Joint cardiac $T₁$ mapping and cardiac cine using a deep manifold framework

The main focus of this work is to introduce a deep generative model for simultaneous free-breathing cardiac $T₁$ mapping and CINE MRI. The data is acquired by a gradient echo inversion recovery sequence with intermittent delays for magnetization recovery. The joint reconstruction of the image time-series is performed using a patient-specific deep manifold reconstruction algorithm which learns a CNN generative model and its latent vectors from the measured k-t space data in an unsupervised fashion. Following learning, the model can be used to generate synthetic images at specific motion and contrast states.