Deep Learning Based Angular Compounding for Accelerated Plane Wave Ultrasound Imaging

The quality of ultrasound plane wave imaging benefits from compounding multiple angle acquisitions to reconstruct an image. However, the acquisition of additional data lowers the frame rate and - in presence of motion - the data integrity. This work presents an approach to reconstruct high-quality images from a reduced set of angles making use of artificial deep neural networks (DNNs). Unlike existing approaches that utilize DNNs for transforming beamformed data into image data directly, the presented DNN is trained to produce per-pixel angular weighting factors within an existing reconstruction pipeline.