Intravascular Ultrasound

Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

3D-Ultrasound Based Mechanical and Geometrical Analysis of Abdominal Aortic Aneurysms and Relationship to Growth

The heterogeneity of progression of abdominal aortic aneurysms (AAAs) is not well understood. This study investigates which geometrical and mechanical factors, determined using time-resolved 3D ultrasound (3D + t US), correlate with increased growth of the aneurysm. The AAA diameter, volume, wall curvature, distensibility, and compliance in the maximal diameter region were determined automatically from 3D + t echograms of 167 patients. Due to limitations in the field-of-view and visibility of aortic pulsation, measurements of the volume, compliance of a 60 mm long region and the distensibility were possible for 78, 67, and 122 patients, respectively. Validation of the geometrical parameters with CT showed high similarity, with a median similarity index of 0.92 and root-mean-square error (RMSE) of diameters of 3.5 mm. Investigation of Spearman correlation between parameters showed that the elasticity of the aneurysms decreases slightly with diameter (p = 0.034) and decreases significantly with mean arterial pressure (p < 0.0001). The growth of a AAA is significantly related to its diameter, volume, compliance, and surface curvature (p < 0.002). Investigation of a linear growth model showed that compliance is the best predictor for upcoming AAA growth (RMSE 1.70 mm/year). To conclude, mechanical and geometrical parameters of the maximally dilated region of AAAs can automatically and accurately be determined from 3D + t echograms. With this, a prediction can be made about the upcoming AAA growth. This is a step towards more patient-specific characterization of AAAs, leading to better predictability of the progression of the disease and, eventually, improved clinical decision making about the treatment of AAAs

Deep learning for fast and robust medical image reconstruction and analysis

Medical imaging is an indispensable component of modern medical research as well as clinical practice. Nevertheless, imaging techniques such as magnetic resonance imaging (MRI) and computational tomography (CT) are costly and are less accessible to the majority of the world. To make medical devices more accessible, affordable and efficient, it is crucial to re-calibrate our current imaging paradigm for smarter imaging. In particular, as medical imaging techniques have highly structured forms in the way they acquire data, they provide us with an opportunity to optimise the imaging techniques holistically by leveraging data. The central theme of this thesis is to explore different opportunities where we can exploit data and deep learning to improve the way we extract information for better, faster and smarter imaging. This thesis explores three distinct problems. The first problem is the time-consuming nature of dynamic MR data acquisition and reconstruction. We propose deep learning methods for accelerated dynamic MR image reconstruction, resulting in up to 10-fold reduction in imaging time. The second problem is the redundancy in our current imaging pipeline. Traditionally, imaging pipeline treated acquisition, reconstruction and analysis as separate steps. However, we argue that one can approach them holistically and optimise the entire pipeline jointly for a specific target goal. To this end, we propose deep learning approaches for obtaining high fidelity cardiac MR segmentation directly from significantly undersampled data, greatly exceeding the undersampling limit for image reconstruction. The final part of this thesis tackles the problem of interpretability of the deep learning algorithms. We propose attention-models that can implicitly focus on salient regions in an image to improve accuracy for ultrasound scan plane detection and CT segmentation. More crucially, these models can provide explainability, which is a crucial stepping stone for the harmonisation of smart imaging and current clinical practice.Open Acces

