  Tagged magnetic resonance imaging (MRI) is a widely used imaging technique
for measuring tissue deformation in moving organs. Due to tagged MRI's
intrinsic low anatomical resolution, another matching set of cine MRI with
higher resolution is sometimes acquired in the same scanning session to
facilitate tissue segmentation, thus adding extra time and cost. To mitigate
this, in this work, we propose a novel dual-cycle constrained bijective VAE-GAN
approach to carry out tagged-to-cine MR image synthesis. Our method is based on
a variational autoencoder backbone with cycle reconstruction constrained
adversarial training to yield accurate and realistic cine MR images given
tagged MR images. Our framework has been trained, validated, and tested using
1,768, 416, and 1,560 subject-independent paired slices of tagged and cine MRI
from twenty healthy subjects, respectively, demonstrating superior performance
over the comparison methods. Our method can potentially be used to reduce the
extra acquisition time and cost, while maintaining the same workflow for
further motion analyses.
