Graphics Processor Unit Based Parallelization of Optimized Baseline Wander Filtering Algorithms for Long-term Electrocardiography

Long-term electrocardiogram (ECG) often suffers from relevant noise. Baseline wander in particular is pronounced in ECG recordings using dry or esophageal electrodes, which are dedicated for prolonged registration. While analog high-pass filters introduce phase distortions, reliable offline filtering of the baseline wander implies a computational burden that has to be put in relation to the increase in signal-to-baseline ratio (SBR). Here we present a graphics processor unit (GPU) based parallelization method to speed up offline baseline wander filter algorithms, namely the wavelet, finite, and infinite impulse response, moving mean, and moving median filter. Individual filter parameters were optimized with respect to the SBR increase based on ECGs from the Physionet database superimposed to auto-regressive modeled, real baseline wander. A Monte-Carlo simulation showed that for low input SBR the moving median filter outperforms any other method but negatively affects ECG wave detection. In contrast, the infinite impulse response filter is preferred in case of high input SBR. However, the parallelized wavelet filter is processed 500 and 4 times faster than these two algorithms on the GPU, respectively, and offers superior baseline wander suppression in low SBR situations. Using a signal segment of 64 mega samples that is filtered as entire unit, wavelet filtering of a 7-day high-resolution ECG is computed within less than 3 seconds. Taking the high filtering speed into account, the GPU wavelet filter is the most efficient method to remove baseline wander present in long-term ECGs, with which computational burden can be strongly reduced.

BOLD correlates of continuously fluctuating epileptic activity isolated by independent component analysis

Combined EEG/fMRI recordings offer a promising opportunity to detect brain areas with altered BOLD signal during interictal epileptic discharges (IEDs). These areas are likely to represent the irritative zone, which is itself a reflection of the epileptogenic zone. This paper reports on the imaging findings using independent component analysis (ICA) to continuously quantify epileptiform activity in simultaneously acquired EEG and fMRI. Using ICA derived factors coding for the epileptic activity takes into account that epileptic activity is continuously fluctuating with each spike differing in amplitude, duration and maybe topography, including subthreshold epileptic activity besides clear IEDs and may thus increase the sensitivity and statistical power of combined EEG/fMRI in epilepsy. Twenty patients with different types of focal and generalized epilepsy syndromes were investigated. ICA separated epileptiform activity from normal physiological brain activity and artifacts. In 16/20 patients, BOLD correlates of epileptic activity matched the EEG sources, the clinical semiology, and, if present, the structural lesions. In clinically equivocal cases, the BOLD correlates aided to attribute proper diagnosis of the underlying epilepsy syndrome. Furthermore, in one patient with temporal lobe epilepsy, BOLD correlates of rhythmic delta activity could be employed to delineate the affected hippocampus. Compared to BOLD correlates of manually identified IEDs, the sensitivity was improved from 50% (10/20) to 80%. The ICA EEG/fMRI approach is a safe, non-invasive and easily applicable technique, which can be used to identify regions with altered hemodynamic effects related to IEDs as well as intermittent rhythmic discharges in different types of epilepsy.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).

Atlas of the Songwe River Basin: Thematic Maps with Explanatory Text Sheets

This atlas presents a comprehensive set of thematic maps depicting a wide range of aspects of the Songwe river area. It includes baseline maps (such as topographic overview, hillshade), satellite images (years 1991, 2001, 2004), land cover and land cover change, biomass and biomass change, priority conservation areas, resource management (watershed classification, watershed classification combined with biomass, soil erosion), accessibility and special maps (such as historical river course). Map clippings of the most important maps facilitate the readability of the maps. The accompanying explanatory text sheets contain graphics and information about material, methods, results and interpretation.

Medical image computing in diagnosis and intervention of spinal diseases

Spinal image analysis and computer assisted intervention have emerged as new and independent research areas, due to the importance of treatment of spinal diseases, increasing availability of spinal imaging, and advances in analytics and navigation tools. Among others, multiple modality spinal image analysis and spinal navigation tools have emerged as two keys in this new area. We believe that further focused research in these two areas will lead to a much more efficient and accelerated research path, avoiding detours that exist in other applications, such as in brain and heart.

Evolution of mylonitic microfabrics (EMM), a computer application for educational purposes

`Evolution of mylonitic microfabrics' (EMM) is an interactive Filemaker Pro 3.0 application that documents a series of see-through deformation experiments on polycrystalline norcamphor. The application comprises computer animations, graphics and text explanations designed to give students and researchers insight into the interaction and dynamic nature of small-scale, mylonitic processes like intracrystalline glide, dynamic recrystallization and strain localization (microshearing). EMM shows how mylonitic steady state is achieved at different strain rates and temperatures. First, rotational mechanisms like glide-induced vorticity, subgrain rotation recrystallization and rigid-body rotation bring grains' crystal lattices into orientations that are favorable for intracrystalline glide. In a second stage, selective elimination of grains whose lattices are poorly oriented for glide involves grain boundary migration. This strengthens the texture. Temperature and strain rate affect both the relative activity of different strain accommodation mechanisms and the rate of microfabric change. Steady-state microfabrics are characterized by stable texture, grain size and shape-preferred orientations of grains and domains. This involves the cyclical generation and elimination of dynamically recrystallized grains and microshear zones.