Cardiac image modelling: Breadth and depth in heart disease

Abstract

With the advent of large-scale imaging studies and big health data, and the corresponding growth in analytics, machine learning and computational image analysis methods, there are now exciting opportunities for deepening our understanding of the mechanisms and characteristics of heart disease. Two emerging fields are computational analysis of cardiac remodelling (shape and motion changes due to disease) and computational analysis of physiology and mechanics to estimate biophysical properties from non-invasive imaging. Many large cohort studies now underway around the world have been specifically designed based on non-invasive imaging technologies in order to gain new information about the development of heart disease from asymptomatic to clinical manifestations. These give an unprecedented breadth to the quantification of population variation and disease development. Also, for the individual patient, it is now possible to determine biophysical properties of myocardial tissue in health and disease by interpreting detailed imaging data using computational modelling. For these population and patient-specific computational modelling methods to develop further, we need open benchmarks for algorithm comparison and validation, open sharing of data and algorithms, and demonstration of clinical efficacy in patient management and care. The combination of population and patient-specific modelling will give new insights into the mechanisms of cardiac disease, in particular the development of heart failure, congenital heart disease, myocardial infarction, contractile dysfunction and diastolic dysfunction.

Keywords: Biomechanics; Cardiac atlases; Computational modelling.

Figure 1

Information maximising component analysis (IMCA) of shape differences between asymptomatic subjects and patients with myocardial infarction (MI). Left: visualisation of the remodelling index. Right: discrimination of remodelling score (Zhang et al., 2015).

Figure 2

Top: Pipeline for generating templates for CHD lesions: a) 3D image from MRI or CT; b) registration with initial model; c) coarse shape customisation; d) refinement through subdivision surfaces; e) customisation to different patients; f) patient specific model. Bottom: Patient specific model at end-diastole for a 42 year old female with repaired tetralogy of Fallot: g) short axis view; h) long axis view; i) model with images; j) valves.

Figure 3

a) Anisotropic mechanical behaviour. b) Diffusion tensor MRI showing muscle fibre orientation. c) Left ventricle model customised to patient images. d) Stress derived from a biomechanical model. e) Local myocardial work estimated for each of the 17 AHA segments.

Figure 4

a) 3D echo dataset showing short and long axis reformatted slices. b) 3D model customised to a 3D echo dataset from a healthy volunteer. c) 3D model customised to MRI dataset in the same healthy volunteer.