Fusion of structural and functional cardiac magnetic resonance imaging data for studying Ventricular Fibrillation
Karl Magtibay, Mohammad Taghi Hamidi Beheshti, Farbod Hosseyndoust Foomany, Krishnanand Balasundaram, Stéphane Massé, Patrick F.H. Lai, John A. Asta, Nima Zamiri, David A. Jaffray, Kumaraswamy Nanthakumar, Sridhar Krishnan, Karthikeyan Umapathy · 2014
Magnetic Resonance Imaging (MRI) techniques such as Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI) provide a complementing set of imaging data that can describe both the functional and structural states of biological tissues. This paper presents a Joint Independent Component Analysis (jICA) based fusion approach which can be utilized to fuse CDI and DTI data to quantify the differences between two cardiac states: Ventricular Fibrillation (VF) and Asystolic/Normal (AS/NM). Such an approach could lead to a better insight on the mechanism of VF. Fusing CDI and DTI data from 8 data sets from 6 beating porcine hearts, in effect, detects the differences between two cardiac states, qualitatively and quantitatively. This initial study demonstrates the applicability of MRI-based imaging techniques and jICA-based fusion approach in studying cardiac arrhythmias.