Guy Cloutier

TL;DR: A new three-dimensional IVUS segmentation model, that is based on the fast-marching method and uses gray level probability density functions (PDFs) of the vessel wall structures, was developed and demonstrated the potential of gray level PDF and fast- marching methods in 3-D IVUS image processing.

TL;DR: The added value of combining different features computed from a single US acquisition with machine learning to characterize carotid artery plaques is shown.

TL;DR: The feasibility of using SSE to highlight atherosclerotic plaque vulnerability characteristics is indicated, and the Lagrangian speckle model estimator (LSME) elasticity imaging method was further developed to estimate shear strain elasticity (SSE).

TL;DR: The imaging parameters cumulated axial translation and the ratio of cumulation axial strain to cumulatedAxial translation, as computed using NIVE, were able to discriminate vulnerable carotid artery plaques characterized by MRI from nonvulnerable carotids.

TL;DR: This new IVUS segmentation method provides accurate results that correspond well to the experts' manually traced contours, but requires much less manual interactions and is faster.