DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction

TL;DR

This paper introduces DMCVR, a morphology-guided diffusion model that significantly improves 3D cardiac volume reconstruction from sparse 2D MRI slices, enhancing diagnostic accuracy.

Contribution

The novel DMCVR model integrates cardiac morphology into a diffusion framework, eliminating iterative optimization and producing high-quality 3D reconstructions from limited 2D data.

Findings

Outperforms previous methods in 3D reconstruction quality

Produces high-resolution 3D cardiac MRI from sparse 2D slices

Provides interpretable latent spaces capturing cardiac morphology

Abstract

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.