Calibration of sonographic gel probe covers for in-vivo mechanical testing

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (leaf 29).Cervical insufficiency is a condition in pregnancy in which the cervix asymptomatically dilates in the absence of uterine contractions, resulting in a spontaneous preterm delivery. The condition is often misdiagnosed and presents a significant challenge for the clinical community. In order to establish better diagnostic criteria for cervical insufficiency and to improve assessment of preterm delivery risk for the individual patient, a non-invasive medical imaging tool, which uses ultrasound elastography to test the mechanical properties of cervical tissue, has been developed. The hand-held ultrasound indentation system will enable in vivo collection of stress-strain data from patients that will provide researchers with the necessary information to be used in material modeling and improve diagnosis of cervical insufficiency. The device consists of an ultrasound probe, enclosed by a gel-filled cover. The mechanical properties of the covers vary with each cap as well as with time and temperature. Therefore, in order to ensure accurate measurement, the probe covers must be calibrated prior to use. An experimental study was carried out to examine the effects of various testing conditions on the mechanical behavior of the probe covers. Different freezing and thawing techniques were explored in order to determine favorable conditions in order to preserve the integrity of the probes between the time of manufacture and actual use. From the results of the research, the appropriate combination of testing conditions for probe calibration was determined, as well as freezing and thawing techniques for probe preservation.by Panasaya Charenkavanich.S.B

Accelerating 3-D GPU-based Motion Tracking for Ultrasound Strain Elastography Using Sum-Tables: Analysis and Initial Results.

Now, with the availability of 3-D ultrasound data, a lot of research efforts are being devoted to developing 3-D ultrasound strain elastography (USE) systems. Because 3-D motion tracking, a core component in any 3-D USE system, is computationally intensive, a lot of efforts are under way to accelerate 3-D motion tracking. In the literature, the concept of Sum-Table has been used in a serial computing environment to reduce the burden of computing signal correlation, which is the single most computationally intensive component in 3-D motion tracking. In this study, parallel programming using graphics processing units (GPU) is used in conjunction with the concept of Sum-Table to improve the computational efficiency of 3-D motion tracking. To our knowledge, sum-tables have not been used in a GPU environment for 3-D motion tracking. Our main objective here is to investigate the feasibility of using sum-table-based normalized correlation coefficient (ST-NCC) method for the above-mentioned GPU-accelerated 3-D USE. More specifically, two different implementations of ST-NCC methods proposed by Lewis et al. and Luo-Konofagou are compared against each other. During the performance comparison, the conventional method for calculating the normalized correlation coefficient (NCC) was used as the baseline. All three methods were implemented using compute unified device architecture (CUDA; Version 9.0, Nvidia Inc., CA, USA) and tested on a professional GeForce GTX TITAN X card (Nvidia Inc., CA, USA). Using 3-D ultrasound data acquired during a tissue-mimicking phantom experiment, both displacement tracking accuracy and computational efficiency were evaluated for the above-mentioned three different methods. Based on data investigated, we found that under the GPU platform, Lou-Konofaguo method can still improve the computational efficiency (17-46%), as compared to the classic NCC method implemented into the same GPU platform. However, the Lewis method does not improve the computational efficiency in some configuration or improves the computational efficiency at a lower rate (7-23%) under the GPU parallel computing environment. Comparable displacement tracking accuracy was obtained by both methods

Analysis of the Nonlinear Parameter in Inverse Problems of Elasticity

The mechanical properties of tissues are important indicators of tissue “health”. It has been recorded and acknowledged that diseased tissues due to cancer or other conditions tend to stiffen with increase in strain, exhibiting a nonlinear stress-strain behavior. In literature, hyperelastic models, such as Veronda-Westmann and Blatz, have been widely used to model soft tissues. These models are characterized by an exponential function and two material parameters, namely the shear modulus μ and a nonlinearity parameter γ. A variety of methods and techniques have been developed to solve inverse problems in elasticity to determine these properties given the mechanical response of the tissues. Reconstruction of the nonlinear parameter using noisy measured displacement data is a difficult problem, and obtaining a well-posed solution is a challenge. This thesis is directed towards the improvement in the reconstruction of the nonlinear parameter, γ, by introducing a new parameter, which is a combination of γ and the first invariant of the Green deformation tensor. Comparative study is carried out between reconstructions of γ directly from previously existing formulations and the reconstruction of γ from the new parameter, for 2D problems. Numerical experiments are conducted and the performance is tested and compared based on different criteria like shape of the stiff regions (representing the diseased tissue), the contrast in γ and robustness for different loading conditions. Different arrangements and sizes of stiff inclusions are tested and critically analyzed. It is found that obtaining the distribution of γ from the new parameter results in a much better reconstruction than by directly optimizing for γ

