Congenital aortic disease: 4D magnetic resonance segmentation and quantitative analysis

Abstract

Automated and accurate segmentation of the aorta in 4D (3D+time) cardiovascular magnetic resonance (MR) image data is important for early detection of congenital aortic disease leading to aortic aneurysms and dissections. A computer-aided diagnosis (CAD) method is reported that allows one to objectively identify subjects with connective tissue disorders from 16-phase 4D aortic MR images. Starting with a step of multi-view image registration, our automated segmentation method combines level-set and optimal surface segmentation algorithms in a single optimization process so that the final aortic surfaces in all 16 cardiac phases are determined. The resulting aortic lumen surface is registered with an aortic model followed by calculation of modal indices of aortic shape and motion. The modal indices reflect the differences of any individual aortic shape and motion from an average aortic behavior. A Support Vector Machine (SVM) classifier is used for the discrimination between normal and connective tissue disorder subjects. 4D MR image data sets acquired from 104 normal volunteers and connective tissue disorder patients MR datasets were used for development and performance evaluation of our method. The automated 4D segmentation resulted in accurate aortic surfaces in all 16 cardiac phases, covering the aorta from the aortic annulus to the diaphragm, yielding subvoxel accuracy with signed surface positioning errors of -0.07+/-1.16 voxel (-0.10+/-2.05mm). The computer-aided diagnosis method distinguished between normal and connective tissue disorder subjects with a classification correctness of 90.4%.