Parallel Processing in Web-Based Interactive Echocardiography Simulators

Medical simulation is a new method of education in medicine. It allows training medical students or practitioners without the need to involve patients and makes them familiar with various kinds of examinations, especially related to medical imaging. Simulators that visualize examinations or operations require large computing power to keep time constraints of output presentation. A common approach to this problem is to use graphics processing units (GPU), but the code is not portable. The method of parallelization of processing is more important in component environments, to allow calculating projections in real time. In this paper parallelization issues in the ultrasound view simulation based on provided computer tomography images are analyzed. The proposed domain decomposition for this problem leads to significant reduction in simulation time and allows obtaining an animated visualization for currently available personal computers with multicore processors. The use of a component environment makes the solution portable and makes it possible to implement a web-based application that is the basis for eTraining. The method for creating animation in real time for such solutions is also analyzed

Enabling technology for non-rigid registration during image-guided neurosurgery

In the context of image processing, non-rigid registration is an operation that attempts to align two or more images using spatially varying transformations. Non-rigid registration finds application in medical image processing to account for the deformations in the soft tissues of the imaged organs. During image-guided neurosurgery, non-rigid registration has the potential to assist in locating critical brain structures and improve identification of the tumor boundary. Robust non-rigid registration methods combine estimation of tissue displacement based on image intensities with the spatial regularization using biomechanical models of brain deformation. In practice, the use of such registration methods during neurosurgery is complicated by a number of issues: construction of the biomechanical model used in the registration from the image data, high computational demands of the application, and difficulties in assessing the registration results. In this dissertation we develop methods and tools that address some of these challenges, and provide components essential for the intra-operative application of a previously validated physics-based non-rigid registration method.;First, we study the problem of image-to-mesh conversion, which is required for constructing biomechanical model of the brain used during registration. We develop and analyze a number of methods suitable for solving this problem, and evaluate them using application-specific quantitative metrics. Second, we develop a high-performance implementation of the non-rigid registration algorithm and study the use of geographically distributed Grid resources for speculative registration computations. Using the high-performance implementation running on the remote computing resources we are able to deliver the results of registration within the time constraints of the neurosurgery. Finally, we present a method that estimates local alignment error between the two images of the same subject. We assess the utility of this method using multiple sources of ground truth to evaluate its potential to support speculative computations of non-rigid registration