Estimating and understanding motion : from diagnostic to robotic surgery

Estimating and understanding motion from an image sequence is a central topic in computer vision. The high interest in this topic is because we are living in a world where many events that occur in the environment are dynamic. This makes motion estimation and understanding a natural component and a key factor in a widespread of applications including object recognition , 3D shape reconstruction, autonomous navigation and medica! diagnosis. Particularly, we focus on the medical domain in which understanding the human body for clinical purposes requires retrieving the organs' complex motion patterns, which is in general a hard problem when using only image data. In this thesis, we cope with this problem by posing the question - How to achieve a realistic motion estimation to offer a better clinical understanding? We focus this thesis on answering this question by using a variational formulation as a basis to understand one of the most complex motions in the human's body, the heart motion, through three different applications: (i) cardiac motion estimation for diagnostic, (ii) force estimation and (iii) motion prediction, both for robotic surgery. Firstly, we focus on a central topic in cardiac imaging that is the estimation of the cardiac motion. The main aim is to offer objective and understandable measures to physicians for helping them in the diagnostic of cardiovascular diseases. We employ ultrafast ultrasound data and tools for imaging motion drawn from diverse areas such as low-rank analysis and variational deformation to perform a realistic cardiac motion estimation. The significance is that by taking low-rank data with carefully chosen penalization, synergies in this complex variational problem can be created. We demonstrate how our proposed solution deals with complex deformations through careful numerical experiments using realistic and simulated data. We then move from diagnostic to robotic surgeries where surgeons perform delicate procedures remotely through robotic manipulators without directly interacting with the patients. As a result, they lack force feedback, which is an important primary sense for increasing surgeon-patient transparency and avoiding injuries and high mental workload. To solve this problem, we follow the conservation principies of continuum mechanics in which it is clear that the change in shape of an elastic object is directly proportional to the force applied. Thus, we create a variational framework to acquire the deformation that the tissues undergo due to an applied force. Then, this information is used in a learning system to find the nonlinear relationship between the given data and the applied force. We carried out experiments with in-vivo and ex-vivo data and combined statistical, graphical and perceptual analyses to demonstrate the strength of our solution. Finally, we explore robotic cardiac surgery, which allows carrying out complex procedures including Off-Pump Coronary Artery Bypass Grafting (OPCABG). This procedure avoids the associated complications of using Cardiopulmonary Bypass (CPB) since the heart is not arrested while performing the surgery on a beating heart. Thus, surgeons have to deal with a dynamic target that compromisetheir dexterity and the surgery's precision. To compensate the heart motion, we propase a solution composed of three elements: an energy function to estimate the 3D heart motion, a specular highlight detection strategy and a prediction approach for increasing the robustness of the solution. We conduct evaluation of our solution using phantom and realistic datasets. We conclude the thesis by reporting our findings on these three applications and highlight the dependency between motion estimation and motion understanding at any dynamic event, particularly in clinical scenarios.L’estimació i comprensió del moviment dins d’una seqüència d’imatges és un tema central en la visió per ordinador, el que genera un gran interès perquè vivim en un entorn ple d’esdeveniments dinàmics. Per aquest motiu és considerat com un component natural i factor clau dins d’un ampli ventall d’aplicacions, el qual inclou el reconeixement d’objectes, la reconstrucció de formes tridimensionals, la navegació autònoma i el diagnòstic de malalties. En particular, ens situem en l’àmbit mèdic en el qual la comprensió del cos humà, amb finalitats clíniques, requereix l’obtenció de patrons complexos de moviment dels òrgans. Aquesta és, en general, una tasca difícil quan s’utilitzen només dades de tipus visual. En aquesta tesi afrontem el problema plantejant-nos la pregunta - Com es pot aconseguir una estimació realista del moviment amb l’objectiu d’oferir una millor comprensió clínica? La tesi se centra en la resposta mitjançant l’ús d’una formulació variacional com a base per entendre un dels moviments més complexos del cos humà, el del cor, a través de tres aplicacions: (i) estimació del moviment cardíac per al diagnòstic, (ii) estimació de forces i (iii) predicció del moviment, orientant-se les dues últimes en cirurgia robòtica. En primer lloc, ens centrem en un tema principal en la imatge cardíaca, que és l’estimació del moviment cardíac. L’objectiu principal és oferir als metges mesures objectives i comprensibles per ajudar-los en el diagnòstic de les malalties cardiovasculars. Fem servir dades d’ultrasons ultraràpids i eines per al moviment d’imatges procedents de diverses àrees, com ara l’anàlisi de baix rang i la deformació variacional, per fer una estimació realista del moviment cardíac. La importància rau en que, en prendre les dades de baix rang amb una penalització acurada, es poden crear sinergies en aquest problema variacional complex. Mitjançant acurats experiments numèrics, amb dades realístiques i simulades, hem demostrat com les nostres propostes solucionen deformacions complexes. Després passem del diagnòstic a la cirurgia robòtica, on els cirurgians realitzen procediments delicats remotament, a través de manipuladors robòtics, sense interactuar directament amb els pacients. Com a conseqüència, no tenen la percepció de la força com a resposta, que és un sentit primari important per augmentar la transparència entre el cirurgià i el pacient, per evitar lesions i per reduir la càrrega de treball mental. Resolem aquest problema seguint els principis de conservació de la mecànica del medi continu, en els quals està clar que el canvi en la forma d’un objecte elàstic és directament proporcional a la força aplicada. Per això hem creat un marc variacional que adquireix la deformació que pateixen els teixits per l’aplicació d’una força. Aquesta informació s’utilitza en un sistema d’aprenentatge, per trobar la relació no lineal entre les dades donades i la força aplicada. Hem dut a terme experiments amb dades in-vivo i ex-vivo i hem combinat l’anàlisi estadístic, gràfic i de percepció que demostren la robustesa de la nostra solució. Finalment, explorem la cirurgia cardíaca robòtica, la qual cosa permet realitzar procediments complexos, incloent la cirurgia coronària sense bomba (off-pump coronary artery bypass grafting o OPCAB). Aquest procediment evita les complicacions associades a l’ús de circulació extracorpòria (Cardiopulmonary Bypass o CPB), ja que el cor no s’atura mentre es realitza la cirurgia. Això comporta que els cirurgians han de tractar amb un objectiu dinàmic que compromet la seva destresa i la precisió de la cirurgia. Per compensar el moviment del cor, proposem una solució composta de tres elements: un funcional d’energia per estimar el moviment tridimensional del cor, una estratègia de detecció de les reflexions especulars i una aproximació basada en mètodes de predicció, per tal d’augmentar la robustesa de la solució. L’avaluació de la nostra solució s’ha dut a terme mitjançant conjunts de dades sintètiques i realistes. La tesi conclou informant dels nostres resultats en aquestes tres aplicacions i posant de relleu la dependència entre l’estimació i la comprensió del moviment en qualsevol esdeveniment dinàmic, especialment en escenaris clínics.Postprint (published version

