A High-Resolution Atlas and Statistical Model of the Human Heart From Multislice CT

Abstract

Atlases and statistical models play important roles in the personalization and simulation of cardiac physiology. For the study of the heart, however, the construction of comprehensive atlases and spatio-temporal models is faced with a number of challenges, in particular the need to handle large and highly variable image datasets, the multi-region nature of the heart, and the presence of complex as well as small cardiovascular structures. In this paper, we present a detailed atlas and spatio-temporal statistical model of the human heart based on a large population of 3D+time multi-slice computed tomography sequences, and the framework for its construction. It uses spatial normalization based on nonrigid image registration to synthesize a population mean image and establish the spatial relationships between the mean and the subjects in the population. Temporal image registration is then applied to resolve each subject-specific cardiac motion and the resulting transformations are used to warp a surface mesh representation of the atlas to fit the images of the remaining cardiac phases in each subject. Subsequently, we demonstrate the construction of a spatio-temporal statistical model of shape such that the inter-subject and dynamic sources of variation are suitably separated. The framework is applied to a 3D+time data set of 138 subjects. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. The obtained level of detail and the extendability of the atlas present an advantage over most cardiac models published previously.

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

“…In terms of the imaging modality, cine MR and CT are often used to infer shape information (Frangi et al, 2002;Fonseca et al, 2011;Hoogendoorn et al, 2013), whereas tagged MR, cine MR and US are often used to capture cardiac motion (Chandrashekara et al, 2003;Suinesiaputra et al, 2009;Duchateau et al, 2011). In order to characterise the cardiac myocardial fibre architecture, diffusion tensor MRI (DT-MRI) is used for imaging ex vivo human hearts (Lombaert et al, 2012).…”

“…Based on the atlas, the cardiac shape or function of a popula- Rougon et al (2004) Tagged MR 11 healthy Statistical motion model Suinesiaputra et al (2009) 2D cine MR 44 healthy Statistical contour model Fonseca et al (2011) 2D cine MR 2864 healthy, 470 patients Statistical shape model Duchateau et al (2011) 2D US 21 healthy Statistical motion model Duchateau et al (2012) 2D Ecabert et al (2008) CT 13 patients Statistical shape model Zhuang et al (2010) Whole-heart MR 10 healthy - Hoogendoorn et al (2013) CT 138 patients Statistical shape model tion can be analysed and compared in a common space. The statistical shape model is the most commonly used tool for shape analysis.…”

“…Apart from PCA, independent component analysis (ICA) has also been used to encode the variation of the cardiac shape (Suinesiaputra et al, 2009). Recently, Hoogendoorn et al proposed a bilinear shape model in which the inter-subject variation and inter-phase variation are encoded separately in two dimensions (Hoogendoorn et al, 2009(Hoogendoorn et al, , 2013. To utilise pre-existing statistical shape models, Pereañez et al proposed a framework of merging multimodality models by spatial normalisation and eigenspace fusion (Pereañez et al, 2014).…”

“…Cardiac motion can be encoded either implicitly or explicitly. Implicit models describe the motion using the variation of the shape across a cardiac cycle (Perperidis et al, 2005;Hoogendoorn et al, 2013) or using the relative position of the end-systolic (ES) contour with respect to the end-diastolic (ED) contour (Suinesiaputra et al, 2009;Medrano-Gracia et al, 2012). An alternative way is to describe motion explicitly by estimating the displacement field (or the velocity field) via motion tracking for each subject, transporting the displacement field of all the subjects to the common template space and computing the statistics (Duchateau et al, 2011;De Craene et al, 2012).…”

“…The template image space provides a common reference space for population-based shape analysis (Hoogendoorn et al, 2013). The template surface mesh can be used to generate a personalised patient-specific mesh for biomechanical simulation (Lamata et al, 2011).…”