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

Deformation Estimation and Assessment of Its Accuracy in Ultrasound Images

This thesis aims to address two problems; one in ultrasound elastography and one in image registration. The first problem entails estimation of tissue displacement in Ultrasound Elastography (UE). UE is an emerging technique used to estimate mechanical properties of tissue. It involves calculating the displacement field between two ultrasound Radio Frequency (RF) frames taken before and after a tissue deformation. A common way to calculate the displacement is to use correlation based approaches. However, these approaches fail in the presence of signal decorrelation. To address this issue, Dynamic Programming was used to find the optimum displacement using all the information on the RF-line. Although taking this approach improved the results, some failures persisted. In this thesis, we have formulated the DP method on a tree. Doing so allows for more information to be used for estimating the displacement and therefore reducing the error. We evaluated our method on simulation, phantom and real patient data. Our results shows that the proposed method outperforms the previous method in terms of accuracy with small added computational cost. In this work, we also address a problem in image registration. Although there is a vast literature in image registration, quality evaluation of registration is a field that has not received as much attention. This evaluation becomes even more crucial in medical imaging due to the sensitive nature of the field. We have addressed the said problem in the context of ultrasound guided radiotherapy. Image guidance has become an important part of radiotherapy wherein image registration is a critical step. Therefore, an evaluation of this registration can play an important role in the outcome of the therapy. In this work, we propose using both bootstrapping and supervised learning methods to evaluate the registration. We test our methods on 2D and 3D data acquired from phantom and patients. According to our results, both methods perform well while having advantages and disadvantages over one another. Supervised learning methods offer more accuracy and less computation time. On the other hand, for bootstrapping, no training data is required and also offers more sensitivity

Design and evaluation of a full-sensitivity tilt scanning interferometry system for displacement field tomography and profilometry

This thesis reports the investigation and further development of a tilt scanning interferometry system for surface profilometry and sub-surface tomographic applications. A new 3D full sensitivity interferometry system extends the work carried out on a previous prototype that was capable of measuring displacement along one lateral plus the axial component. Depth-resolved imaging is achieved by the acquisition of a sequence of 2D interferograms whilst the illumination beam undergoes a constant rate of tilt and full sensitivity displacement is achieved by performing scans from multiple illumination directions. The comparison of phase volumes from two successive series of scans enables 3D displacement fields to be determined. The working principle that describes the technique is presented, covering the reconstruction of a depth-resolved sample from the detected intensity distribution. The system performance is studied, including measurement repeatability and factors that affect the depth resolution and depth range. Depth resolution is fundamentally limited by the range of the illumination tilting angle and the new system design enables a larger range. However, the resolution is degraded by a frequency chirp that appears in the temporal interference signal when a large tilting range is scanned. It is shown through a numerical simulation that the chirp depends on the curvature of the illumination wavefront and also on the position of the pivot axis of the illumination beam. Data processing methods are proposed to overcome these limitations and their effects are illustrated with experimental measurements of opaque surfaces and a weakly scattering phantom with internal features. Displacement measurements involving a controlled rigid body rotation and tilt of a weakly scattering phantom were completed to validate the expected deformations. Both in-plane and out-of-plane components were measured

A Novel Ultrasound Elastography Technique for Evaluating Tumor Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Breast Cancer