Strain ultrasound elastography of aneurysm sac content after randomized endoleak embolization with sclerosing and non-sclerosing chitosan-based hydrogels in a preclinical model

Mise en contexte : La réparation endovasculaire des anévrismes de l’aorte abdominale est limitée par le développement des endofuites, qui nécessite un suivi à long terme par imagerie. L’élastographie sonore de déformation a été proposée comme méthode complémentaire pour aider à la détection des endofuites et la caractérisation des propriétés mécaniques des anévrismes. On s’intéresse ici également à la possibilité de suivre l’embolisation des endofuites, qui est indiquée dans certains cas mais dont le succès est variable. Un nouvel agent d’embolisation a été récemment créé en combinant un hydrogel de chitosane radio-opaque (CH) et le sclérosant tetradecyl sulfate de sodium (STS), qui s’appelle CH-STS. Le CH-STS démontre des propriétés mécaniques in vitro favorables, mais son comportement in vivo et son effet sur l’évolution du sac par rapport à un agent non-sclérosant pourraient être mieux caractérisés. L’objectif de cette étude était la caractérisation des propriétés mécaniques des composantes des endofuites embolisées avec CH-STS et CH avec élastographie sonore de déformation. Méthodologie : Des anévrismes bilatéraux avec endofuites de type I ont été créés au niveau des artères iliaques communes chez neuf chiens. Chez chaque sujet, une endofuite a été embolisée avec CH, et l’autre, avec CH-STS, d’une façon aléatoire et aveugle. Des images d’échographie duplex et des cinéloops pour élastographie sonore de déformation ont été acquis à 1 semaine, 1 mois, 3 mois et (chez 3 sujets) 6 mois post-embolisation. La tomodensitométrie a été faite à 3 mois et (si pertinente) 6 mois post-embolisation. L’histopathologie a été faite au sacrifice. Les études radiologiques et les données d’histopathologie ont été co-enregistrées pour définir trois régions d’intérêt sur les cinéloops : l’agent d’embolisation (au sacrifice), le thrombus intraluminal (au sacrifice) et le sac anévrismal (pendant chaque suivi). L’élastographie sonore de déformation a été faite avec les segmentations par deux observateurs indépendants. La déformation axiale maximale (DAM) a été le critère d’évaluation principal. Les analyses statistiques ont été faites avec des modèles mixtes linéaires généralisés et des coefficients de corrélations intraclasses (ICCs). Résultats : Des endofuites résiduelles ont été trouvées dans 7/9 (77.8%) et 4/9 (44.4%) des anévrismes embolisés avec CH et CH-STS, respectivement. Le CH-STS a eu une DAM 66 % plus basse (p < 0.001) que le CH. Le thrombus a eu une DAM 37% plus basse (p = 0.010) que le CH et 77% plus élevée (p = 0.079) que le CH-STS. Il n’y avait aucune différence entre les thrombi associés avec les deux traitements. Les sacs anévrismaux embolisés avec CH-STS ont eu une DAM 29% plus basse (p < 0.001) que ceux embolisés avec CH. Des endofuites résiduelles ont été associées avec une DAM du sac anévrismal 53% plus élevée (p < 0.001). Le ICC pour la DAM a été de 0.807 entre les deux segmentations. Conclusion : Le CH-STS confère des valeurs de déformations plus basses aux anévrismes embolisés. Les endofuites persistantes sont associées avec des déformations plus élevées du sac anévrismal.Background: Endovascular aneurysm repair (EVAR) is the modality of choice for the treatment of abdominal aortic aneurysms (AAAs). EVAR is limited by the development of endoleaks, which necessitate long-term imaging follow-up. Conventional follow-up modalities suffer from unique limitations. Strain ultrasound elastography (SUE) has been recently proposed as an imaging adjunct to detect endoleaks and to characterize aneurysm mechanical properties. Once detected, certain endoleaks may be treated with embolization; however, success is limited. In this context, the embolic agent CH-STS—containing a chitosan hydrogel and the sclerosant sodium tetradecyl sulphate (STS)—was created. CH-STS demonstrates favorable mechanical properties in vitro; however, its behavior in vivo and impact on sac evolution compared to a non-sclerosing chitosan-based embolic agent (CH) merit further characterization. Purpose: To compare the mechanical properties of the constituents of endoleaks embolized with CH and CH-STS—including the agent, the intraluminal thrombus (ILT), and the overall sac—via SUE. Methods: Bilateral common iliac artery aneurysms with type I endoleaks were created in nine dogs. In each animal, one endoleak was randomly embolized with CH, and the other with CH-STS. Duplex ultrasound (DUS) and radiofrequency cine loops were acquired at 1 week, 1 month, 3 months, and—in 3 subjects—6 months post-embolization. Contrast-enhanced CT was performed at 3 months and—where applicable—6 months post-embolization. Histopathological analysis was performed at time of sacrifice. Radiological studies and histopathological slides were co-registered to identify three regions of interest (ROIs) on the cine loops: embolic agent (at sacrifice), ILT (at sacrifice), and aneurysm sac (at all follow-up times). SUE was performed using segmentations from two independent observers on the cine loops. Maximum axial deformation (MAD) was the main outcome. Statistical analysis was performed using general linear mixed models and intraclass correlation coefficients (ICCs). Results: Residual endoleaks were identified in 7/9 (77.8%) and 4/9 (44.4%) aneurysms embolized with CH and CH-STS, respectively. CH-STS had a 66 % lower MAD (p < 0.001) than CH. The ILT had a 37% lower MAD (p = 0.010) than CH and a 77% greater MAD (p = 0.079; trending towards significance) than CH-STS. There was no difference in the ILT between treatment groups. Aneurysm sacs embolized with CH-STS had a 29% lower MAD (p < 0.001) than those with CH. Residual endoleak increased MAD of the aneurysm sac by 53% (p < 0.001), regardless of the agent used. The ICC for MAD was 0.807 between readers’ segmentations. Conclusion: CH-STS confers lower strain values to embolized aneurysms. Persistent endoleaks result are associated with increased sac strain, which may be useful for clinical follow-up

