Abstract

Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological organisation of brain structure in health and disease. Network reconstruction is complex and involves a series of processesing methods including anatomical parcellation, registration, fiber orientation estimation and whole-brain fiber tractography. Methodological choices at each stage can affect the anatomical accuracy and graph theoretical properties of the reconstructed networks, meaning applying different combinations in a network reconstruction pipeline may produce substantially different networks. Furthermore, the choice of which connections are considered important is unclear. In this study, we assessed the similarity between structural networks obtained using two independent state-of-the-art reconstruction pipelines. We aimed to quantify network similarity and identify the core connections emerging most robustly in both pipelines. Similarity of network connections was compared between pipelines employing different atlases by merging parcels to a common and equivalent node scale. We found a high agreement between the networks across a range of fiber density thresholds. In addition, we identified a robust core of highly connected regions coinciding with a peak in similarity across network density thresholds, and replicated these results with atlases at different node scales. The binary network properties of these core connections were similar between pipelines but showed some differences in atlases across node scales. This study demonstrates the utility of applying multiple structural network reconstrution pipelines to diffusion data in order to identify the most important connections for further study. Document type: Article

Abstract

The identification of corrosion, cracks and defects in pipelines used for transporting oil and gas can reduce the possibility of leaks, and consequently, it can limit the extent of an environmental disaster, public hazard and the associated financial impact of such events. Typically, corrosion in oil pipelines is measured with non-destructive ultrasonic or electromagnetic techniques, on the basis that corrosion and defects are often manifest as a change of thickness in the steel from which pipelines are made. However, such approaches are not practical for underground pipelines and their deployment can be complicated for the case of pipelines covered by insulation. In this paper, we present an innovative, non-destructive testing technique, which exploits the backscatter of a combination of fast-neutron and γ radiation from steel samples of a variety of thicknesses consistent with changes that might arise due to corrosion of a pipe wall. Our research demonstrates the potential to measure and characterise different steel thicknesses by detecting both the elastic, fast-neutron backscatter and the Compton-scattered γ radiations, simultaneously. Further, we demonstrate that the presence of insulation yields a consistent and separable influence on the experimental, wall-thickness measurements. The data from experimental measurements are supported by a comprehensive Monte Carlo computer simulation study. Document type: Article