Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis

Zayed, Abdelrahman; Rivaz, Hassan

doi:10.1109/embc.2019.8857242

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…We set p = 5 RF lines as trials showed us that choosing a value for p more than 5 would not improve the quality of the strain image [12]. The number of hidden units in the MLP classifier is a hyperparameter that is chosen in a way so as to have the highest accuracy on the validation data.…”

Section: Resultsmentioning

confidence: 99%

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Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography

Zayed

Rivaz

2019

Lecture Notes in Computer Science

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Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force. Different levels of deformation are obtained from different tissue types depending on their mechanical properties, where stiffer tissues deform less. Given two radio frequency (RF) frames collected before and after some deformation, we estimate displacement and strain images by comparing the RF frames. The quality of the strain image is dependent on the type of motion that occurs during deformation. In-plane axial motion results in high-quality strain images, whereas out-of-plane motion results in low-quality strain images. In this paper, we introduce a new method using a convolutional neural network (CNN) to determine the suitability of a pair of RF frames for elastography in only 5.4 ms. Our method could also be used to automatically choose the best pair of RF frames, yielding a high-quality strain image. The CNN was trained on 3,818 pairs of RF frames, while testing was done on 986 new unseen pairs, achieving an accuracy of more than 91%. The RF frames were collected from both phantom and in vivo data.

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Section: Resultsmentioning

confidence: 99%

“…It is important to note that steps 2 and 3 in Algorithm 1 are very computationally complex. As such, they cannot be performed in real-time for selecting optimal pairs of RF RF frames I 1 and I 2 are passed to PCA-GLUE [16], [17] to obtain the displacement image.…”

Section: A Data Collectionmentioning

confidence: 99%

Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography

Zayed

Rivaz

2019

Lecture Notes in Computer Science

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“…3) PCA-GLUE, which relies on DP to compute the initial displacement map, is robust to potential DP failures. This work is an extension of our recent work [39], [40], with the following major changes. First, we replace the multi-layer perceptron (MLP) classifier with a more robust one that can generalize better to unseen data.…”

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confidence: 93%

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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“…In addition, to avoid the over-smoothing induced by the quadratic regularization function used in GLUE and GUEST, total variation regularization has been proposed in [57]. Furthermore, principal components of the displacement field have been investigated in [58], [59] to reduce the execution time of GLUE. In [60], multi-scale pyramidal approach has been adopted to refine the DP integer estimate.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Gradient Similarity for Robust Time Delay Estimation in Ultrasound Elastography

Ashikuzzaman¹,

Hall²,

Rivaz³

2022

Preprint

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Energy-based ultrasound elastography techniques minimize a regularized cost function consisting of data and continuity terms to obtain local displacement estimates based on the local time-delay estimation (TDE) between radio-frequency (RF) frames. The data term associated with the existing techniques takes only the amplitude similarity into account and hence is not sufficiently robust to the outlier samples present in the RF frames under consideration. This drawback creates noticeable artifacts in the strain image. To resolve this issue, we propose to formulate the data function as a linear combination of the amplitude and gradient similarity constraints. We estimate the adaptive weight concerning each similarity term following an iterative scheme. Finally, we optimize the non-linear cost function in an efficient manner to convert the problem to a sparse system of linear equations which are solved for millions of variables. We call our technique rGLUE: robust data term in GLobal Ultrasound Elastography. rGLUE has been validated using simulation, phantom, in vivo liver, and breast datasets. In all of our experiments, rGLUE substantially outperforms the recent elastography methods both visually and quantitatively. For simulated, phantom, and in vivo datasets, respectively, rGLUE achieves 107%, 18%, and 23% improvements of signalto-noise ratio (SNR) and 61%, 19%, and 25% improvements of contrast-to-noise ratio (CNR) over GLUE, a recently-published elastography algorithm.

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Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis

Cited by 7 publications

References 12 publications

Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography

Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography

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

Incorporating Gradient Similarity for Robust Time Delay Estimation in Ultrasound Elastography

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