Breast cancer is the second most diagnosed cancer in women, estimated to affect 1 in 8 women during their lifetime. About 10% to 20% of new breast cancer cases are diagnosed with locally advanced breast cancer (LABC). LABC tumors are usually larger than 5 cm and/or attached to the skin or chest wall. It has been reported that when such cases are treated with surgery alone, metastasis and mortality rates are high, especially where skin involvement or attachment to the chest wall is extensive. As such, efficient treatment for this kind of breast cancer includes neoadjuvant chemotherapy (NAC) to shrink the tumor and detach it from the chest wall followed by surgery. Several studies have shown that there is a strong correlation between response to NAC and improved treatment outcomes, including survival rate. Unfortunately, 30% to 40% of patients do not respond to chemotherapy, hence losing critical treatment time and resources. Predicting a patient’s response at the early stages of treatment can help physicians make informed decisions about whether to continue the treatment or use an alternative treatment if a poor response is predicted. Such early and accurate response prediction can shorten the wasted time and reduce resources dedicated to patients while they endure significant side effects. Therefore, it is important to identify this group of non-responder patients as early as possible so that they can be prescribed alternative treatments. Current methods for evaluating LABC response to NAC are based on changes in tumor dimensions using physical examinations or standard anatomical imaging. Such changes may take several months to be detectable. Studies have shown that there is a correlation between LABC response to NAC and tumor softening. In other words, in contrast to responder patients where tumor stiffness generally decreases in response to NAC, in non-responder patients the stiffness of the tumor increases or does not change significantly. As such, a reliable and widely available breast elastography technique can have a major impact on the effective treatment of LABC patients. In this study, we first develop a tissue-mechanics-based method for improving the accuracy of ultrasound elastography. This method consists of 3 steps that are applied to the displacement fields generated from conventional motion-tracking methods. These three steps include: smoothing the displacement fields using Laplacian filtering, enforcing tissue incompressibility equation to refine the displacement fields, and finally enforcing tissue compatibility equation to refine the strain fields. The method was promising through validation using in silico, phantom, and in vivo studies. A huge improvement of this method compared to other motion-tracking methods is its ability in generating lateral displacement with high accuracy. This becomes especially important when the displacement and strain fields are used as inputs to an inverse-problem framework for calculating the stiffness characteristics of tissue, for example, Young’s modulus. We then use this enhanced ultrasound elastography technique to assess the response of LABC patients to NAC based on monitoring the stiffness of their tumors throughout the chemotherapy course. Our results show that this method is effective in predicting patients’ responses accurately as early as 1 week after NAC initiation

Characterization of carotid artery plaques using noninvasive vascular ultrasound elastography

