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

Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography

Ultrasound elastography estimates the mechanical properties of the tissue from two Radio-Frequency (RF) frames collected before and after tissue deformation due to an external or internal force. This work focuses on strain imaging in quasi-static elastography, where the tissue undergoes slow deformations and strain images are estimated as a surrogate for elasticity modulus. The quality of the strain image depends heavily on the underlying deformation, and even the best strain estimation algorithms cannot estimate a good strain image if the underlying deformation is not suitable. Herein, we introduce a new method for tracking the RF frames and selecting automatically the best possible pair. We achieve this by decomposing the axial displacement image into a linear combination of principal components (which are calculated offline) multiplied by their corresponding weights. We then use the calculated weights as the input feature vector to a multi-layer perceptron (MLP) classifier. The output is a binary decision, either 1 which refers to good frames, or 0 which refers to bad frames. Our MLP model is trained on in-vivo dataset and tested on different datasets of both in-vivo and phantom data. Results show that by using our technique, we would be able to achieve higher quality strain images compared to the traditional methods of picking up pairs that are 1, 2 or 3 frames apart. The training phase of our algorithm is computationally expensive and takes few hours, but it is only done once. The testing phase chooses the optimal pair of frames in only 1.9 ms

Ultrasound Elastography using Machine Learning

This thesis aims at solving two main problems that we face in ultrasound elastography, namely fast strain estimation and radio frequency (RF) frame selection. We rely on machine learning concepts such as Principal Components Analysis (PCA), multi-layer perceptron (MLP) and convolutional neural networks (CNN) to build 3 models that are trained on both phantom and in vivo data. In our first work, we developed a method to estimate the initial displacement between two ultrasound RF frames using PCA. We first compute an initial displacement estimate of around 1% of the samples, and then decompose the displacement into a linear combination of principal components (obtained offline during the training step). Our method assumes that the initial displacement of the whole image could also be described by this linear combination of principal components. This yields the same result that we could have had if we run dynamic programming (DP). The advantage of using PCA is that we could compute the same initial displacement image more than 10 times faster than DP. We then pass the result to GLobal Ultrasound Elastography (GLUE) for fine-tuning it, so we call the method PCA-GLUE. In our second work, we developed a novel method to address the problem of RF frame selection in ultrasound elastography. Intuitively, we would like to have a classifier that gives a binary 1 to RF frame pairs that yield high-quality strain images. We make use of our previous work where we decompose the initial displacement between two RF frames into a weight vector multiplied by some principal components. We consider the weight vector as our input feature vector to an MLP model. Given two RF frames I1 and I2, the MLP model predicts the normalized cross correlation (NCC) between the two RF frames I1 and I2′ (I2′ is I2 after being displaced according to the displacement of GLUE/PCA-GLUE). Our final contribution in this line of research is the introduction of a CNN-based method for RF frame selection as follows. First, we changed the architecture from an MLP model to a CNN that takes the two RF frames on two channels. The CNN has better results compared to the MLP model due to having more features. Second, we improved the automatic labelling of the data by having physical conditions that must be satisfied together in order to consider the pair as a suitable pair of RF frames

Novel 3D Ultrasound Elastography Techniques for In Vivo Breast Tumor Imaging and Nonlinear Characterization

Breast cancer comprises about 29% of all types of cancer in women worldwide. This type of cancer caused what is equivalent to 14% of all female deaths due to cancer. Nowadays, tissue biopsy is routinely performed, although about 80% of the performed biopsies yield a benign result. Biopsy is considered the most costly part of breast cancer examination and invasive in nature. To reduce unnecessary biopsy procedures and achieve early diagnosis, ultrasound elastography was proposed.;In this research, tissue displacement fields were estimated using ultrasound waves, and used to infer the elastic properties of tissues. Ultrasound radiofrequency data acquired at consecutive increments of tissue compression were used to compute local tissue strains using a cross correlation method. In vitro and in vivo experiments were conducted on different tissue types to demonstrate the ability to construct 2D and 3D elastography that helps distinguish stiff from soft tissues. Based on the constructed strain volumes, a novel nonlinear classification method for human breast tumors is introduced. Multi-compression elastography imaging is elucidated in this study to differentiate malignant from benign tumors, based on their nonlinear mechanical behavior under compression. A pilot study on ten patients was performed in vivo, and classification results were compared with biopsy diagnosis - the gold standard. Various nonlinear parameters based on different models, were evaluated and compared with two commonly used parameters; relative stiffness and relative tumor size. Moreover, different types of strain components were constructed in 3D for strain imaging, including normal axial, first principal, maximum shear and Von Mises strains. Interactive segmentation algorithms were also evaluated and applied on the constructed volumes, to delineate the stiff tissue by showing its isolated 3D shape.;Elastography 3D imaging results were in good agreement with the biopsy outcomes, where the new classification method showed a degree of discrepancy between benign and malignant tumors better than the commonly used parameters. The results show that the nonlinear parameters were found to be statistically significant with p-value \u3c0.05. Moreover, one parameter; power-law exponent, was highly statistically significant having p-value \u3c 0.001. Additionally, volumetric strain images reconstructed using the maximum shear strains provided an enhanced tumor\u27s boundary from the surrounding soft tissues. This edge enhancement improved the overall segmentation performance, and diminished the boundary leakage effect. 3D segmentation provided an additional reliable means to determine the tumor\u27s size by estimating its volume.;In summary, the proposed elastographic techniques can help predetermine the tumor\u27s type, shape and size that are considered key features helping the physician to decide the sort and extent of the treatment. The methods can also be extended to diagnose other types of tumors, such as prostate and cervical tumors. This research is aimed toward the development of a novel \u27virtual biopsy\u27 method that may reduce the number of unnecessary painful biopsies, and diminish the increasingly risk of cancer

Ultrafast Ultrasound Imaging

Among medical imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), ultrasound imaging stands out due to its temporal resolution. Owing to the nature of medical ultrasound imaging, it has been used for not only observation of the morphology of living organs but also functional imaging, such as blood flow imaging and evaluation of the cardiac function. Ultrafast ultrasound imaging, which has recently become widely available, significantly increases the opportunities for medical functional imaging. Ultrafast ultrasound imaging typically enables imaging frame-rates of up to ten thousand frames per second (fps). Due to the extremely high temporal resolution, this enables visualization of rapid dynamic responses of biological tissues, which cannot be observed and analyzed by conventional ultrasound imaging. This Special Issue includes various studies of improvements to the performance of ultrafast ultrasoun

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, iii 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

Characterizing anisotropy in fibrous soft materials by MR elastography of slow and fast shear waves

The general objective of this work was to develop experimental methods based on magnetic resonance elastography (MRE) to characterize fibrous soft materials. Mathematical models of tissue biomechanics capable of predicting injury, such as traumatic brain injury (TBI), are of great interest and potential. However, the accuracy of predictions from such models depends on accuracy of the underlying material parameters. This dissertation describes work toward three aims. First, experimental methods were designed to characterize fibrous materials based on a transversely isotropic material model. Second, these methods are applied to characterize the anisotropic properties of white matter brain tissue ex vivo. Third, a theoretical investigation of the potential application of MRE to probe nonlinear mechanical behavior of soft tissue was performed. These studies provide new methods to characterize anisotropic and nonlinear soft materials as well as contributing significantly to our understanding of the behavior of specific biological soft tissues