Deep Learning-Driven Generation of Diffusion-Weighted Imaging for Acute Ischemic Stroke from Non-Contrast Computed Tomography

Motivation: Noncontrast computed tomography(NCCT), commonly used for its rapidity in acute ischemic stroke(AIS) diagnosis, often fails to identify early ischemic changes, whereas MRI, despite its superior sensitivity , may introduce critical delays due to its lengthier image acquisition time. Goal(s): This study aims to investigate the feasibility of converting NCCT images of AIS patients to diffusion-weighted (DW) images using deep learning techniques. Approach: The proposed method utilizes an enhanced CycleGAN model to generate synthetic DW images from CT scans. Results: The synthetic DW images generated by our network achieved good performance with a PSNR of 28.30, an SSIM of 0.843, and an NMSE of 0.293. Impact: This work might be translated to clinical settings to help physicians make clinical decisions for patients by providing with high-quality MR images in emergency situations.