Quality versus quantity of dynamic CT perfusion images at isodose
Bibliographic record
Abstract
Whole-brain high temporal resolution CT perfusion (CTP) is now feasible with wide-detector row CT scanners, but the optimal dose distribution of dynamic images remains unknown. In this study, we investigated the accuracy of perfusion parameters estimated in digital perfusion phantoms generated at various temporal resolutions with fixed scan dose. In accordance with CTP guidelines, simulated dose was set to a time-density curve (TDC) noise of 10 HU at a sampling interval of 2.0 s over 60 s, and higher temporal resolutions of 1.0 and 0.5 s intervals were investigated at 14 and 20 HU of noise, respectively. Monte Carlo simulations with known ground truth perfusion were conducted to test the performance of model-independent and model-dependent deconvolution algorithms as a function of temporal resolution at isodose. Tissue TDCs were simulated by convolving gamma-variate, linear or boxcar residue functions with a patient arterial TDC before adding Gaussian noise at the appropriate level then sampling at the investigated temporal resolutions. A digital brain perfusion phantom with physiological ground truth perfusion was similarly investigated. Only cerebral blood flow (CBF) estimates with the model-dependent algorithm marginally improved at higher temporal resolution as indicated by mean absolute error (MAE; 7.1±4.6 ml/min/100 g at 0.5 s, 9.6±6.0 ml/min/100 g at 2.0 s) but not with the modelindependent algorithm (MAE: 11.6±11.4 ml/min/100 g at 0.5 s, 11.3±11.7 ml/min/100 g at 2.0 s). Higher temporal resolution did not improve parameter estimation in the brain perfusion phantom. For the investigated temporal resolutions and simulated CTP dose, dose distribution appears negligible.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 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".