CARDIOMARKER- Computational imaging phenomics in population cardiac MRI with automatic image quality assessment: benchmarking, scalability and inference with state-of-the-art algorithms.
Principal Investigator: Professor Alejandro Frangi
Approved Research ID: 11350
Approval date: January 17th 2019
Several cardiovascular conditions like heart failure, coronary artery disease, diabetes, and structural heart disease manifest in alterations of the anatomy or deformation of the myocardium. The hypothesis of this study is that existing tools for cardiac image analysis providing information on 3D cardiac morphology and deformation, developed for small patient cohorts, scale up to handle datasets in the order of hundreds and thousands of subjects. We will simultaneously undertake benchmarking of competing algorithms as demonstrate the impact of image analysis errors on relevant associative and causal inference tasks. CARDIOMARKER will carry out a large-scale scalability testing of current image analysis tools ultimately helping UK Biobank researchers in extracting objective imaging phenotypic biomarkers of cardiac morphology and deformation correlated to disease presence, severity or progression. We will manually assess a subset of the datasets by two operators in two independent sessions. We will compare the manually segmented cardiac structures (MR cine) and tag intersections (MR tagging) from manual analysis against those of our automatic techniques. CARDIOMARKER will elucidate how errors in CMR biomarkers influence the strength of associative and causal models. In collaboration with our clinical experts, we will formulate illustrative hypothesis re the association between cardiac morphology/deformation and genetic, lifestyle (activity, body mass composition), metabolism-related (bone ageing, liver function), environmental (exposures), and physiological (HR, BP) variables. We will generate associative/causative models, and will study the influence on those models of errors in CMR biomarkers derived from automatic analysis. This will shed light on the strength of the associations/causal relationships as a function of the size of the population and the noise level in the markers themselves. We want to answer these questions in relationship to manual delineation and previous performance of the techniques: a) What is the accuracy in cardiac anatomy delineation in population imaging studies? b) What is the accuracy of extracted cardiac deformation fields? c) What is the failure rate of automated methods operating on large-scale population imaging? d) What is the impact of automated segmentation/registration errors on associative/causative models of phenotype-genotype relationship? We will deliver the UKBB automatic and objective quantitative imaging information on the full cohort of patients imaged with CMR helping. These will establish population ranges of normality and thresholds of abnormality useful in cardiology. We will deliver knowledge on how to interpret and how errors compound in statistical modelling when attempting to unravel associative/causal relationships involving not only CMR but also other biomarkers.