1,468 research outputs found
Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis
Time delay estimation (TDE) is a critical and challenging step in all
ultrasound elastography methods. A growing number of TDE techniques require an
approximate but robust and fast method to initialize solving for TDE. Herein,
we present a fast method for calculating an approximate TDE between two radio
frequency (RF) frames of ultrasound. Although this approximate TDE can be
useful for several algorithms, we focus on GLobal Ultrasound Elastography
(GLUE), which currently relies on Dynamic Programming (DP) to provide this
approximate TDE. We exploit Principal Component Analysis (PCA) to find the
general modes of deformation in quasi-static elastography, and therefore call
our method PCA-GLUE. PCA-GLUE is a data-driven approach that learns a set of
TDE principal components from a training database in real experiments. In the
test phase, TDE is approximated as a weighted sum of these principal
components. Our algorithm robustly estimates the weights from sparse feature
matches, then passes the resulting displacement field to GLUE as initial
estimates to perform a more accurate displacement estimation. PCA-GLUE is more
than ten times faster than DP in estimation of the initial displacement field
and yields similar results.Comment: Accepted to be Published in 2019, 41th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
Berlin, German
Global Ultrasound Elastography Using Convolutional Neural Network
Displacement estimation is very important in ultrasound elastography and
failing to estimate displacement correctly results in failure in generating
strain images. As conventional ultrasound elastography techniques suffer from
decorrelation noise, they are prone to fail in estimating displacement between
echo signals obtained during tissue distortions. This study proposes a novel
elastography technique which addresses the decorrelation in estimating
displacement field. We call our method GLUENet (GLobal Ultrasound Elastography
Network) which uses deep Convolutional Neural Network (CNN) to get a coarse
time-delay estimation between two ultrasound images. This displacement is later
used for formulating a nonlinear cost function which incorporates similarity of
RF data intensity and prior information of estimated displacement. By
optimizing this cost function, we calculate the finer displacement by
exploiting all the information of all the samples of RF data simultaneously.
The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain
images from our technique is very much close to that of strain images from
GLUE. While most elastography algorithms are sensitive to parameter tuning, our
robust algorithm is substantially less sensitive to parameter tuning.Comment: 4 pages, 4 figures; added acknowledgment section, submission type
late
Optimal two-stage filtering of elastograms
In ultrasound elastography, tissue axial strains are obtained through the differentiation of measured axial displacements. However, during the measurement process, the displacement signals are often contaminated with de-correlation noise caused by changes in the speckle pattern in the tissue. Thus, the application of the gradient operator on the displacement signals results in the presence of amplified noise in the axial strains, which severely obscures the useful information. The use of an effective denoising scheme is therefore imperative. In this paper, a method based on a two-stage consecutive filtering approach is proposed for the accurate estimation of axial strains. The presented method considers a cascaded system of a frequency filter and a time window, which are both designed such that the overall system operates optimally as a minimum variance estimator. Experimentation on simulated signals shows that the two-stage scheme employed in this study has good potential as a denoising method for ultrasound elastograms
Viscoelastic modulus reconstruction using time harmonic vibrations
This paper presents a new iterative reconstruction method to provide
high-resolution images of shear modulus and viscosity via the internal
measurement of displacement fields in tissues. To solve the inverse problem, we
compute the Fr\'echet derivatives of the least-squares discrepancy functional
with respect to the shear modulus and shear viscosity. The proposed iterative
reconstruction method using this Fr\'echet derivative does not require any
differentiation of the displacement data for the full isotropic linearly
viscoelastic model, whereas the standard reconstruction methods require at
least double differentiation. Because the minimization problem is ill-posed and
highly nonlinear, this adjoint-based optimization method needs a very
well-matched initial guess. We find a good initial guess. For a well-matched
initial guess, numerical experiments show that the proposed method considerably
improves the quality of the reconstructed viscoelastic images.Comment: 15 page
Influence of wall thickness and diameter on arterial shear wave elastography: a phantom and finite element study
Quantitative, non-invasive and local measurements of arterial mechanical
properties could be highly beneficial for early diagnosis of cardiovascular
disease and follow up of treatment. Arterial shear wave elastography (SWE)
and wave velocity dispersion analysis have previously been applied to
measure arterial stiffness. Arterial wall thickness (h) and inner diameter (D)
vary with age and pathology and may influence the shear wave propagation.
