Physiological Motion Reduction Using Lagrangian Tracking for Electrode Displacement Elastography

Pohlman, Robert M.; Varghese, Tomy

doi:10.1016/j.ultrasmedbio.2019.11.001

Cited by 5 publications

(1 citation statement)

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“…Quasi-static elastography, which is our focus in this paper, is more related to estimating the deformation of the tissue when an external force is applied [10], [11]. Recent work has shown success in performing ultrasound elastography using different methods such as spatial angular compounding [12], multi-compression strategy [13], Lagrangian tracking [14] and guided circumferential waves [15]. In addition, other work has exploited the power of deep learning to achieve the same goal [16]- [23].…”

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confidence: 99%

Fast Strain Estimation and Frame Selection in Ultrasound Elastography Using Machine Learning

Zayed

Rivaz

2021

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Ultrasound Elastography aims to determine the mechanical properties of the tissue by monitoring tissue deformation due to internal or external forces. Tissue deformations are estimated from ultrasound radio frequency (RF) signals and are often referred to as time delay estimation (TDE). Given two RF frames I 1 and I 2 , we can compute a displacement image which shows the change in the position of each sample in I 1 to a new position in I 2 . Two important challenges in TDE include high computational complexity and the difficulty in choosing suitable RF frames. Selecting suitable frames is of high importance because many pairs of RF frames either do not have acceptable deformation for extracting informative strain images or are decorrelated and deformation cannot be reliably estimated. Herein, we introduce a method that learns 12 displacement modes in quasi-static elastography by performing Principal Component Analysis (PCA) on displacement fields of a large training database. In the inference stage, we use dynamic programming (DP) to compute an initial displacement estimate of around 1% of the samples, and then decompose this sparse displacement into a linear combination of the 12 displacement modes. Our method assumes that the displacement of the whole image could also be described by this linear combination of principal components. We then use the GLobal Ultrasound Elastography (GLUE) method to fine-tune the result yielding the exact displacement image. Our method, which we call PCA-GLUE, is more than 10 times faster than DP in calculating the initial displacement map while giving the same result. This is due to converting the problem of estimating millions of variables in DP into a much simpler problem of only 12 unknown weights of the principal components. Our second contribution in this paper is determining the suitability of the frame pair I 1 and I 2 for strain estimation, which we achieve by using the weight vector that we calculated for PCA-GLUE as an input to a multilayer perceptron (MLP) classifier. We validate PCA-GLUE using simulation, phantom, and in vivo data. Our classifier takes only 1.5 ms during the testing phase and has an F1-measure of more than 92% when tested on 1,430 instances collected from both phantom and in vivo datasets.

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