L'athérosclérose est une maladie vasculaire complexe qui affecte la paroi des artères (par l'épaississement) et les lumières (par la formation de plaques). La rupture d'une plaque de l'artère carotide peut également provoquer un accident vasculaire cérébral ischémique et des complications. Bien que plusieurs modalités d'imagerie médicale soient actuellement utilisées pour évaluer la stabilité d'une plaque, elles présentent des limitations telles que l'irradiation, les propriétés invasives, une faible disponibilité clinique et un coût élevé. L'échographie est une méthode d'imagerie sûre qui permet une analyse en temps réel pour l'évaluation des tissus biologiques. Il est intéressant et prometteur d’appliquer une échographie vasculaire pour le dépistage et le diagnostic précoces des plaques d’artère carotide. Cependant, les ultrasons vasculaires actuels identifient uniquement la morphologie d'une plaque en termes de luminosité d'écho ou l’impact de cette plaque sur les caractéristiques de l’écoulement sanguin, ce qui peut ne pas être suffisant pour diagnostiquer l’importance de la plaque. La technique d’élastographie vasculaire non-intrusive (« noninvasive vascular elastography (NIVE) ») a montré le potentiel de détermination de la stabilité d'une plaque. NIVE peut déterminer le champ de déformation de la paroi vasculaire en mouvement d’une artère carotide provoqué par la pulsation cardiaque naturelle. En raison des différences de module de Young entre les différents tissus des vaisseaux, différents composants d’une plaque devraient présenter différentes déformations, caractérisant ainsi la stabilité de la plaque. Actuellement, les performances et l’efficacité numérique sous-optimales limitent l’acceptation clinique de NIVE en tant que méthode rapide et efficace pour le diagnostic précoce des plaques vulnérables. Par conséquent, il est nécessaire de développer NIVE en tant qu’outil d’imagerie non invasif, rapide et économique afin de mieux caractériser la vulnérabilité liée à la plaque. La procédure à suivre pour effectuer l’analyse NIVE consiste en des étapes de formation et de post-traitement d’images. Cette thèse vise à améliorer systématiquement la précision de ces deux aspects de NIVE afin de faciliter la prédiction de la vulnérabilité de la plaque carotidienne. Le premier effort de cette thèse a été dédié à la formation d'images (Chapitre 5). L'imagerie par oscillations transversales a été introduite dans NIVE. Les performances de l’imagerie par oscillations transversales couplées à deux estimateurs de contrainte fondés sur un modèle de déformation fine, soit l’ « affine phase-based estimator (APBE) » et le « Lagrangian speckle model estimator (LSME) », ont été évaluées. Pour toutes les études de simulation et in vitro de ce travail, le LSME sans imagerie par oscillation transversale a surperformé par rapport à l'APBE avec imagerie par oscillations transversales. Néanmoins, des estimations de contrainte principales comparables ou meilleures pourraient être obtenues avec le LSME en utilisant une imagerie par oscillations transversales dans le cas de structures tissulaires complexes et hétérogènes. Lors de l'acquisition de signaux ultrasonores pour la formation d'images, des mouvements hors du plan perpendiculaire au plan de balayage bidimensionnel (2-D) existent. Le deuxième objectif de cette thèse était d'évaluer l'influence des mouvements hors plan sur les performances du NIVE 2-D (Chapitre 6). À cette fin, nous avons conçu un dispositif expérimental in vitro permettant de simuler des mouvements hors plan de 1 mm, 2 mm et 3 mm. Les résultats in vitro ont montré plus d'artefacts d'estimation de contrainte pour le LSME avec des amplitudes croissantes de mouvements hors du plan principal de l’image. Malgré tout, nous avons néanmoins obtenu des estimations de déformations robustes avec un mouvement hors plan de 2.0 mm (coefficients de corrélation supérieurs à 0.85). Pour un jeu de données cliniques de 18 participants présentant une sténose de l'artère carotide, nous avons proposé d'utiliser deux jeux de données d'analyses sur la même plaque carotidienne, soit des images transversales et longitudinales, afin de déduire les mouvements hors plan (qui se sont avérés de 0.25 mm à 1.04 mm). Les résultats cliniques ont montré que les estimations de déformations restaient reproductibles pour toutes les amplitudes de mouvement, puisque les coefficients de corrélation inter-images étaient supérieurs à 0.70 et que les corrélations croisées normalisées entre les images radiofréquences étaient supérieures à 0.93, ce qui a permis de démontrer une plus grande confiance lors de l'analyse de jeu de données cliniques de plaques carotides à l'aide du LSME. Enfin, en ce qui concerne le post-traitement des images, les algorithmes NIVE doivent estimer les déformations des parois des vaisseaux à partir d’images reconstituées dans le but d’identifier les tissus mous et durs. Ainsi, le dernier objectif de cette thèse était de développer un algorithme d'estimation de contrainte avec une résolution de la taille d’un pixel ainsi qu'une efficacité de calcul élevée pour l'amélioration de la précision de NIVE (Chapitre 7). Nous avons proposé un estimateur de déformation de modèle fragmenté (SMSE) avec lequel le champ de déformation dense est paramétré avec des descriptions de transformées en cosinus discret, générant ainsi des