A non-invasive image based system for early diagnosis of prostate cancer.

Prostate cancer is the second most fatal cancer experienced by American males. The average American male has a 16.15% chance of developing prostate cancer, which is 8.38% higher than lung cancer, the second most likely cancer. The current in-vitro techniques that are based on analyzing a patients blood and urine have several limitations concerning their accuracy. In addition, the prostate Specific Antigen (PSA) blood-based test, has a high chance of false positive diagnosis, ranging from 28%-58%. Yet, biopsy remains the gold standard for the assessment of prostate cancer, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The major limitation of the relatively small needle biopsy samples is the higher possibility of producing false positive diagnosis. Moreover, the visual inspection system (e.g., Gleason grading system) is not quantitative technique and different observers may classify a sample differently, leading to discrepancies in the diagnosis. As reported in the literature that the early detection of prostate cancer is a crucial step for decreasing prostate cancer related deaths. Thus, there is an urgent need for developing objective, non-invasive image based technology for early detection of prostate cancer. The objective of this dissertation is to develop a computer vision methodology, later translated into a clinically usable software tool, which can improve sensitivity and specificity of early prostate cancer diagnosis based on the well-known hypothesis that malignant tumors are will connected with the blood vessels than the benign tumors. Therefore, using either Diffusion Weighted Magnetic Resonance imaging (DW-MRI) or Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), we will be able to interrelate the amount of blood in the detected prostate tumors by estimating either the Apparent Diffusion Coefficient (ADC) in the prostate with the malignancy of the prostate tumor or perfusion parameters. We intend to validate this hypothesis by demonstrating that automatic segmentation of the prostate from either DW-MRI or DCE-MRI after handling its local motion, provides discriminatory features for early prostate cancer diagnosis. The proposed CAD system consists of three majors components, the first two of which constitute new research contributions to a challenging computer vision problem. The three main components are: (1) A novel Shape-based segmentation approach to segment the prostate from either low contrast DW-MRI or DCE-MRI data; (2) A novel iso-contours-based non-rigid registration approach to ensure that we have voxel-on-voxel matches of all data which may be more difficult due to gross patient motion, transmitted respiratory effects, and intrinsic and transmitted pulsatile effects; and (3) Probabilistic models for the estimated diffusion and perfusion features for both malignant and benign tumors. Our results showed a 98% classification accuracy using Leave-One-Subject-Out (LOSO) approach based on the estimated ADC for 30 patients (12 patients diagnosed as malignant; 18 diagnosed as benign). These results show the promise of the proposed image-based diagnostic technique as a supplement to current technologies for diagnosing prostate cancer