PerfTool: A software platform for investigating bolus‐tracking perfusion imaging quantification strategies
Bibliographic record
Abstract
PURPOSE: To develop a software platform, PerfTool (for perfusion tool), for the comprehensive evaluation of bolus-tracking quantitative perfusion imaging methods and algorithms, along with a method to rapidly visualize and evaluate the performance of algorithms. MATERIALS AND METHODS: Algorithms were evaluated interactively with PerfTool using synthetic DeltaR2* data sets with different perfusion parameter permutations (known as test patterns). Patient data and test patterns were used to evaluate a standard singular value deconvolution (SVD) approach (sSVD) and a reformulated implementation (rSVD) that is insensitive to arterial-tissue delay (ATD), and to explore the effect of the SVD regularization parameter (p(SVD)) on CBF estimates. RESULTS: The CBF overestimation resulting from sensitivity to ATD in sSVD compared to rSVD was demonstrated with the patient data, and the effect was confirmed using a test pattern. The same test pattern demonstrated the CBF underestimation resulting from high p(SVD) thresholds. CONCLUSION: PerfTool is an extensible software tool that allows perfusion measurements to be obtained by different methods, and is flexible enough to incorporate new developments and apply them to real patient data and test patterns.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".