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.

微创手术（Minimally-Invasive Surgeries）可通过在手术抓取器上搭载微型传感器，为外科医师提供额外诊疗信息，其中一种颇具应用潜力的传感器为超声换能器（ultrasound transducer）。在较长传播距离下，超声换能器可精准测量其发射超声波的飞行时间，并据此对抓取的组织进行分类。然而，超声换能器的压电元件（piezo element）会产生衰减振荡，进而引发振铃伪影（ringing artifact）；在短传播距离下，该伪影会与声学回波（acoustic echo）发生混叠。若无法从混叠信号中去除该伪影，将无法准确测量波形的飞行时间。现有研究可结合经典信号处理与深度学习方法，对原始超声信号进行滤波，去除振铃伪影，并从滤波后的信号中提取波形的飞行时间。本数据集包含两套数据，分别用于训练和测试用于去除振铃伪影、进而提取波形飞行时间的算法。所有原始（实测）信号均采用统一实验装置采集：示波器连接超声驱动器，驱动安装于盛水容器上的换能器，以在严格可控的环境中模拟生物组织的声学特性。训练数据集包含两组信号对。第一组包含993对信号，每对信号均由一条原始超声信号（声学回波与振铃伪影混叠）与一条目标滤波信号（仅保留所需回波）组成。该组信号以500 MHz的原始采样频率进行采样。第二组与第一组结构一致，但所有信号均以26倍的降采样因子进行降采样。本训练数据集仅涵盖2 cm至4 cm（含端点）的传播距离，因为在此距离范围内的水中，回波与振铃伪影的分离度足够高，可通过人工方式提取。信号对在覆盖的距离区间内近似均匀分布。测试数据集同样包含两组原始超声信号。第一组包含270条信号，采集自0.5 cm至4.0 cm之间的9个传播距离，每个距离对应30条信号，同时附带每个距离对应的真实飞行时间。该组信号以500 MHz的原始采样频率进行采样。第二组与第一组结构一致，但所有信号均以26倍的降采样因子进行降采样。两套数据集中的所有信号均已完成对齐。