Key result
A refined 4-D parametric modeling method utilizing a learning segmentation process successfully produced 3-D myocardial thickness and strain maps from MRI cardiac acquisitions.
A novel 4-D parametric modeling method utilizing neural network classification demonstrates potential for extracting myocardial thickness and strain maps from standard cardiac MRI.
May enable detailed 3-D myocardial mapping from routine MRI; extends prior 4-D modeling but remains hypothesis-generating.
A previously, presented 4-D method for modeling the myocardial surfaces and their deformation was refined and applied to the measurement of diagnostic parameters. The method initially defines the myocardial surfaces of the left ventricle. Based on the derived model, measurements of myocardial thickness and thickening in time were produced and used to construct 3-D myocardial thickness maps, which were color coded on the surface for visualization over time. Estimations of myocardial strain maps were also produced, taking into account the deformation of myocardial surfaces. The shape extraction method was improved by utilizing a learning segmentation process, based on a generating-shrinking neural network classifier. A multiscale approach was also adopted which starts from a rough approximation of the expected shape and gradually proceeds to the accurate model. The method was applied to multi-slice multi-phase MRI cardiac acquisitions. Although the displacement and strain maps were not derived from true functional data, have shown promise for cardiac function diagnosis.
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Stalidis et al. (2003) studied Cardiac function diagnosis. 4-D parametric modeling and learning classification method was evaluated on Measurement of myocardial thickness, thickening, and strain maps. A refined 4-D parametric modeling method utilizing a learning segmentation process successfully produced 3-D myocardial thickness and strain maps from MRI cardiac acquisitions.
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