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

Preoperative Systems for Computer Aided Diagnosis based on Image Registration: Applications to Breast Cancer and Atherosclerosis

Computer Aided Diagnosis (CAD) systems assist clinicians including radiologists and cardiologists to detect abnormalities and highlight conspicuous possible disease. Implementing a pre-operative CAD system contains a framework that accepts related technical as well as clinical parameters as input by analyzing the predefined method and demonstrates the prospective output. In this work we developed the Computer Aided Diagnostic System for biomedical imaging analysis of two applications on Breast Cancer and Atherosclerosis. The aim of the first CAD application is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user-independent. Base on the fact that automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time-temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. We implemented and evaluated 3 different 3D time-series registration methods: 1. Linear affine, 2. Non-linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70±0.03, 0.74±0.03 and 0.81±0.09 (out of 1) for Affine, BSpline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons registration method outperforms also with the best breast alignment and non-negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons registration as an effective technique for time-series dynamic infrared registration, to stabilize the local temperature oscillation. The aim of the second implemented CAD application is to assess contribution of calcification in plaque vulnerability and wall rupture and to find its maximum resistance before break in image-based models of carotid artery stenting. The role of calcification inside fibroatheroma during carotid artery stenting operation is controversial in which cardiologists face two major problems during the placement: (i) “plaque protrusion” (i.e. elastic fibrous caps containing early calcifications that penetrate inside the stent); (ii) “plaque vulnerability” (i.e. stiff plaques with advanced calcifications that break the arterial wall or stent). Finite Element Analysis was used to simulate the balloon and stent expansion as a preoperative patient-specific virtual framework. A nonlinear static structural analysis was performed on 20 patients acquired using in vivo MDCT angiography. The Agatston Calcium score was obtained for each patient and subject-specific local Elastic Modulus (EM) was calculated. The in silico results showed that by imposing average ultimate external load of 1.1MPa and 2.3MPa on balloon and stent respectively, average ultimate stress of 55.7±41.2kPa and 171±41.2kPa are obtained on calcifications. The study reveals that a significant positive correlation (R=0.85, p<0.0001) exists on stent expansion between EM of calcification and ultimate stress as well as Plaque Wall Stress (PWS) (R=0.92, p<0.0001), comparing to Ca score that showed insignificant associations with ultimate stress (R=0.44, p=0.057) and PWS (R=0.38, p=0.103), suggesting minor impact of Ca score in plaque rupture. These average data are in good agreement with results obtained by other research groups and we believe this approach enriches the arsenal of tools available for pre-operative prediction of carotid artery stenting procedure in the presence of calcified plaques

Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions

Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter

Task complexity analysis and QoS management for mapping dynamic video-processing tasks on a multi-core platform

This paper addresses efficient mapping and reconfiguration of advanced video applications onto a general purpose multi-core platform. By accurately modeling the resource usage for an application, allocation of processing resources on the platform can be based on the actually needed resources instead of a worst-case approach, thereby improving Quality-of-Service (QoS). Here, we exploit a new and strongly upcoming class of dynamic video applications based on image and content analysis for resource management and control. Such applications are characterized by irregular computing behavior and memory usage. It is shown that with linear models and statistical techniques based on the Markov modeling, a rather good accuracy (94–97%) for predicting the resource usage can be obtained. This prediction accuracy is so good that it allows resource prediction at runtime, thereby leading to an actively controlled system management

Cardiovascular imaging 2011 in the International Journal of Cardiovascular Imaging

Cardiovascular Aspects of Radiolog

3D shape instantiation for intra-operative navigation from a single 2D projection

