Common Carotid Artery Lumen Segmentation from Cardiac Cycle-Resolved Cine Fast Spin Echo Magnetic Resonance Imaging.

Atherosclerosis is a disease responsible for millions of deaths each year, primarily due to heart attack and stroke. Magnetic resonance (MR) imaging is a non-invasive method that can be used to analyze the carotid artery and detect signs of atherosclerosis. Most MR methods acquire high contrast, static images. These methods, however, are sensitive to artifacts from cardiac motion, produce time-averaged images, and do not allow for carotid distensibility analysis. Carotid distensibility is an important, systematic measure of vascular health. Cine fast spin echo (FSE) is a new MR imaging that can obtain dynamic MR data (i.e., cardiac phase-resolved datasets). Dynamic imaging, however, comes at the cost of lower spatial resolution and signal-to-noise ratio, making these data potentially more difficult to segment. This paper introduces a semi-automated segmentation method that segments the common carotid artery (CCA) lumen across the cardiac cycle from dynamic MR images. To the best of our knowledge, this work is the first proposed technique for segmenting cardiac cycle-resolved cine FSE images. It combines a priori knowledge about the size and shape of the CCA, with the max-tree data structure, the tie-zone watershed transform (using identified internal and external markers) and supervised classification, to segment the carotid artery wall-lumen boundary. The user has to select only a seed point (centred in the carotid artery lumen). Technique performance was assessed using forty-five cine FSE data sets, each consisting of images reconstructed at sixteen temporal bins across the cardiac cycle. The automatic segmentation results were compared against the consensus of three different manual segmentation results. Our technique achieved an average Dice coefficient, sensitivity and false positive rate of 0.928±0.031 (mean ± standard deviation), 0.915 ± 0.037 and 0.056 ± 0.049, respectively. Our method achieved higher agreement versus the consensus of the three manual segmentations than the individual manual segmentations versus the consensus.