Radiation Dose Reduction in Computed Tomography Perfusion of Acute Ischemic Stroke Patients Using a Denoising Autoencoder
Bibliographic record
Abstract
Background Ischemic stroke results from the occlusion of a cerebral artery and is a leading cause of mortality and disability worldwide. Multimodal computed tomography (CT), including CT perfusion (CTP) and CT angiography, is crucial to acute stroke evaluation but involves higher radiation exposure than non-contrast CT due to repeated volumetric imaging. Reducing CTP radiation dose without losing image quality remains important challenge. This study proposes a machine-learning-based denoising autoencoder (DAE) to reduce noise introduced by dose reduction while preserving the quality of CTP images and perfusion parameter maps. Method CTP images from 48 acute ischemic stroke patients from the PRove-IT trial were used. Low-dose conditions were simulated by adding Gaussian and Poisson noise at varying strengths, with Poisson noise applied in the sinogram domain and Gaussian noise in the image domain. The DAE was trained using paired noisy and original images. Performance was evaluated by assessing structural similarity of CTP source images and perfusion maps, as well as clinical accuracy based on infarct core volumes derived from cerebral blood flow maps. Results The DAE restored strong structural similarity in CTP source images at dose reductions up to 90% (SSIM 0.81, PSNR 43 dB). Perfusion maps showed slightly lower similarity. Clinically, denoising substantially improved the accuracy of CBF-derived infarct core volumes, reducing mean absolute error from 10-30 mL in noisy images to approximately 4–16 mL and restoring high agreement with reference volumes (R 2 > 0.85). Conclusions These findings demonstrate that substantial simulated radiation dose reductions can be compensated by the DAE while preserving clinically meaningful perfusion-derived biomarkers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".