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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 %.
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Ultrasound Waveforms with and without Ringdown Artifacts
Minimally-Invasive Surgeries can benefit from having miniaturized sensors on surgical graspers to provide additional information to the surgeons. One such potential sensor is an ultrasound transducer. At long travel distances, the ultrasound transducer can accurately measure its ultrasound wave's time of flight, and from it, classify the grasped tissue. However, the ultrasound transducer 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 the waveform's time of flight.It is possible to use both classical signal processing and deep learning methods to filter raw ultrasound signals, removing the ringing artifact from them, and from the filtered signals, to obtain the time of flight. In this dataset, two datasets are provided to train and test algorithms developed for filtering out the ringdown artifact, and for subsequently extracting the waveform's time of flight. All measured (raw) signals were collected the same experimental setup: an oscilloscope connected to an ultrasound driver to drive a transducer attached to a liquid water container, in an attempt to mimic tissue properties in a tightly controlled environment.The training dataset consists of two groups of signal pairs. The first group consists of 993 signal pairs, with each pair consisting of a raw ultrasound signal (with the acoustic echo blended with the ringing artifact), and a target filtered signal (with only the desired echo). Signals in the first group are sampled at the original sampling frequency of 500 MHz. The second group is like the first group, but with all signals downsampled by a factor of 26. This training dataset includes only travel distances from 2 cm to 4 cm, inclusively, because at these distances in water, the echo is sufficiently separated from the ringdown artifact to be manually extractable. The signal pairs are approximately equally distributed between the distances covered.The test dataset similarly consists of two groups of raw ultrasound signals. The first group consists of 270 signals, collected at 9 travel distances between 0.5 cm and 4.0 cm, with 30 signals per distance. It also includes the associated true times of flight for each distance. Signals in the first group are sampled at the original sampling frequency of 500 MHz. The second group is like the first group, but with all signals downsampled by a factor of 26. All signals in both datasets are aligned.
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- Award ID(s):
- 2036255
- PAR ID:
- 10353053
- Publisher / Repository:
- IEEE DataPort
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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