CT Perfusion Map Generation from Multiphase CTA Using a Generative Adversarial Model for Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Multiphase CT Angiography (mCTA) has shown potential as a diagnostic tool for acute ischemic stroke because it captures dynamic changes in the cerebral vasculature. However, mCTA has limitations in assessing brain tissue perfusion, which reduces its clinical interpretability. To address this limitation, we aimed to develop a generative adversarial network (GAN) that generates CTP-like maps from mCTA. This approach aims to improve the interpretability of mCTA. MATERIALS AND METHODS: A total of 714 cases with NCCT, CTP, mCTA, and follow-up NCCT/MRI were analyzed across internal and external data sets. A GAN was trained to generate multiparametric CTP maps (Tmax, CBF, CBV). The performance of the model was evaluated using the Structural Similarity Index (SSIM), peak signal-to-noise ratio (PSNR), and Fréchet Inception Distance (FID) compared with actual CTP maps. Clinical utility was assessed by predicting infarct core and penumbra using threshold-based segmentation and evaluating metrics such as the Dice coefficient, area under the receiver operating characteristic curve (AUC) of dichotomized infarct volumes of < 70 mL, and mismatch ratio following DEFUSE 3 criteria, compared with the ground truth of actual CTP prediction. RESULTS: The GAN achieved SSIM, 0.65-0.66; PSNR, 20.4-20.8; and FID, 15.8-17.0 on internal data, surpassing both CycleGAN (SSIM: 0.608-0.642, PSNR: 18.2-19.7, FID: 27.6-32.5) and Pix2Pix (SSIM: 0.630-0.645, PSNR: 19.5-19.7, FID: 19.4-20.8) across all metrics. Predicted penumbra and infarct core showed Dice coefficients of 0.672 and 0.468, with strong correlations (penumbra: 0.921, core: 0.902) and AUCs of 0.854 (95% CI, 0.819-0.888) (mismatch ratio) and 0.850 (95% CI, 0.817-0.884) (dichotomized infarct core). External data validation yielded Dice coefficients of 0.481 (penumbra) and 0.301 (core) with AUCs of 0.720 (95% CI, 0.589-0.808) (mismatch ratio) and 0.703 (95% CI, 0.528-0.794) (dichotomized infarct core). CONCLUSIONS: The GAN effectively generated CTP-like maps from mCTA, improving interpretability and demonstrating promising diagnostic performance, particularly for resource-limited settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".