A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images.

ABSTRACT: Heart motion tracking for radiation therapy treatment planning can result in effective motion management strategies to minimize radiation-induced cardiotoxicity. However, automatic heart motion tracking is challenging due to factors that include the complex spatial relationship between the heart and its neighboring structures, dynamic changes in heart shape, and limited image contrast, resolution, and volume coverage. In this study, we developed and evaluated a deep generative shape model-driven level set method to address these challenges. The proposed heart motion tracking method makes use of a heart shape model that characterizes the statistical variations in heart shapes present in a training data set. This heart shape model was established by training a three-layered deep Boltzmann ma

SUBMITTER: Wu J 

PROVIDER: S-EPMC6501847 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature