Modeling the residue function in DSC‐MRI simulations: Analytical approximation to in vivo data
Bibliographic record
Abstract
PURPOSE: An exponential residue function is commonly used in numerical simulations to assess the accuracy of perfusion quantification using dynamic susceptibility contrast (DSC) MRI. Although this might be a reasonable assumption for normal tissue, microvascular hemodynamics are likely to be significantly altered in pathology. Thus the exponential function may no longer be appropriate and the estimated accuracy of DSC-MRI quantification might be inappropriate. The purpose of this study was to characterize in vivo residue function variations in normal and infarcted tissue in a chronic atherosclerotic disease cohort, and to find the most appropriate model for use in DSC simulations. METHODS: Residue functions were measured in vivo in patients with atherosclerotic disease using a nonparametric Control Point Interpolation method, which has been shown to provide a robust characterization of the shape of the residue function. The observed residue functions were approximated with five commonly used analytical expressions: exponential, bi-exponential, Lorentzian, and Fermi functions, and a previously proposed Vascular Model. RESULTS: The lowest error was found with the bi-exponential function approximations to the in vivo residue functions from both normal and infarcted tissue. CONCLUSION: A bi-exponential model should therefore be used in future numerical simulations of DSC-MRI instead of the exponential function.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".