Robust Estimation of Displacement in Real-Time Freehand Ultrasound Strain Imaging

Displacement Estimation in Ultrasound Elastography Using Pyramidal Convolutional Neural Network

In this article, two novel deep learning methods are proposed for displacement estimation in ultrasound elastography (USE). Although convolutional neural networks (CNNs) have been very successful for displacement estimation in computer vision, they have been rarely used for USE. One of the main limitations is that the radio frequency (RF) ultrasound data, which is crucial for precise displacement estimation, has vastly different frequency characteristics compared with images in computer vision. Top-rank CNN methods used in computer vision applications are mostly based on a multilevel strategy, which estimates finer resolution based on coarser ones. This strategy does not work well for RF data due to its large high-frequency content. To mitigate the problem, we propose modified pyramid warping and cost volume network (MPWC-Net) and RFMPWC-Net, both based on PWC-Net, to exploit information in RF data by employing two different strategies. We obtained promising results using networks trained only on computer vision images. In the next step, we constructed a large ultrasound simulation database and proposed a new loss function to fine-tune the network to improve its performance. The proposed networks and well-known optical flow networks as well as state-of-the-art elastography methods are evaluated using simulation, phantom, and in vivo data. Our two proposed networks substantially outperform current deep learning methods in terms of contrast-to-noise ratio (CNR) and strain ratio (SR). Also, the proposed methods perform similar to the state-of-the-art elastography methods in terms of CNR and have better SR by substantially reducing the underestimation bias.

Probe Sector Matching for Freehand 3D Ultrasound Reconstruction

A 3D ultrasound image reconstruction technique, named probe sector matching (PSM), is proposed in this paper for a freehand linear array ultrasound probe equipped with multiple sensors, providing the position and attitude of the transducer and the pressure between the transducer and the target surface. The proposed PSM method includes three main steps. First, the imaging target and the working range of the probe are set to be the center and the radius of the imaging field of view, respectively. To reconstruct a 3D volume, the positions of all necessary probe sectors are pre-calculated inversely to form a sector database. Second, 2D cross-section probe sectors with the corresponding optical positioning, attitude and pressure information are collected when the ultrasound probe is moving around the imaging target. Last, an improved 3D Hough transform is used to match the plane of the current probe sector to the existing sector images in the sector database. After all pre-calculated probe sectors are acquired and matched into the 3D space defined by the sector database, a 3D ultrasound reconstruction is completed. The PSM is validated through two experiments: a virtual simulation using a numerical model and a lab experiment using a real physical model. The experimental results show that the PSM effectively reduces the errors caused by changes in the target position due to the uneven surface pressure or the inhomogeneity of the transmission media. We conclude that the PSM proposed in this study may help to design a lightweight, inexpensive and flexible ultrasound device with accurate 3D imaging capacity.