Multimodal interface for an intelligent wheelchair

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Automação). Faculdade de Engenharia. Universidade do Porto. 200

Deliverable D10.4.2

This deliverable describes the final status of Task 10.4 of Workpackage 10 of the euHeart project. The aim of this task is to develop a prototype of an endovascular simulator of cardiac radiofrequency ablation. More precisely, its purpose is to simulate the patient-specific catheter navigation and radiofre- quency ablation of ventricular tachycardia. Since deliverable 10.4.1, work on the simulator prototype has focused on the development of a user interface and the integration of two software compo- nents : endovascular simulation and electrophysiology simulation. The first component aims at modeling the deformation of catheters and guidewires inside vessels and to generate a realistic visualization of the vis- ible X-ray images. The second component is focused on the simulation of electrophysiology. We have chosen the Mitchell-Schaeffer phenomenological model to represent the evolution of action potential on the myocardium. The integration of those 2 software components is difficult because they should both run simultaneously in real-time. To this end, we have developed a multi-thread framework allowing to parallelize the computation of the catheter deformation and the cardiac electrophysiology while sharing a minimum num- ber of information. We have also developed a user interface that can display X-ray images, 3D view of the heart and simulated electro-physiology signals measured at the tip of the catheter. An example of simulation is provided starting from the endovascular navi- gation from the veina cava and finishing with the radiofrequency ablation of endocardial tissue inside the right ventricle

Service-oriented visualization applied to medical data analysis

With the era of Grid computing, data driven experiments and simulations have become very advanced and complicated. To allow specialists from various domains to deal with large datasets, aside from developing efficient extraction techniques, it is necessary to have available computational facilities to visualize and interact with the results of an extraction process. Having this in mind, we developed an Interactive Visualization Framework, which supports a service-oriented architecture. This framework allows, on one hand visualization experts to construct visualizations to view and interact with large datasets, and on the other hand end-users (e.g., medical specialists) to explore these visualizations irrespective of their geographical location and available computing resources. The image-based analysis of vascular disorders served as a case study for this project. The paper presents main research findings and reports on the current implementation status

Standardized Platform for Coregistration of Noncurrent Diffuse Optical and Magnetic Resonance Breast Images Obtained in Different Geometries

We present a novel methodology for combining breast image data obtained at different times, in different geometries, and by different techniques. We combine data based on diffuse optical tomography (DOT) and magnetic resonance imaging (MRI). The software platform integrates advanced multimodal registration and segmentation algorithms, requires minimal user experience, and employs computationally efficient techniques. The resulting superposed 3-D tomographs facilitate tissue analyses based on structural and functional data derived from both modalities, and readily permit enhancement of DOT data reconstruction using MRI-derived a-priori structural information. We demonstrate the multimodal registration method using a simulated phantom, and we present initial patient studies that confirm that tumorous regions in a patient breast found by both imaging modalities exhibit significantly higher total hemoglobin concentration (THC) than surrounding normal tissues. The average THC in the tumorous regions is one to three standard deviations larger than the overall breast average THC for all patients

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails