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An evaluation of the initial UK Biobank 5,000 cases to generate homologous points on the right and left ventricular surfaces for the purposes of atlas analysis.
UK Biobank, a large cohort study, plans to acquire 100,000 cardiac MRI studies by 2020. Atlas-based right and left ventricular analysis is useful for investigating relationships between remodelling and disease. This project sought to provide points to enable construction of bi-ventricular atlases.
The files contain points generated from a finite element model fitted to contours drawn by hand under UK Biobank application 2964. There are 4329 cases. The points can be used to generate a biventricular atlas of right and left ventricular shape at end-diastole and end-systole. The json files contain epicardial and endocardial surface and valve points as well as landmarks. The points are homologous: each point should be in approximately the same position in the heart. The file Read_and_Plot_RVLV_Model.html contains a jupyter notebook illustrating how to load and display the points using python.
Derived output from this project: 1. Points in x,y,z coordinates (mm). 2. A script for reading and plotting points.
Description of cardiovascular phenotype in the UK Biobank population based on cardiovascular magnetic resonance and carotid ultrasound
Imaging of the heart and blood vessels is performed in a large subset of the UK Biobank cohort. Many measures defining the state of the heart and blood vessels can be derived from the images acquired. These measures are influenced by various health conditions and modifiable and non-modifiable factors, such as age, gender and ethnicity. The aim of this proposal is to describe the measures of the heart and blood vessel in the UK Biobank population and investigate how much modifiable and non-modifiable factors influence them. All new data will be made available for future research. Knowing the reference ranges for common imaging measures of the heart and circulation and how they are influenced by factors, such as age, gender, ethnicity, risk factors for heart attacks and strokes, is key for improving making diagnoses and predicting health outcomes. Descriptive statistics will be performed for all image derived phenotypes (IDPs) from the cardiovascular magnetic resonance (CMR) and carotid ultrasound images. We will perform subgroup analysis for important clinical factors, such as age, gender, cardiovascular risk, chronic conditions (e.g. Diabetes). We will apply descriptive statistics to a subpopulation considered `healthy without cardiovascular disease or presence of modifiable risk factors`. Univariate and multivariate regression analysis will be used to assess relationships between IDPs and relevant co-variates. We will also assess intra- and inter-observer variability for IDP measurement when repeat analysis is available. Initial 5000 subjects from the imaging enhancement study.