Reza Razavi

Personalized Computational Models of the Heart for Cardiac Resynchronization Therapy

Cardiovascular diseases (CVD) are the major cause of morbidity and mortality in the western world. Within CVD, the increasing prevalence of congestive heart failure (CHF) is mainly caused by the steadily increasing number of heart attack survivors. They suffer an important scar burden on their cardiac function due to the infarction. Moreover, CHF has a terrible prognosis with 50% mortality in the first 3 years after diagnosis. Of all CHF patients, those with an additional dyssynchronous contraction have the worst prognosis. Cardiac resynchronization therapy (CRT) involves placing a pacemaker to improve the synchronicity of cardiac contraction. It has recently been shown to be an effective method of treating patients with dyssynchronous CHF, inducing significant reductions in morbidity and mortality in large clinical trials. However, clinical trials have also demonstrated that up to 30% of patients may be classified as nonresponders. There remains major controversy surrounding patient selection and optimization of this expensive treatment (e.g., lead positioning, pacemaker setting). For instance, recent studies showed that patients with heart failure and narrow QRS intervals do not currently benefit from CRT (RethinQ, [3]) and that no single echocardiographic measure of dyssynchrony may be recommended to improve patient selection (PROSPECT, [10]). Therefore, new approaches are needed in order to provide a better diagnosis and characterization of patients while achieving a better planning and delivery of the therapy.

CARDIOSENSE3D : PATIENT-SPECIFIC CARDIAC SIMULATION

In this paper, we overview the objectives and achievements of the CardioSense3D project dedicated to the construction of an electro-mechanical model of the heart.

Pediatric Interventional Cardiovascular Magnetic Resonance

Abstract Magnetic resonance image guidance for cardiovascular interventions has grown in recent years from a research tool to translation into clinical practice. The scope of improved visualization of cardiovascular anatomy, tissue characterization, physiologic information, and reduced ionizing radiation from cardiovascular magnetic resonance (CMR) is attractive compared to conventional fluoroscopic cardiac catheterization techniques. This is particularly relevant to a pediatric population who are more at risk from radiation, and more likely to need repeat interventions in the future. Industry support has increased the development of interventional CMR systems. New generation scanners allow for faster scanning protocols, which translate into improved catheter visualization and tracking. The platforms for interacting with these images are also more intuitive. CMR conditional hardware in the form of catheters and guidewires is more available, although more work is still needed in this area. These advances are balanced with the need to maintain safety issues in a CMR environment and any risks from heating. While there is some limitation in the available hardware options for sole CMR guidance, x-ray fused with CMR allows physiologic testing in the assessment of pulmonary avascular resistance as an example. There is now a growing body of early human experience or interventional cases, electrophysiology testing, and radiofrequency ablation in the CMR environment. These are discussed in the chapter.

Time-varying Image Data Visualization Framework for Application in Cardiac Catheterization Procedures

Visualization plays an important role in image guided surgery. This paper presents a real-time 3D motion visualization method where pre-computed meshes of the beating heart are synchronized with and overlaid onto live X-ray images. This provides the surgeon with a navigational aid in guiding catheters during cardiac catheterization. In order to generate time-varying meshes of the beating heart, we first acquire a time-series of images of the patient using Magnetic Resonance Imaging (MRI). The MRI heart images used for the cardiac catheterization procedures can either be contrast-enhanced by injecting a contrast agent prior to imaging or they can be unenhanced. The contrast-enhanced images can easily be segmented and binarized using a fixed grey-level threshold. In this case, we can use an adaptive Delaunay-based surface extraction algorithm for mesh generation, for which specifically developed for noisy binary image data sets. For unenhanced images, we have to choose a semi-automated segmentation approach, where a region of interest in the patients heart is outlined manually in an intermediate slice in the 3-D MRI data set and then propagated to neighbouring slices. In a next step, the extracted snake contours are propagated in time from the first phase of the cardiac cycle to subsequent phases using multiple snake contours. In this scenario, the final mesh is generated using a serial section reconstruction algorithm. However, due to the nature of the underlyling MRI images which frequently contain areas of inhomogenous contrast caused by motion and blood flow, it is difficult to generate a smooth mesh directly from the result of the previously described semi-automatic segmentation procedure. Therefore, we also introduce a contour-based mesh smoothing algorithm using a 1D Gaussian filter in order to post-process the snake contours along the series of cross-sections before reconstruction.