Patient-specific coronary blood supply territories for quantitative perfusion analysis.
Constantine ZakkaroffJohn D BiglandsJohn P GreenwoodSven PleinRoger D BoyleAleksandra RadjenovicDerek R MageePublished in: Computer methods in biomechanics and biomedical engineering. Imaging & visualization (2016)
Myocardial perfusion imaging, coupled with quantitative perfusion analysis, provides an important diagnostic tool for the identification of ischaemic heart disease caused by coronary stenoses. The accurate mapping between coronary anatomy and under-perfused areas of the myocardium is important for diagnosis and treatment. However, in the absence of the actual coronary anatomy during the reporting of perfusion images, areas of ischaemia are allocated to a coronary territory based on a population-derived 17-segment (American Heart Association) AHA model of coronary blood supply. This work presents a solution for the fusion of 2D Magnetic Resonance (MR) myocardial perfusion images and 3D MR angiography data with the aim to improve the detection of ischaemic heart disease. The key contribution of this work is a novel method for the mediated spatiotemporal registration of perfusion and angiography data and a novel method for the calculation of patient-specific coronary supply territories. The registration method uses 4D cardiac MR cine series spanning the complete cardiac cycle in order to overcome the under-constrained nature of non-rigid slice-to-volume perfusion-to-angiography registration. This is achieved by separating out the deformable registration problem and solving it through phase-to-phase registration of the cine series. The use of patient-specific blood supply territories in quantitative perfusion analysis (instead of the population-based model of coronary blood supply) has the potential of increasing the accuracy of perfusion analysis. Quantitative perfusion analysis diagnostic accuracy evaluation with patient-specific territories against the AHA model demonstrates the value of the mediated spatiotemporal registration in the context of ischaemic heart disease diagnosis.
Keyphrases
- contrast enhanced
- coronary artery disease
- coronary artery
- magnetic resonance
- high resolution
- optical coherence tomography
- computed tomography
- magnetic resonance imaging
- deep learning
- aortic stenosis
- left ventricular
- big data
- convolutional neural network
- aortic valve
- artificial intelligence
- atrial fibrillation
- ejection fraction
- solid state