- Poster presentation
- Open Access
Statistical analysis of ventricular shape of ARVC patients and correlation with clinical diagnostic indices
© McLeod et al; licensee BioMed Central Ltd. 2015
- Published: 3 February 2015
- Shape Mode
- Arrhythmogenic Right Ventricular Cardiomyopathy
- Clinical Index
- Ventricular Cardiomyopathy
- Syncope Episode
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.
We studied 28 ARVC patients fulfilling the 2010TFC ARVC diagnosis criteria at varying stages of the disease retrospectively from cine MRI images acquired from a Siemens SonataVision 1.5T scanner. The mean of these patients was computed using an iterative minimization approach with currents to represent ventricle surfaces and the LDDMM algorithm to compute the pair-wise shape deformations between each patient and the mean. Principal component analysis (PCA) was applied to the mean-to-patient deformations (which encode the shape variation in the population), in order to establish the dominant shape patterns present in this population. The correlations between the PCA shape modes and 11 clinical indices (including the 2010TFC indices) were computed.
A computational method for analysing shape abnormalities in ARVC patients and correlating these with the 2010TFC indices is presented and applied to 28 patients. The results indicate that the abnormal ventricular structure of ARVC patients may be affected by the clinical symptoms identified by the 2010TFC indices, in accordance with expected relationships observed in clinical practice.
This project was carried out as a part of the Centre for Cardiological Innovation, Norway, funded by the Research Council of Norway.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.