Clinical feasibility of accelerated diffusion weighted imaging with deep learning reconstruction for patients with acute neurologic symptoms

Motivation: Timely diagnosis of patient with acute neurologic symptom is critical. Detecting small lesions on DWI can be challenging and experience plays a significant role, especially when motion artifacts affect image quality. Goal(s): Our goal was to shorten acquisition time and provide highly sensitive images of small lesions, particularly for emergency clinicians unfamiliar with DWI. Approach: We included 80 patients, comparing quantitative and qualitative analyses between conventional and deep-learning DWI. We assessed diagnostic performance among experienced neuroradiologists and primary care physician. Results: Results showed similar image quality between two sequences, but deep-learning DWI exhibited superior lesion conspicuity. Diagnostic accuracy remained consistent between the two. Impact: Deep-learning DWI offers comparable image quality with significant shorter acquisition time. It also enhances detection of tiny brain lesion, providing diagnostic confidence to less experienced clinicians in emergency situation.