Implementation and Validation of a Three-dimensional Cardiac Motion Estimation Network.

ABSTRACT:

Purpose

To describe an unsupervised three-dimensional cardiac motion estimation network (CarMEN) for deformable motion estimation from two-dimensional cine MR images.

Materials and methods

A function was implemented using CarMEN, a convolutional neural network that takes two three-dimensional input volumes and outputs a motion field. A smoothness constraint was imposed on the field by regularizing the Frobenius norm of its Jacobian matrix. CarMEN was trained and tested with data from 150 cardiac patients who underwent MRI examinations and was validated on synthetic (n = 100) and pediatric (n = 33) datasets. CarMEN was compared to five state-of-the-art nonrigid body registration methods by using several performance metrics, including Dice similarity coefficient

SUBMITTER: Morales MA 

PROVIDER: S-EPMC6677286 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature