3-D Registration on Carotid Artery imaging data: MRI for different timesteps

A common problem which is faced by the researchers when dealing with arterial carotid imaging data is the registration of the geometrical structures between different imaging modalities or different timesteps. The use of the "Patient Position" DICOM field is not adequate to achieve accurate results due to the fact that the carotid artery is a relatively small structure and even imperceptible changes in patient position and/or direction make it difficult. While there is a wide range of simple/advanced registration techniques in the literature, there is a considerable number of studies which address the geometrical structure of the carotid artery without using any registration technique. On the other hand the existence of various registration techniques prohibits an objective comparison of the results using different registration techniques. In this paper we present a method for estimating the statistical significance that the choice of the registration technique has on the carotid geometry. One-Way Analysis of Variance(ANOVA) showed that the p-values were <0.0001 for the distances of the lumen from the centerline for both right and left carotids of the patient case that was studied.Comment: 4 pages, 4 figures, 1 table, preprint submitted to IEEE-EMBC 201

Biologically Inspired Near Extinct System Reconstruction

Recovery software system operations from a state of extensive damage without human intervention is a challenging problem as it may need to be based on a different infrastructure from the one that the system was originally designed for and deployed on (i.e., computational and communication devices) and significant reorganization of system functionalities. In this paper, we introduce a bio-inspired approach for reconstructing nearly extinct complex software systems. Our approach is based on encoding a computational DNA (co-DNA) of a system and computational analogues of biological processes to enable the transmission of co-DNA over computational devices and, through it, the transformation of these devices into system cells that can realise chunks of the system functionality, and spread further its reconstruction process

ARTreat Project: Three-Dimensional Numerical Simulation of Plaque Formation and Development in the Arteries

Atherosclerosis is a progressive disease characterized by the accumulation of lipids and fibrous elements in arteries. It is characterized by dysfunction of endothelium and vasculitis, and accumulation of lipid, cholesterol, and cell elements inside blood vessel wall. In this study, a continuum-based approach for plaque formation and development in 3-D is presented. The blood flow is simulated by the 3-D Navier-Stokes equations, together with the continuity equation while low-density lipoprotein (LDL) transport in lumen of the vessel is coupled with Kedem-Katchalsky equations. The inflammatory process was solved using three additional reaction-diffusion partial differential equations. Transport of labeled LDL was fitted with our experiment on the rabbit animal model. Matching with histological data for LDL localization was achieved. Also, 3-D model of the straight artery with initial mild constriction of 30% plaque for formation and development is presented

Mining balance disorders' data for the development of diagnostic decision support systems

In this work we present the methodology for the development of the EMBalance diagnostic Decision Support System (DSS) for balance disorders. Medical data from patients with balance disorders have been analysed using data mining techniques for the development of the diagnostic DSS. The proposed methodology uses various data, ranging from demographic characteristics to clinical examination, auditory and vestibular tests, in order to provide an accurate diagnosis. The system aims to provide decision support for general practitioners (GPs) and experts in the diagnosis of balance disorders as well as to provide recommendations for the appropriate information and data to be requested at each step of the diagnostic process. Detailed results are provided for the diagnosis of 12 balance disorders, both for GPs and experts. Overall, the reported accuracy ranges from 59.3 to 89.8% for GPs and from 74.3 to 92.1% for experts

The native architecture of a photosynthetic membrane

In photosynthesis, the harvesting of solar energy and its subsequent conversion into a stable charge separation are dependent upon an interconnected macromolecular network of membrane-associated chlorophyll–protein complexes. Although the detailed structure of each complex has been determined, the size and organization of this network are unknown. Here we show the use of atomic force microscopy to directly reveal a native bacterial photosynthetic membrane. This first view of any multi-component membrane shows the relative positions and associations of the photosynthetic complexes and reveals crucial new features of the organization of the network: we found that the membrane is divided into specialized domains each with a different network organization and in which one type of complex predominates. Two types of organization were found for the peripheral light-harvesting LH2 complex. In the first, groups of 10–20 molecules of LH2 form light-capture domains that interconnect linear arrays of dimers of core reaction centre (RC)–light-harvesting 1 (RC–LH1–PufX) complexes; in the second they were found outside these arrays in larger clusters. The LH1 complex is ideally positioned to function as an energy collection hub, temporarily storing it before transfer to the RC where photochemistry occurs: the elegant economy of the photosynthetic membrane is demonstrated by the close packing of these linear arrays, which are often only separated by narrow 'energy conduits' of LH2 just two or three complexes wide

Artificial Neural Networks for Solving Ordinary and Partial Differential Equations

We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the boundary (or initial) conditions and contains no adjustable parameters. The second part is constructed so as not to affect the boundary conditions. This part involves a feedforward neural network, containing adjustable parameters (the weights). Hence by construction the boundary conditions are satisfied and the network is trained to satisfy the differential equation. The applicability of this approach ranges from single ODE's, to systems of coupled ODE's and also to PDE's. In this article we illustrate the method by solving a variety of model problems and present comparisons with finite elements for several cases of partial differential equations.Comment: LAtex file, 26 pages, 21 figs, submitted to IEEE TN

Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks

The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%

The preferences of participants in small-scale sport events: A conjoint analysis case study from Taiwan

The primary objective of this study was an investigation of participants\u27 preferences for a cycling orientated sport tourism event using conjoint analysis. Respondents in a survey were presented with a range of different event alternatives related to the characteristics of proposed small-scale cycling events to draw out useful conclusions about the ideal scenario of such a sporting event that would be the most attractive and desirable for those who compete. A questionnaire, in two parts, was developed and distributed to 195 bicyclers during an event in Kaohsiung, Taiwan and the data was analysed using SPSS Conjoint at the aggregate level (pooled data). Based on the preferences expressed by the athletes the three most important factors were: "preferred season to organizing the event", "parallel organised trade shows & exhibitions" and "entertainment & awards". The findings of this study provide event coordinators and sport marketers with practical insights into event planning and possibility of development of effective marketing strategies designed to reach and attract more participants to these types of activities. This investigation is unique since is one of the first to use a full design of seven parameters in the conjoint analysis model to comprehensively examine athlete\u27s preferences