  Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to
building statistical cardiac anatomy models and understanding functional
mechanisms from motion patterns. However, due to the low through-plane
resolution of cine MR and high inter-subject variance, accurately segmenting
cardiac images and reconstructing the 3D volume are challenging. In this study,
we propose an end-to-end latent-space-based framework, DeepRecon, that
generates multiple clinically essential outcomes, including accurate image
segmentation, synthetic high-resolution 3D image, and 3D reconstructed volume.
Our method identifies the optimal latent representation of the cine image that
contains accurate semantic information for cardiac structures. In particular,
our model jointly generates synthetic images with accurate semantic information
and segmentation of the cardiac structures using the optimal latent
representation. We further explore downstream applications of 3D shape
reconstruction and 4D motion pattern adaptation by the different latent-space
manipulation strategies.The simultaneously generated high-resolution images
present a high interpretable value to assess the cardiac shape and
motion.Experimental results demonstrate the effectiveness of our approach on
multiple fronts including 2D segmentation, 3D reconstruction, downstream 4D
motion pattern adaption performance.
