Abstract Operating offshore oil and gas production facilities is often associated with high risk. In order to manage the risk, operators commonly use aids to support decision making in the establishment of a maintenance and inspection strategy. Risk Based Inspection (RBI) analysis is widely used in the offshore industry as a means to justify the inspection strategy adopted. The RBI analysis is a decision-making technique that enables asset managers to identify the risk related to failure of their most critical systems and components, with an effect on safety, environmental and business related issues. Risk is a measure of possible loss or injury, and is expressed as the combination of the incident probability and its consequences. A component may have several associated risk levels depending on the different consequences of failure and the different probabilities of those failures occurring. Microbiologically Influenced Corrosion (MIC) is a degradation mechanism that has received increased attention from corrosion engineers and asset operators in the past decades. In this paper, the most recent models that have been developed in order to assess the impact of MIC on asset integrity will be presented and discussed. From a risk perspective, MIC is not satisfactorily assessed by the current models and the models lack a proper view of the MIC threat. Therefore, a review of known parameters that affect MIC is presented. The mapping and identification of parameters is based on the review of past models and an extensive up-to date literature study. The parameters are discussed and subsequently combined in a novel procedure that allows assessment of MIC in a RBI analysis. The procedure is sub-divided into one screening step and a detailed assessment, which fits the recommended approach to assess risk in a RBI analysis. To illustrate the practical application of the developed procedure a field case is presented.
Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): Research Institute of the McGill University Health Centre. Background Oxygenation-Sensitive Cardiac Magnetic Resonance (OS-CMR) has emerged as a powerful tool to investigate the underlying physiology of a number of disease states through the assessment of tissue oxygenation status with myocardial oxygenation reserve and functional kinetics of the myocardium with strain. Recently, the analysis of CMR scans with radiomics algorithms has demonstrated to have superior diagnostic accuracy over standard analysis and reporting methods. As up to half of patients undergoing coronary angiography are found to have ischemia with no significant coronary artery obstruction, a non-invasive diagnostic test that can help to more accurately stratify patients presenting with symptoms of ischemia as having significant or no significant coronary artery disease (CAD) would be of great clinical use. Methods We analyzed 49 patients (38 with significant and 15 without significant obstructive CAD) with a positive stress test and coronary angiography. All participants underwent a non-contrast CMR exam on a clinical 3T MRI system (Magnetom Skyra™, Siemens Healthineers, Erlangen, Germany) within one week of the coronary angiography. Long axis cine CMR for ventricular morphology, volumes, function including strain, and short axis OS-CMR images were acquired (total image acquisition time less than 15min). The images were imported and analyzed with a fully automated analysis package including an advanced machine learning algorithm (cvi42™ Cardiom prototype (Circle Cardiovascular Imaging, Alberta, Canada). Per participant, 602 discrete data points per participant are extracted. A 75% or higher degree of coronary artery stenosis on Quantitative Coronary Angiography (QCA) was used as the ground truth and classified as either 1 vessel disease (VD), 2VD, 3VD, or no significant coronary artery obstruction. Results Fig. 1 shows the top discriminative features as identified by the algorithm: OS-CMR derived marker: 1) myocardial oxygen saturation (LV SVO2), 2) myocardial oxygenation in response to hyperventilation stress (MORS), and 3) epicardial myocardial oxygenation reserve (MORE). Other predictive markers were: Peak Systolic Radial Strain, treatment with calcium channel blockers, presence of cerebrovascular disease, and hypertension. The algorithm showed a 73% classification accuracy of identifying patients with or without obstructive coronary artery stenosis. Conclusion In this proof-of-concept analysis, a fully automated post-processing tool and radiomics algorithm has demonstrated the potential to accurately predict clinical classification in patients with and without significant CAD with a non-invasive, contrast-free CMR protocol. Further training and refinement of analysis algorithms are likely to further enhance the predictive value.
Introduction: Prior studies on Canadian physicians' income have demonstrated a gender pay gap (GPG); however, there is a paucity of data in the Radiology specialty. A cross-sectional study was conducted to determine if practicing Canadian radiologists' self-reported income is related to gender, controlling for demographic and work variables. Methods: English and French online surveys were distributed by email and social media to radiologists and trainees (May-July 2021). The association between Gender (controlling for Ethnicity variables, Region, having Children, Full-/Part-Time work, and Academic position) and Self-Reported Income was examined using chi-square tests. Pearson correlations examined relationships between opinion variables. Analyses were conducted using SPSS V28.0. A priori significance was P < .05. Study had ethics approval. Results: Four hundred and fifty-four practicing Canadian radiologists responded. Majority were women (51.2%, n = 227), a non-visible Minority (71.7%, n = 317), and from Western Provinces (67.8%, n = 308). Significant relationship was established between Self-Reported Income and Gender (χ2 = 10.44, df = 2, P < .05). More men (70.6%, n = 120) than women (56.4%, n = 110), reported income "greater than $500 000"; fewer men (20.6%, n = 35) than women (35.9%, n = 70) reported "$300 000-$500 000"; a similar percent of men (8.8%, n = 15) and women (7.7%, n = 15) reported "less than $300 000." No relationship was found between self-reported income and gender for ethnicity variables, those without children, part-time, or non-academic radiologists. The opinion "Addressing the GPG is important" correlated to "Canadian Association of Radiologists should collect demographic data" (r = 0.63). Responses were low for ethnic minorities and non-western provinces. Conclusion: Our results suggest a GPG exists in Canadian radiology and is an important first step for future studies.
Abstract Background Myocardial strain imaging has gained importance in cardiac magnetic resonance (CMR) imaging in recent years as an even more sensitive marker of early left ventricular dysfunction than left-ventricular ejection fraction (LVEF). fSENC (fast strain encoded imaging) and FT (feature tracking) both allow for reproducible assessment of myocardial strain. However, left-ventricular long axis strain (LVLAS) might enable an equally sensitive measurement of myocardial deformation as global longitudinal or circumferential strain in a more rapid and simple fashion. Methods In this study we compared the diagnostic performance of fSENC, FT and LVLAS for identification of cardiac pathology (ACS, cardiac-non-ACS) in patients presenting with chest pain (initial hscTnT 5–52 ng/l). Patients were prospectively recruited from the chest pain unit in Heidelberg. The CMR scan was performed within 1 h after patient presentation. Analysis of LVLAS was compared to the GLS and GCS as measured by fSENC and FT. Results In total 40 patients were recruited (ACS n = 6, cardiac-non-ACS n = 6, non-cardiac n = 28). LVLAS was comparable to fSENC for differentiation between healthy myocardium and myocardial dysfunction (GLS-fSENC AUC: 0.882; GCS-fSENC AUC: 0.899; LVLAS AUC: 0.771; GLS-FT AUC: 0.740; GCS-FT: 0.688), while FT-derived strain did not allow for differentiation between ACS and non-cardiac patients. There was significant variability between the three techniques. Intra- and inter-observer variability (OV) was excellent for fSENC and FT, while for LVLAS the agreement was lower and levels of variability higher (intra-OV: Pearson > 0.7, ICC > 0.8; inter-OV: Pearson > 0.65, ICC > 0.8; CoV > 25%). Conclusions While reproducibility was excellent for both FT and fSENC, it was only fSENC and the LVLAS which allowed for significant identification of myocardial dysfunction, even before LVEF, and therefore might be used as rapid supporting parameters for assessment of left-ventricular function.
