Bedside Ultrasound Vector Doppler Imaging System With GPU Processing and Deep Learning

Recent innovations in vector flow imaging promise to bring the modality closer to clinical application and allow for more comprehensive, high frame-rate vascular assessments. One such innovation is plane-wave multi-angle vector Doppler, where pulsed Doppler principles from multiple steering angles are used to realize vector flow imaging at frame rates upward of 1000 frames per second (fps). Currently, vector Doppler is limited by the presence of aliasing artifacts that have prevented its reliable realization at the bedside. In this work, we present a new aliasing-resistant vector Doppler imaging system that can be deployed at the bedside using a programmable ultrasound core, graphics processing unit (GPU) processing, and deep-learning principles. The framework supports two operational modes: 1) live imaging at 17 fps, where vector flow imaging serves to guide image view navigation in blood vessels with complex dynamics, and 2) on-demand replay mode, where flow data acquired at high frame rates of over 1000 fps is depicted as a slow-motion playback at 60 fps using an aliasing-resistant vector projectile visualization. Using with our new system, aliasing-free vector flow cineloops were successfully obtained in a stenosis phantom experiment and in human bifurcation imaging scans. This system represents a major engineering advance toward the clinical adoption of vector flow imaging.