13 research outputs found

    Towards Computer Aided Management of Kidney Disease

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    Autosomal dominant polycystic kidney disease (ADPKD) is the fourth most common cause of kidney transplant worldwide accounting for 7-10% of all cases. Although ADPKD usually progresses over many decades, accurate risk prediction is an important task. Identifying patients with progressive disease is vital to providing new treatments being developed and enable them to enter clinical trials for new therapy. Among other factors, total kidney volume (TKV) is a major biomarker predicting the progression of ADPKD. Consortium for Radiologic Imaging Studies in Polycystic Kidney Disease (CRISP) have shown that TKV is an early, and accurate measure of cystic burden and likely growth rate. It is strongly associated with loss of renal function. While ultrasound (US) has proven as an excellent tool for diagnosing the disease; monitoring short-term changes using ultrasound has been shown to not be accurate. This is attributed to high operator variability and reproducibility as compared to tomographic modalities such as CT and MR (Gold standard). Ultrasound has emerged as one of the standout modalities for intra-procedural imaging and with methods for spatial localization has afforded us the ability to track 2D ultrasound in the physical space in which it is being used. In addition to this, the vast amount of recorded tomographic data can be used to generate statistical shape models that allow us to extract clinical value from archived image sets. Renal volumetry is of great interest in the management of chronic kidney diseases (CKD). In this work, we have implemented a tracked ultrasound system and developed a statistical shape model of the kidney. We utilize the tracked ultrasound to acquire a stack of slices that are able to capture the region of interest, in our case kidney phantoms, and reconstruct 3D volume from spatially localized 2D slices. Approximate shape data is then extracted from this 3D volume using manual segmentation of the organ and a shape model is fit to this data. This generates an instance from the shape model that best represents the scanned phantom and volume calculation is done on this instance. We observe that we can calculate the volume to within 10% error in estimation when compared to the gold standard volume of the phantom

    Optimization and validation of a new 3D-US imaging robot to detect, localize and quantify lower limb arterial stenoses

