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
this https URL

通过形态导向扩散模型，我们提出了 DMCVR，用于从稀疏的 2D 图像堆叠中更好地重建 3D 心脏容积，这种方法在生成模型中将心脏形态作为条件，消除了潜在代码的耗时迭代优化过程，提高了生成质量。我们的实验表明，DMCVR 在 2D 生成和 3D 重建性能等几个方面非常有效，能够产生高分辨率的 3D 心脏 MRI 重建，超过了当前的技术水平。我们提出的框架有着改善心脏疾病诊断和治疗规划准确性的巨大潜力。