2026-04-17T07:50:51Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/199582025-12-24T08:31:35Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Oubel, E. author Craene, Mathieu de author Hero, A.O. author Pourmorteza, A. author Huguet, Marina author Avegliano, Gustavo P. author Bijnens, Bart author Frangi Caregnato, Alejandro author 2012-12-13T13:54:15Z 2012-12-13T13:54:15Z 2011 Image registration has been proposed as an automatic method for recovering cardiac displacement fields from Tagged Magnetic Resonance Imaging (tMRI) sequences. Initially performed as a set of pairwise registrations, these techniques have evolved to the use of 3D+t deformation models, requiring metrics of joint image alignment (JA). However, only linear combinations of cost functions defined with respect to the first frame have been used. In this paper, we have applied k-Nearest Neighbors Graphs (kNNG) estimators of the -entropy (H ) to measure the joint similarity between frames, and to combine the information provided by different cardiac views in an unified metric. Experiments performed on six subjects showed a significantly higher accuracy (p < 0.05) with respect to a standard pairwise alignment (PA) approach in terms of mean positional error and variance with respect to manually placed landmarks. The developed method was used to study strains in patients with myocardial infarction, showing a consistency between strain, infarction location, and coronary occlusion. This paper also presents/nan interesting clinical application of graph-based metric estimators, showing their value for solving practical problems found in medical imaging. Cor -- Malalties Imatges per ressonància magnètica MR tagging Myocardial deformation analysis Non-rigid registration Entropic graphs Cardiac motion estimation by joint alignment of tagged MRI sequences