Real-Time Reverberation Suppression in Ultrasound Channel Signals Using a Permuted 2D Convolutional Neural Network

Diffuse reverberation noise in ultrasound imaging arises from multiple reflections or secondary scattering of an echo, such as when a strong echo reflects multiple times between a fascial layer and the transducer. Unlike coherent reverberation artifacts, such as multiple reflections within an arterial wall that produce obvious ringdown in the image, these multiple reflections produce speckle-like artifacts that overlay and obscure anatomical targets and are difficult to distinguish from tissue speckle. This noise also degrades image reconstruction and interferes with imaging techniques that rely on displacement or time shifts in the ultrasonic echoes. This work introduces a permuted 2D convolutional neural network (2DCNN) for real-time reverberation suppression in ultrasound channel signals. This technique can be used to improve B-mode image quality or can be used as a pre-processing filter for techniques that require channel or beamsummed signals. The proposed architecture offers significant computational advantages over the previously implementations using 3D convolutional networks, enabling real-time implementation on existing hardware at 15 fps. The 2DCNN was trained on an enhanced dataset that combines Field II and Fullwave simulations, incorporating a broad range of acoustic affects, including reverberation noise, aberration, and attenuation. Validation on liver and kidney scans from 15 volunteers demonstrated improvements in image quality metrics related to the removal of diffuse reverberation noise, including increasing contrast, generalized contrast-to-noise ratio (GCNR), and lag-one coherence (LOC).