Unlike traditional open surgery where surgeons can see the operation area clearly, in robot-assisted Minimally Invasive Surgery (MIS), a surgeon’s view of the region of interest is usually limited. Currently, 2D images from fluoroscopy, Magnetic Resonance Imaging (MRI), endoscopy or ultrasound are used for intra-operative guidance as real-time 3D volumetric acquisition is not always possible due to the acquisition speed or exposure constraints. 3D reconstruction, however, is key to navigation in complex in vivo geometries and can help resolve this issue. Novel 3D shape instantiation schemes are developed in this thesis, which can reconstruct the high-resolution 3D shape of a target from limited 2D views, especially a single 2D projection or slice. To achieve a complete and automatic 3D shape instantiation pipeline, segmentation schemes based on deep learning are also investigated. These include normalization schemes for training U-Nets and network architecture design of Atrous Convolutional Neural Networks (ACNNs). For U-Net normalization, four popular normalization methods are reviewed, then Instance-Layer Normalization (ILN) is proposed. It uses a sigmoid function to linearly weight the feature map after instance normalization and layer normalization, and cascades group normalization after the weighted feature map. Detailed validation results potentially demonstrate the practical advantages of the proposed ILN for effective and robust segmentation of different anatomies. For network architecture design in training Deep Convolutional Neural Networks (DCNNs), the newly proposed ACNN is compared to traditional U-Net where max-pooling and deconvolutional layers are essential. Only convolutional layers are used in the proposed ACNN with different atrous rates and it has been shown that the method is able to provide a fully-covered receptive field with a minimum number of atrous convolutional layers. ACNN enhances the robustness and generalizability of the analysis scheme by cascading multiple atrous blocks. Validation results have shown the proposed method achieves comparable results to the U-Net in terms of medical image segmentation, whilst reducing the trainable parameters, thus improving the convergence and real-time instantiation speed. For 3D shape instantiation of soft and deforming organs during MIS, Sparse Principle Component Analysis (SPCA) has been used to analyse a 3D Statistical Shape Model (SSM) and to determine the most informative scan plane. Synchronized 2D images are then scanned at the most informative scan plane and are expressed in a 2D SSM. Kernel Partial Least Square Regression (KPLSR) has been applied to learn the relationship between the 2D and 3D SSM. It has been shown that the KPLSR-learned model developed in this thesis is able to predict the intra-operative 3D target shape from a single 2D projection or slice, thus permitting real-time 3D navigation. Validation results have shown the intrinsic accuracy achieved and the potential clinical value of the technique. The proposed 3D shape instantiation scheme is further applied to intra-operative stent graft deployment for the robot-assisted treatment of aortic aneurysms. Mathematical modelling is first used to simulate the stent graft characteristics. This is then followed by the Robust Perspective-n-Point (RPnP) method to instantiate the 3D pose of fiducial markers of the graft. Here, Equally-weighted Focal U-Net is proposed with a cross-entropy and an additional focal loss function. Detailed validation has been performed on patient-specific stent grafts with an accuracy between 1-3mm. Finally, the relative merits and potential pitfalls of all the methods developed in this thesis are discussed, followed by potential future research directions and additional challenges that need to be tackled.Open Acces

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area

Medical image registration by neural networks: a regression-based registration approach

This thesis focuses on the development and evaluation of a registration-by-regression approach for the 3D/2D registration of coronary Computed Tomography Angiography (CTA) and X-ray angiography. This regression-based method relates image features of 2D projection images to the transformation parameters of the 3D image by a nonlinear regression. It treats registration as a regression problem, as an alternative for the traditional iterative approach that often comes with high computational costs and limited capture range. First we presented a survey of the methods with a regression-based registration approach for medical applications, as well as a summary of their main characteristics (Chapter 2). Second, we studied the registration methodology, addressing the input features and the choice of regression model (Chapter 3 and Chapter 4). For that purpose, we evaluated different options using simulated X-ray images generated from coronary artery tree models derived from 3D CTA scans. We also compared the registration-by-regression results with a method based on iterative optimization. Different image features of 2D projections and seven regression techniques were considered. The regression approach for simulated X-rays was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach. Neural Networks obtained accurate results and showed to be robust to large initial misalignment. Third, we evaluated the registration-by-regression method using clinical data, integrating the 3D preoperative CTA of the coronary arteries with intraoperative 2D X-ray angiography images (Chapter 5). For the evaluation of the image registration, a gold standard registration was established using an exhaustive search followed by a multi-observer visual scoring procedure. The influence of preprocessing options for the simulated images and the real X-rays was studied. Several image features were also compared. The coronary registration–by-regression results were not satisfactory, resembling manual initialization accuracy. Therefore, the proposed method for this concrete problem and in its current configuration is not sufficiently accurate to be used in the clinical practice. The framework developed enables us to better understand the dependency of the proposed method on the differences between simulated and real images. The main difficulty lies in the substantial differences in appearance between the images used for training (simulated X-rays from 3D coronary models) and the actual images obtained during the intervention (real X-ray angiography). We suggest alternative solutions and recommend to evaluate the registration-by-regression approach in other applications where training data is available that has similar appearance to the eventual test data