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    L’athérosclérose est une maladie qui cause, par l’accumulation de plaques lipidiques, le durcissement de la paroi des artères et le rétrécissement de la lumière. Ces lésions sont généralement localisées sur les segments artériels coronariens, carotidiens, aortiques, rénaux, digestifs et périphériques. En ce qui concerne l’atteinte périphérique, celle des membres inférieurs est particulièrement fréquente. En effet, la sévérité de ces lésions artérielles est souvent évaluée par le degré d’une sténose (réduction >50 % du diamètre de la lumière) en angiographie, imagerie par résonnance magnétique (IRM), tomodensitométrie ou échographie. Cependant, pour planifier une intervention chirurgicale, une représentation géométrique artérielle 3D est notamment préférable. Les méthodes d’imagerie par coupe (IRM et tomodensitométrie) sont très performantes pour générer une imagerie tridimensionnelle de bonne qualité mais leurs utilisations sont dispendieuses et invasives pour les patients. L’échographie 3D peut constituer une avenue très prometteuse en imagerie pour la localisation et la quantification des sténoses. Cette modalité d’imagerie offre des avantages distincts tels la commodité, des coûts peu élevés pour un diagnostic non invasif (sans irradiation ni agent de contraste néphrotoxique) et aussi l’option d’analyse en Doppler pour quantifier le flux sanguin. Étant donné que les robots médicaux ont déjà été utilisés avec succès en chirurgie et en orthopédie, notre équipe a conçu un nouveau système robotique d’échographie 3D pour détecter et quantifier les sténoses des membres inférieurs. Avec cette nouvelle technologie, un radiologue fait l’apprentissage manuel au robot d’un balayage échographique du vaisseau concerné. Par la suite, le robot répète à très haute précision la trajectoire apprise, contrôle simultanément le processus d’acquisition d’images échographiques à un pas d’échantillonnage constant et conserve de façon sécuritaire la force appliquée par la sonde sur la peau du patient. Par conséquent, la reconstruction d’une géométrie artérielle 3D des membres inférieurs à partir de ce système pourrait permettre une localisation et une quantification des sténoses à très grande fiabilité. L’objectif de ce projet de recherche consistait donc à valider et optimiser ce système robotisé d’imagerie échographique 3D. La fiabilité d’une géométrie reconstruite en 3D à partir d’un système référentiel robotique dépend beaucoup de la précision du positionnement et de la procédure de calibration. De ce fait, la précision pour le positionnement du bras robotique fut évaluée à travers son espace de travail avec un fantôme spécialement conçu pour simuler la configuration des artères des membres inférieurs (article 1 - chapitre 3). De plus, un fantôme de fils croisés en forme de Z a été conçu pour assurer une calibration précise du système robotique (article 2 - chapitre 4). Ces méthodes optimales ont été utilisées pour valider le système pour l’application clinique et trouver la transformation qui convertit les coordonnées de l’image échographique 2D dans le référentiel cartésien du bras robotisé. À partir de ces résultats, tout objet balayé par le système robotique peut être caractérisé pour une reconstruction 3D adéquate. Des fantômes vasculaires compatibles avec plusieurs modalités d’imagerie ont été utilisés pour simuler différentes représentations artérielles des membres inférieurs (article 2 - chapitre 4, article 3 - chapitre 5). La validation des géométries reconstruites a été effectuée à l`aide d`analyses comparatives. La précision pour localiser et quantifier les sténoses avec ce système robotisé d’imagerie échographique 3D a aussi été déterminée. Ces évaluations ont été réalisées in vivo pour percevoir le potentiel de l’utilisation d’un tel système en clinique (article 3- chapitre 5).Atherosclerosis is a disease caused by the accumulation of lipid deposits inducing the remodeling and hardening of the vessel wall, which leads to a progressive narrowing of arteries. These lesions are generally located on the coronary, carotid, aortic, renal, digestive and peripheral arteries. With regards to peripheral vessels, lower limb arteries are frequently affected. The severity of arterial lesions are evaluated by the stenosis degree (reduction > 50.0 % of the lumen diameter) using angiography, magnetic resonance angiography (MRA), computed tomography (CT) and ultrasound (US). However, to plan a surgical therapeutic intervention, a 3D arterial geometric representation is notably preferable. Imaging methods such as MRA and CT are very efficient to generate a three-dimensional imaging of good quality even though their use is expensive and invasive for patients. 3D-ultrasound can be perceived as a promising avenue in imaging for the location and the quantification of stenoses. This non invasive, non allergic (i.e, nephrotoxic contrast agent) and non-radioactive imaging modality offers distinct advantages in convenience, low cost and also multiple diagnostic options to quantify blood flow in Doppler. Since medical robots already have been used with success in surgery and orthopedics, our team has conceived a new medical 3D-US robotic imaging system to localize and quantify arterial stenoses in lower limb vessels. With this new technology, a clinician manually teaches the robotic arm the scanning path. Then, the robotic arm repeats with high precision the taught trajectory and controls simultaneously the ultrasound image acquisition process at even sampling and preserves safely the force applied by the US probe. Consequently, the reconstruction of a lower limb arterial geometry in 3D with this system could allow the location and quantification of stenoses with high accuracy. The objective of this research project consisted in validating and optimizing this 3D-ultrasound imaging robotic system. The reliability of a 3D reconstructed geometry obtained with 2D-US images captured with a robotic system depends considerably on the positioning accuracy and the calibration procedure. Thus, the positioning accuracy of the robotic arm was evaluated in the workspace with a lower limb-mimicking phantom design (article 1 - chapter 3). In addition, a Z-phantom was designed to assure a precise calibration of the robotic system. These optimal methods were used to validate the system for the clinical application and to find the transformation which converts image coordinates of a 2D-ultrasound image into the robotic arm referential. From these results, all objects scanned by the robotic system can be adequately reconstructed in 3D. Multimodal imaging vascular phantoms of lower limb arteries were used to evaluate the accuracy of the 3D representations (article 2 - chapter 4, article 3 - chapter 5). The validation of the reconstructed geometry with this system was performed by comparing surface points with the manufacturing vascular phantom file surface points. The accuracy to localize and quantify stenoses with the 3D-ultrasound robotic imaging system was also determined. These same evaluations were analyzed in vivo to perceive the feasibility of the study

    Quantitative ultrasound: low variance estimation of backscattering and attenuation coefficients