Nevertheless, the effect of arterial geometry in SWE has not yet been
systematically investigated. In this study the influence of geometry on the
estimated mechanical properties of plates (h = 0.5–3 mm) and hollow
cylinders (h = 1, 2 and 3 mm, D = 6 mm) was assessed by experiments in
phantoms and by finite element method simulations. In addition, simulations
in hollow cylinders with wall thickness difficult to achieve in phantoms
were performed (h = 0.5–1.3 mm, D = 5–8 mm). The phase velocity curves obtained from experiments and simulations were compared in the frequency
range 200–1000 Hz and showed good agreement (R2 = 0.80 ± 0.07 for plates
and R2 = 0.82 ± 0.04 for hollow cylinders). Wall thickness had a larger effect
than diameter on the dispersion curves, which did not have major effects above
400 Hz. An underestimation of 0.1–0.2 mm in wall thickness introduces an
error 4–9 kPa in hollow cylinders with shear modulus of 21–26 kPa. Therefore,
wall thickness should correctly be measured in arterial SWE applications for
accurate mechanical properties estimation
New Image Processing Methods for Ultrasound Musculoskeletal Applications
In the past few years, ultrasound (US) imaging modalities have received increasing interest as diagnostic tools for orthopedic applications. The goal for many of these novel ultrasonic methods is to be able to create three-dimensional (3D) bone visualization non-invasively, safely and with high accuracy and spatial resolution. Availability of accurate bone segmentation and 3D reconstruction methods would help correctly interpreting complex bone morphology as well as facilitate quantitative analysis. However, in vivo ultrasound images of bones may have poor quality due to uncontrollable motion, high ultrasonic attenuation and the presence of imaging artifacts, which can affect the quality of the bone segmentation and reconstruction results.
In this study, we investigate the use of novel ultrasonic processing methods that can significantly improve bone visualization, segmentation and 3D reconstruction in ultrasound volumetric data acquired in applications in vivo. Specifically, in this study, we investigate the use of new elastography-based, Doppler-based and statistical shape model-based methods that can be applied to ultrasound bone imaging applications with the overall major goal of obtaining fast yet accurate 3D bone reconstructions. This study is composed to three projects, which all have the potential to significantly contribute to this major goal.
The first project deals with the fast and accurate implementation of correlation-based elastography and poroelastography techniques for real-time assessment of the mechanical properties of musculoskeletal tissues. The rationale behind this project is that,
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in the future, elastography-based features can be used to reduce false positives in ultrasonic bone segmentation methods based on the differences between the mechanical properties of soft tissues and the mechanical properties of hard tissues. In this study, a hybrid computation model is designed, implemented and tested to achieve real time performance without compromise in elastographic image quality .
In the second project, a Power Doppler-based signal enhancement method is designed and tested with the intent of increasing the contrast between soft tissue and bone while suppressing the contrast between soft tissue and connective tissue, which is often a cause of false positives in ultrasonic bone segmentation problems. Both in-vitro and in-vivo experiments are performed to statistically analyze the performance of this method.
In the third project, a statistical shape model based bone surface segmentation method is proposed and investigated. This method uses statistical models to determine if a curve detected in a segmented ultrasound image belongs to a bone surface or not. Both in-vitro and in-vivo experiments are performed to statistically analyze the performance of this method.
I conclude this Dissertation with a discussion on possible future work in the field of ultrasound bone imaging and assessment
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