The feasibility of deep learning-based synthetic contrast-enhanced CT from nonenhanced CT in emergency department patients with acute abdominal pain.

Our objective was to investigate the feasibility of deep learning-based synthetic contrast-enhanced CT (DL-SCE-CT) from nonenhanced CT (NECT) in patients who visited the emergency department (ED) with acute abdominal pain (AAP). We trained an algorithm generating DL-SCE-CT using NECT with paired precontrast/postcontrast images. For clinical application, 353 patients from three institutions who visited the ED with AAP were included.

On-Line Video Event Detection by Constraint Flow.

We present a novel approach in describing and detecting the composite video events based on scenarios, which constrain the configurations of target events by temporal-logical structures of primitive events. We propose a new scenario description method to represent composite events more fluently and efficiently, and discuss an on-line event detection algorithm based on a combinatorial optimization. For this purpose, constraint flow-a dynamic configuration of scenario constraints-is first generated automatically by our scenario parsing algorithm.