Radiation dose reduction in computed tomography perfusion of acute ischemic stroke patients using a denoising...

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.

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.

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 (R² > 0.85).

These findings demonstrate that substantial simulated radiation dose reductions can be compensated by the DAE while preserving clinically meaningful perfusion-derived biomarkers.