Cardiovascular imaging 2010 in the International Journal of Cardiovascular Imaging

Cardiovascular Aspects of Radiolog

Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography

Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality for the visualization of the heart and coronary arteries. To fully exploit the potential of the CCTA datasets and apply it in clinical practice, an automated coronary artery extraction approach is needed. The purpose of this paper is to present and validate a fully automatic centerline extraction algorithm for coronary arteries in CCTA images. The algorithm is based on an improved version of Frangi’s vesselness filter which removes unwanted step-edge responses at the boundaries of the cardiac chambers. Building upon this new vesselness filter, the coronary artery extraction pipeline extracts the centerlines of main branches as well as side-branches automatically. This algorithm was first evaluated with a standardized evaluation framework named Rotterdam Coronary Artery Algorithm Evaluation Framework used in the MICCAI Coronary Artery Tracking challenge 2008 (CAT08). It includes 128 reference centerlines which were manually delineated. The average overlap and accuracy measures of our method were 93.7% and 0.30 mm, respectively, which ranked at the 1st and 3rd place compared to five other automatic methods presented in the CAT08. Secondly, in 50 clinical datasets, a total of 100 reference centerlines were generated from lumen contours in the transversal planes which were manually corrected by an expert from the cardiology department. In this evaluation, the average overlap and accuracy were 96.1% and 0.33 mm, respectively. The entire processing time for one dataset is less than 2 min on a standard desktop computer. In conclusion, our newly developed automatic approach can extract coronary arteries in CCTA images with excellent performances in extraction ability and accuracy

Identification of Zoonotic Genotypes of Giardia duodenalis

Giardia duodenalis, originally regarded as a commensal organism, is the etiologic agent of giardiasis, a gastrointestinal disease of humans and animals. Giardiasis causes major public and veterinary health concerns worldwide. Transmission is either direct, through the faecal-oral route, or indirect, through ingestion of contaminated water or food. Genetic characterization of G. duodenalis isolates has revealed the existence of seven groups (assemblages A to G) which differ in their host distribution. Assemblages A and B are found in humans and in many other mammals, but the role of animals in the epidemiology of human infection is still unclear, despite the fact that the zoonotic potential of Giardia was recognised by the WHO some 30 years ago. Here, we performed an extensive genetic characterization of 978 human and 1440 animal isolates, which together comprise 3886 sequences from 4 genetic loci. The data were assembled into a molecular epidemiological database developed by a European network of public and veterinary health Institutions. Genotyping was performed at different levels of resolution (single and multiple loci on the same dataset). The zoonotic potential of both assemblages A and B is evident when studied at the level of assemblages, sub-assemblages, and even at each single locus. However, when genotypes are defined using a multi-locus sequence typing scheme, only 2 multi-locus genotypes (MLG) of assemblage A and none of assemblage B appear to have a zoonotic potential. Surprisingly, mixtures of genotypes in individual isolates were repeatedly observed. Possible explanations are the uptake of genetically different Giardia cysts by a host, or subsequent infection of an already infected host, likely without overt symptoms, with a different Giardia species, which may cause disease. Other explanations for mixed genotypes, particularly for assemblage B, are substantial allelic sequence heterogeneity and/or genetic recombination. Although the zoonotic potential of G. duodenalis is evident, evidence on the contribution and frequency is (still) lacking. This newly developed molecular database has the potential to tackle intricate epidemiological questions concerning protozoan diseases

Statistical model of rough surface contact accounting for size-dependent plasticity and asperity interaction

The work by Greenwood and Williamson (GW) has initiated a simple but effective method of contact mechanics: statistical modeling based on the mechanical response of a single asperity. Two main assumptions of the original GW model are that the asperity response is purely elastic and that there is no interaction between asperities. However, as asperities lie on a continuous substrate, the deformation of one asperity will change the height of all other asperities through deformation of the substrate and will thus influence subsequent contact evolution. Moreover, a high asperity contact pressure will result in plasticity, which below tens of microns is size dependent, with smaller being harder. In this paper, the asperity interaction effect is taken into account through substrate deformation, while a size-dependent plasticity model is adopted for individual asperities. The intrinsic length in the strain gradient plasticity (SGP) theory is obtained by fitting to two-dimensional discrete dislocation plasticity simulations of the flattening of a single asperity. By utilizing the single asperity, response in three dimensions and taking asperity interaction into account, a statistical calculation of rough surface contact is performed. The effectiveness of the statistical model is addressed by comparison with full-detail finite element simulations of rough surface contact using SGP. Throughout the paper, our focus is on the difference of contact predictions based on size-dependent plasticity as compared to conventional size independent plasticity. (C) 2017 Elsevier Ltd. All rights reserved