantages of the previous 2 methods. The purpose of this work is to refine this complex-based method to determine the venous output function (VOF), an arterial input function surrogate, from the superior sagittal sinus. METHODS: . We compared the magnitude-, phase-, and complex-based VOF determination methods against various simulated biases as well as for 29 brain metastases patients. RESULTS: varied widely within patients, and its effect on the signal phase caused an underestimation of peak concentrations of up to 65%. Correction significantly increased the VOF peak concentration for the phase- and complex-based VOFs in the cohort. The phase-based method recovered accurate peak concentrations but lacked precision in the tail of the VOF. Our complex-based VOF completely recovered the effect of inflow and resulted in a high-peak concentration with limited noise. CONCLUSION: The new complex-based method resulted in high-quality VOF robust against superior sagittal sinus curvature and variations in patient positioning.
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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 | 0.002 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".