title: Joint statistics on cardiac shape and fiber architecture.
abstract: Cardiac fiber architecture plays an important role in electrophysiological and mechanical functions of the heart. Yet, its inter-subject variability and more particularly, its relationship to the shape of the myocardium, is not fully understood. In this paper, we extend the statistical analysis of cardiac fiber architecture beyond its description with a fixed average geometry. We study the co-variation of fiber architecture with either shape or strain-based information by exploring their principal modes of joint variations. We apply our general framework to a dataset of 8 ex vivo canine hearts, and find that strain-based information appears to correlate best with the fiber architecture. Furthermore, compared to current approaches that warp an average atlas to the patient geometry, our preliminary results show that joint statistics improves fiber synthesis from shape by 8.0%, with cases up to 25.9%. Our experiments also reveal evidence on a possible relation between architectural variability and myocardial thickness.
authors.affiliation: Centre for Intelligent Machines, McGill University, Montreal.
authors.forename: Hervé
authors.initials: H
authors.lastname: Lombaert
authors.affiliation: Qatar Robotic Surgery Centre, Doha, Qatar.
authors.forename: Jean-Marc
authors.initials: JM
authors.lastname: Peyrat
mesh_terms: D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods*; D004285:Dogs; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009206:Myocardium / Q000166:cytology*; D032383:Myocytes, Cardiac / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity