Reverberation Noise Suppression in the Aperture Domain Using 3D Fully Convolutional Neural Networks

Reverberation clutter is image noise resulting from multiple reflections between tissue layers and leads to image degradation. We propose a 3D fully convolutional neural network (FCNN) to selectively remove reverberation noise from ultrasound channel data. A training set was generated using Field II Pro to simulate full synthetic aperture channel data. Reverberation noise was approximated by adding bandpass filtered noise to the noise-free simulated channel data. This channel data, with and without noise, was used to train a 3D FCNN with a custom architecture to remove the reverberation and thermal noise. The network was evaluated on a simulated validation set and on an ATS 549 phantom using a L12-3v transducer connected to a Verasonics Vantage 256 system with reverberation noise. The resulting network shows significant reverberation noise reduction. Across the validation set, the normalized RMS loss between the noisy and noise free images decreased from 77% to 39% after being decluttered using 3D FCNN.