Ultrasound Elastography: Direct Strain Estimation

Ultrasound elastography involves measuring the mechanical properties of tissue, and has many applications in diagnostics and intervention. Ultrasound elastography techniques mainly target obtaining strain images from raw Radio-Frequency (RF) echo field produced by ultrasound machine without adding any hardware. A common step in different elastography methods is imaging the tissue while it undergoes deformation and estimating the displacement field from the images. A popular next step is to estimate tissue strain, which gives clues into the underlying tissue elasticity modulus. To estimate the strain, one should compute the gradient of the displacement image, which amplifies the noise. The noise is commonly minimized by least square estimation of the gradient from multiple displacement measurements, which reduces the noise by sacrificing image resolution. The first part of this thesis propose a new method which adaptively adjusts the level and orientation of the smoothing strain images using two different mechanisms. First, the precision of the displacement field decreases significantly in the regions with high signal decorrelation, which requires increasing the smoothness. Second, smoothing the strain field at the boundaries between different tissue types blurs the edges, which can render small targets invisible. To minimize blurring and noise, we perform anisotropic smoothing and perform smoothing parallel to the direction of the edges. The first mechanism ensures that textures/variations in the strain image reflect underlying tissue properties and are not caused by errors in the displacement estimation. The second mechanism keeps the edges between different tissue structures sharp while minimizing the noise. The second part of this thesis introduces a 2D strain imaging technique called SHORTCUT (meSHing Of gRadienT in DP for direCt Ultrasound elasTography) based on minimizing a cost function. The cost function incorporates similarity of echo amplitudes and tissue continuity. The proposed technique is fast, robust and accurate and it directly produces the strain images from RF data using a novel dynamic programming (DP) configuration. Unlike the standard DP algorithm which discretizes the decision space (displacement field) and search in the space of piecewise constant functions, the proposed DP discretizes the gradient of the decision space (strain field) and search the space of continuous piecewise linear functions. Eliminating the displacement differentiation block and performing a global search instead of local search which exist in all of the available strain estimation techniques result in substantial improvement in SNR, CNR and accuracy of the estimations. The effectiveness of the proposed methods is investigated through simulation data, phantom experiments, and in vivo patient data

Accuracy Assessment of Time Delay Estimation in Ultrasound Elastography

The accuracy of time-delay estimation (TDE) in ultrasound elastography is usually measured by calculating the value of the normalized cross correlation (NCC) at the estimated displacement. NCC value is usually high if the TDE is correct. However, it could be very high at a displacement estimate with large error, a well-known problem in TDE referred to as peak-hopping. Furthermore, NCC value could suffer from jitter error, which is due to electric noise and signal decorrelation. In this thesis, we propose a novel method to assess the accuracy of TDE by investigating the NCC profile around the estimated time-delay in a supervised approach. First, we extract seven features from the NCC profile, and utilize a linear support vector machine (SVM) to classify the peak-hopping and jitter error. The results on simulation, phantom and in-vivo data show the significant improvement in the classification accuracy realizing from the proposed algorithm compared to the obtained form the state of the art techniques. Second, we build on our model by utilizing the continuity features in the axial and lateral directions as a prior knowledge. We show that these features also improve the sensitivity and specificity of the classifier. After extracting the continuity features in addition to the seven features, we show the performance improvement of the proposed model on the available data sets. Furthermore, we show that our proposed model could be trained by other elastography methods in future, since we use a new elastography algorithm to train the model. Third, we compare the performance of the method developed using well-known classifiers in the literature and then study the importance of the proposed features using the mean decrease impurity method of the random forest classifier

A Coherent Method for Denoising Ultrasound Data with Applications in Super Resolution and Elastography

Ultrasound (US) imaging is a widely used medical modality since it is inexpensive non-invasive and portable. However, the quality of US is limited by physical constraints (e.g. thermal noise) and hardware restrictions (e.g. the number of sensors in a US probe). To increase the quality and improve the resolution of US images, I proposed two novel algorithms, namely COherent Denoising for Elastography (CODE), for removing noise of RF data for elastography technique and coherent ultrasound super-resolution to perform a novel super-resolution technique. I first propose CODE to improve the estimation of tissue displacement in ultrasound elastography. Ultrasound elastography computes the mechanical properties of tissues affected by an internal of external force. The radio frequency data acquired from ultrasound is usually corrupted with noise that leads ultrasound elastography techniques for fail. To remove this noise I proposed CODE that despite the local denoising algorithms, keeps the information of the RF data for elastography. I investigate two state-of-the-art elastography methods, GLobal Ultrasound Elastography (GLUE), and; (ii) Dynamic Programming Analytic Minimization elastography, and results shows the improvement of the strain maps on both patient and phantom data. I then introduce a super-resolution technique for improving the quality of ultrasound B-mode images. The resolution of ultrasound images is limited by hardware constraints and physical restrictions. Conventionally, ultrasound machines use interpolation techniques for improving the resolution of the B-mode images. However, I propose a new method for coherent ultrasound super-resolution that overcomes conventional approaches in both qualitative and quantitative measures. In both cases, I proposed a mathematical framework that justified the behavior of the algorithms and tested the methods on both in-vivo, and phantom data, and discussed qualitatively and quantitatively