Comparison of Deep Learning and Signal Processing Methods for Removing a Ringing Artifact from Ultrasound Signals

Minimally Invasive Surgeries can benefit from having miniaturized sensors on surgical graspers to provide additional information to the surgeons. In this work, a 6 mm ultrasound transducer was added to a surgical grasper, intended to measure acoustic properties of the tissue. However, the ultrasound sensor has a ringing artifact arising from the decaying oscillation of its piezo element, and at short travel distances, the artifact blends with the acoustic echo. Without a method to remove the artifact from the blended signal, this makes it impossible to measure one of the main characteristics of an ultrasound waveform - Time of Flight. In this paper, six filtering methods to clear the artifact from the ultrasound waveform were compared: Bandpass filter, Adaptive Least Mean Squares (LMS) filter, Spectrum Suppression (SPS), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Following each filtering method, four time of flight extraction methods were compared: Magnitude Threshold, Envelope Peak Detection, Cross-correlation and Short-time Fourier Transform (STFT). The RNN with Cross-correlation method pair was shown to be optimal for this task, performing with the root mean square error of 3.6 %.