  Accurate 3D cardiac reconstruction from cine magnetic resonance imaging
(cMRI) is crucial for improved cardiovascular disease diagnosis and
understanding of the heart's motion. However, current cardiac MRI-based
reconstruction technology used in clinical settings is 2D with limited
through-plane resolution, resulting in low-quality reconstructed cardiac
volumes. To better reconstruct 3D cardiac volumes from sparse 2D image stacks,
we propose a morphology-guided diffusion model for 3D cardiac volume
reconstruction, DMCVR, that synthesizes high-resolution 2D images and
corresponding 3D reconstructed volumes. Our method outperforms previous
approaches by conditioning the cardiac morphology on the generative model,
eliminating the time-consuming iterative optimization process of the latent
code, and improving generation quality. The learned latent spaces provide
global semantics, local cardiac morphology and details of each 2D cMRI slice
with highly interpretable value to reconstruct 3D cardiac shape. Our
experiments show that DMCVR is highly effective in several aspects, such as 2D
generation and 3D reconstruction performance. With DMCVR, we can produce
high-resolution 3D cardiac MRI reconstructions, surpassing current techniques.
Our proposed framework has great potential for improving the accuracy of
cardiac disease diagnosis and treatment planning. Code can be accessed at
https://github.com/hexiaoxiao-cs/DMCVR.
