Building a Parameterized 4D Cardiac Model with Respiratory Motion from 2D MR Time Series

Abstract

Atrial fibrillation (AF) is a growing problem in modern societies with an enormous impact in both short term quality of life and long term survival. A recently developed promising approach to cure AF uses radiofrequency (RF) ablation to carry out "pulmonary vein antrum isolation" (PVAI), electrically isolating the pulmonary veins from the rest of the atrium. However, the lack of proper 3D surgery training, planning, and guidance, along with current limitations in understanding of the true causes and mechanisms of AF, makes this surgery a very difficult task for the surgeons. Therefore recurrence rates and even failures of the procedure, as well as the risk for the patient, increase. The purpose of this work is to develop methods for automatically segmenting and tracking the heart in 4-D cardiac MRI datasets. The reconstructed heart surface will serve as a virtual computer model for the 3D surgery training, planning and guidance. The method used in this project is based on an active contour model for segmentation, followed by a spatial-time post-filtering and processing of the data obtained by the segmentation.