composantes de déformations affines (déformations axiales et latérales et en cisaillement) sans opération mathématique de dérivées. En comparant avec le LSME, le SMSE a réduit les erreurs d'estimation lors des tests de simulations, ainsi que pour les mesures in vitro et in vivo. De plus, la faible mise en oeuvre de la méthode SMSE réduit de 4 à 25 fois le temps de traitement par rapport à la méthode LSME pour les simulations, les études in vitro et in vivo, ce qui pourrait permettre une implémentation possible de NIVE en temps réel.Atherosclerosis is a complex vascular disease that affects artery walls (by thickening) and lumens (by plaque formation). The rupture of a carotid artery plaque may also induce ischemic stroke and complications. Despite the use of several medical imaging modalities to evaluate the stability of a plaque, they present limitations such as irradiation, invasive property, low clinical availability and high cost. Ultrasound is a safe imaging method with a real time capability for assessment of biological tissues. It is clinically used for early screening and diagnosis of carotid artery plaques. However, current vascular ultrasound technologies only identify the morphology of a plaque in terms of echo brightness or the impact of the vessel narrowing on flow properties, which may not be sufficient for optimum diagnosis. Noninvasive vascular elastography (NIVE) has been shown of interest for determining the stability of a plaque. Specifically, NIVE can determine the strain field of the moving vessel wall of a carotid artery caused by the natural cardiac pulsation. Due to Young’s modulus differences among different vessel tissues, different components of a plaque can be detected as they present different strains thereby potentially helping in characterizing the plaque stability. Currently, sub-optimum performance and computational efficiency limit the clinical acceptance of NIVE as a fast and efficient method for the early diagnosis of vulnerable plaques. Therefore, there is a need to further develop NIVE as a non-invasive, fast and low computational cost imaging tool to better characterize the plaque vulnerability. The procedure to perform NIVE analysis consists in image formation and image post-processing steps. This thesis aimed to systematically improve the accuracy of these two aspects of NIVE to facilitate predicting carotid plaque vulnerability. The first effort of this thesis has been targeted on improving the image formation (Chapter 5). Transverse oscillation beamforming was introduced into NIVE. The performance of transverse oscillation imaging coupled with two model-based strain estimators, the affine phase-based estimator (APBE) and the Lagrangian speckle model estimator (LSME), were evaluated. For all simulations and in vitro studies, the LSME without transverse oscillation imaging outperformed the APBE with transverse oscillation imaging. Nonetheless, comparable or better principal strain estimates could be obtained with the LSME using transverse oscillation imaging in the case of complex and heterogeneous tissue structures. During the acquisition of ultrasound signals for image formation, out-of-plane motions which are perpendicular to the two-dimensional (2-D) scan plane are existing. The second objective of this thesis was to evaluate the influence of out-of-plane motions on the performance of 2-D NIVE (Chapter 6). For this purpose, we designed an in vitro experimental setup to simulate out-of-plane motions of 1 mm, 2 mm and 3 mm. The in vitro results showed more strain estimation artifacts for the LSME with increasing magnitudes of out-of-plane motions. Even so, robust strain estimations were nevertheless obtained with 2.0 mm out-of-plane motion (correlation coefficients higher than 0.85). For a clinical dataset of 18 participants with carotid artery stenosis, we proposed to use two datasets of scans on the same carotid plaque, one cross-sectional and the other in a longitudinal view, to deduce the out-of-plane motions (estimated to be ranging from 0.25 mm to 1.04 mm). Clinical results showed that strain estimations remained reproducible for all motion magnitudes since inter-frame correlation coefficients were higher than 0.70, and normalized cross-correlations between radiofrequency images were above 0.93, which indicated that confident motion estimations can be obtained when analyzing clinical dataset of carotid plaques using the LSME. Finally, regarding the image post-processing component of NIVE algorithms to estimate strains of vessel walls from reconstructed images with the objective of identifying soft and hard tissues, we developed a strain estimation method with a pixel-wise resolution as well as a high computation efficiency for improving NIVE (Chapter 7). We proposed a sparse model strain estimator (SMSE) for which the dense strain field is parameterized with Discrete Cosine Transform descriptions, thereby deriving affine strain components (axial and lateral strains and shears) without mathematical derivative operations. Compared with the LSME, the SMSE reduced estimation errors in simulations, in vitro and in vivo tests. Moreover, the sparse implementation of the SMSE reduced the processing time by a factor of 4 to 25 compared with the LSME based on simulations, in vitro and in vivo results, which is suggesting a possible implementation of NIVE in real time