Background Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiomyopathy characterized by fatty and fibrotic replacement of cardiac tissue, which ultimately affects the structure, function and electrical propagation of the ventricles. Diagnosis of ARVC is challenging and is currently guided by the 2010 Task force criteria (2010TFC), which includes criteria identified from imaging, ECG and family history. We aimed to compute a mean 3D model of the ventricles of ARVC patients and analyse the shape modes around this mean to correlate with the 2010TFC indices.
Cardiac disease can reduce the ability of the ventricles to function well enough to sustain long-term pumping efficiency. Recent advances in cardiac motion tracking have led to improvements in the analysis of cardiac function. We propose a method to study cohort effects related to age with respect to cardiac function. The proposed approach makes use of a recent method for describing cardiac motion of a given subject using a polyaffine model, which gives a compact parameterization that reliably and accurately describes the cardiac motion across populations. Using this method, a data tensor of motion parameters is extracted for a given population. The partial least squares method for higher order arrays is used to build a model to describe the motion parameters with respect to age, from which a model of motion given age is derived. Based on the cross-sectional statistical analysis with the data tensor of each subject treated as an observation along time, the left ventricular motion over time of Tetralogy of Fallot patients is analysed to understand the temporal evolution of functional abnormalities in this population compared to healthy motion dynamics.
During the past ten years, the biophysical modelling of the human body has been a topic of increasing interest in the field of biomedical image analysis. The aim of such modelling is to formulate personalized medicine where a digital model of an organ can be adjusted to a patient from clinical data. This virtual organ would enable to estimate the parameters which are difficult to quantify in clinical routine, such as pressure, and to test computer-based therapies that can predict the evolution of the organ over time and with therapy. Nevertheless, in order to be able to translate such an approach to clinical practice, there is a crucial demand for robust statistical methods for studying multiple cases in a patient population, in order to be able to understand the effect of different clinical factors on the anatomy and extract the significant phenomena. Such statistical analyses can both provide a predictive model and guide the biophysical approach. However, computing statistics on such complex objects (i.e. 3D shapes) is very challenging. It was traditionally relying on point based discretisation of the shapes where the point-to-point correspondence is an important limiting factor for the usability of the method. New approaches were recently developed to compute such statistics without this limitation [1], and we present in this paper an application of these along with an open source tool made available through the VPH Network of Excellence Toolkit that allows multiple patients to be compared and analysed using this statistical method. The tools can be downloaded from http://www-sop.inria.fr/asclepios/projects/Health-e-Child/ShapeAnalysis/index.php.
1Department of Nuclear Medicine, Queen Elizabeth Hospital, Birmingham, 2Department of Cardiology, Royal Hospital for Sick Children, Glasgow and 3Department of Radiology, Birmingham Children's Hospital, Birmingham, UK
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
