Cardiac Fibre Trace Clustering for the Interpretation of the Human Heart Architecture

Cardiac fibre architecture plays a key role in heart function. Recently, the estimation of fibre structure has been simplified with diffusion tensor MRI (DT-MRI). In order to assess the heart architecture and its underlying function, with the goal of dealing with pathological tissues and easing inter-patient comparisons, we propose a methodology for finding cardiac myofibrille trace correspondences across a fibre population obtained from DT-MRI data. It relies on the comparison of geometrical and topological clustering operating on different fibre representation modes (fixed length sequences of 3-D coordinates with or without ordering strategy, and 9-D vectors for trace shape approximation). In geometrical clustering (or k-means) each fibre path is assigned to the cluster with nearest barycenter. In topological (or spectral) clustering the data is represented by a similarity graph and the graph vertices are divided into groups so that intra-cluster connectivity is maximized and inter-cluster connectivity is minimized. Using these different clustering methods and fibre representation modes, we predict different fibre trace classifications for the same cardiac dataset. These classification results are compared to the human heart architecture models proposed in the literature.