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    One of the main limitations of ultrasound imaging is that image quality and interpretation depend on the skill of the user and the experience of the clinician. Quantitative ultrasound (QUS) methods provide objective, system-independent estimates of tissue properties such as acoustic attenuation and backscattering properties of tissue, which are valuable as objective tools for both diagnosis and intervention. Accurate and precise estimation of these properties requires correct compensation for intervening tissue attenuation. Prior attempts to estimate intervening-tissues attenuation based on minimizing cost functions that compared backscattered echo data to models have resulted in limited precision and accuracy. The first contribution of this thesis is that we incorporate the prior information of piece-wise continuity of QUS parameters as an L2 norm regularization term into our cost function to overcome these limitations. We further propose to calculate this cost function using Dynamic Programming (DP), a computationally efficient optimization algorithm that finds the global optimum. Our results on tissue-mimicking phantoms show that DP substantially outperforms a state-of-the-art method in terms of both estimation bias and variance. The second contribution of this thesis is that to further improve the accuracy and precision of this DP method, we propose to use L1 norm instead of L2 norm as the regularization term in our cost function and optimize the function using DP. Our results show that DP with L1 regularization reduces bias of attenuation and backscatter parameters even further compared to DP with L2 norm. Besides, we employ DP to estimate the QUS parameters of a new phantom with large scatterer size and compare the results of LSq, L2 norm DP and L1 norm DP. Our results show that L1 norm DP outperforms L2 norm DP, which itself outperforms LSq. In the future, the contributions of this thesis can potentially be used for finding imaging biomarkers associated with different types of pathology and help clinicians obtain an objective assessment of intrinsic tissue properties

    Medical image analysis methods for anatomical surface reconstruction using tracked 3D ultrasound

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    The thesis focuses on a study of techniques for acquisition and reconstruction of surface data from anatomical objects by means of tracked 3D ultrasound. In the context of the work two experimental scanning systems are developed and tested on both artificial objects and biological tissues. The first system is based on the freehand ultrasound principle and utilizes a conventional 2D ultrasound transducer coupled with an electromechanical 3D position tracker. The main properties and the basic features of this system are discussed. A number of experiments show that its accuracy in the close to ideal conditions reaches 1.2 mm RMS. The second proposed system implements the sequential triggered scanning approach. The system consists of an ultrasound machine, a workstation and a scanning body (a moving tank filled with liquid and a transducer fixation block) that performs transducer positioning and tracking functions. The system is tested on artificial and real bones. The performed experiments illustrate that it provides significantly better accuracy than the freehand ultrasound (about 0.2 mm RMS) and allows acquiring regular data with a good precision. This makes such a system a promising tool for orthopaedic and trauma surgeons during contactless X-ray-free examinations of injured extremities. The second major subject of the thesis concerns development of medical image analysis methods for 3D surface reconstruction and 2D object detection. We introduce a method based on mesh-growing surface reconstruction that is designed for noisy and sparse data received from 3D tracked ultrasound scanners. A series of experiments on synthetic and ultrasound data show an appropriate reconstruction accuracy. The reconstruction error is measured as the averaged distance between the faces of the mesh and the points from the cloud. Dependently on the initial settings of the method the error varies in range 0.04 - 0.2% for artificial data and 0.3 - 0.7 mm for ultrasound bone data. The reconstructed surfaces correctly interpolate the original point clouds and demonstrate proper smoothness. The next significant problem considered in the work is 2D object detection. Although medical object detection is not integrated into the developed scanning systems, it can be used as a possible further extension of the systems for automatic detection of specific anatomical structures. We analyse the existent object detection methods and introduce a modification of the one based on the popular Generalized Hough Transform (GHT). Unlike the original GHT, the developed method is invariant to rotation and uniform scaling, and uses an intuitive two-point parametrization. We propose several implementations of the feature-to-vote conversion function with the corresponding vote analysis principles. Special attention is devoted to a study of the hierarchical vote analysis and its probabilistic properties. We introduce a parameter space subdivision strategy that reduces the probability of vote peak omission, and show that it can be efficiently implemented in practice using the Gumbel probability distribution

    Speckle Detection in Ultrasonic Images Using Unsupervised Clustering Techniques

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    Research for the improvement of the quality of clinical ultrasound images has been a topic of interest for researchers and physicians. One of the challenges is the presence of speckle artifacts. This dissertation reviews the speckle phenomena in such images, and develops algorithms to better identify this artifact in sonographic images. Speckle artifact is categorized into two groups: partially developed speckles and fully developed speckles (FDS). This concept has been used, along with the classification techniques, to segment the ultrasound images into patches and classify the patches in the image as FDS or non-FDS. The proposed algorithms and the results of the experiments have been validated using simulation, phantom and real data that were created for the purposes of this study or taken from other research groups. Current speckle detection methods do not optimize statistical features and they are not based on machine learning techniques. For the first time this work introduces a novel method for searching and extracting the best features for optimizing speckle detection rate using statistical machine learning and ensemble classification. Potential applications include strain imaging by tracking speckle displacement, elastography, speckle tracking and suppression applications, and needle-tracking applications.Ph.D., Biomedical Engineering -- Drexel University, 201

    Mixed-reality visualization environments to facilitate ultrasound-guided vascular access

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    Ultrasound-guided needle insertions at the site of the internal jugular vein (IJV) are routinely performed to access the central venous system. Ultrasound-guided insertions maintain high rates of carotid artery puncture, as clinicians rely on 2D information to perform a 3D procedure. The limitations of 2D ultrasound-guidance motivated the research question: “Do 3D ultrasound-based environments improve IJV needle insertion accuracy”. We addressed this by developing advanced surgical navigation systems based on tracked surgical tools and ultrasound with various visualizations. The point-to-line ultrasound calibration enables the use of tracked ultrasound. We automated the fiducial localization required for this calibration method such that fiducials can be automatically localized within 0.25 mm of the manual equivalent. The point-to-line calibration obtained with both manual and automatic localizations produced average normalized distance errors less than 1.5 mm from point targets. Another calibration method was developed that registers an optical tracking system and the VIVE Pro head-mounted display (HMD) tracking system with sub-millimetre and sub-degree accuracy compared to ground truth values. This co-calibration enabled the development of an HMD needle navigation system, in which the calibrated ultrasound image and tracked models of the needle, needle trajectory, and probe were visualized in the HMD. In a phantom experiment, 31 clinicians had a 96 % success rate using the HMD system compared to 70 % for the ultrasound-only approach (p= 0.018). We developed a machine-learning-based vascular reconstruction pipeline that automatically returns accurate 3D reconstructions of the carotid artery and IJV given sequential tracked ultrasound images. This reconstruction pipeline was used to develop a surgical navigation system, where tracked models of the needle, needle trajectory, and the 3D z-buffered vasculature from a phantom were visualized in a common coordinate system on a screen. This system improved the insertion accuracy and resulted in 100 % success rates compared to 70 % under ultrasound-guidance (p=0.041) across 20 clinicians during the phantom experiment. Overall, accurate calibrations and machine learning algorithms enable the development of advanced 3D ultrasound systems for needle navigation, both in an immersive first-person perspective and on a screen, illustrating that 3D US environments outperformed 2D ultrasound-guidance used clinically

    Development of ultrasound to measure deformation of functional spinal units in cervical spine

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    Neck pain is a pervasive problem in the general population, especially in those working in vibrating environments, e.g. military troops and truck drivers. Previous studies showed neck pain was strongly associated with the degeneration of intervertebral disc, which is commonly caused by repetitive loading in the work place. Currently, there is no existing method to measure the in-vivo displacement and loading condition of cervical spine on the site. Therefore, there is little knowledge about the alternation of cervical spine functionality and biomechanics in dynamic environments. In this thesis, a portable ultrasound system was explored as a tool to measure the vertebral motion and functional spinal unit deformation. It is hypothesized that the time sequences of ultrasound imaging signals can be used to characterize the deformation of cervical spine functional spinal units in response to applied displacements and loading. Specifically, a multi-frame tracking algorithm is developed to measure the dynamic movement of vertebrae, which is validated in ex-vivo models. The planar kinematics of the functional spinal units is derived from a dual ultrasound system, which applies two ultrasound systems to image C-spine anteriorly and posteriorly. The kinematics is reconstructed from the results of the multi-frame movement tracking algorithm and a method to co-register ultrasound vertebrae images to MRI scan. Using the dual ultrasound, it is shown that the dynamic deformation of functional spinal unit is affected by the biomechanics properties of intervertebral disc ex-vivo and different applied loading in activities in-vivo. It is concluded that ultrasound is capable of measuring functional spinal units motion, which allows rapid in-vivo evaluation of C-spine in dynamic environments where X-Ray, CT or MRI cannot be used.2020-02-20T00:00:00

    Quantitative Analysis of Ultrasound Images of the Preterm Brain

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    In this PhD new algorithms are proposed to better understand and diagnose white matter damage in the preterm Brain. Since Ultrasound imaging is the most suited modality for the inspection of brain pathologies in very low birth weight infants we propose multiple techniques to assist in what is called Computer-Aided Diagnosis. As a main result we are able to increase the qualitative diagnosis from a 70% detectability to a 98% quantitative detectability
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