  Temporal volume images with 3D+t (4D) information are often used in medical
imaging to statistically analyze temporal dynamics or capture disease
progression. Although deep-learning-based generative models for natural images
have been extensively studied, approaches for temporal medical image generation
such as 4D cardiac volume data are limited. In this work, we present a novel
deep learning model that generates intermediate temporal volumes between source
and target volumes. Specifically, we propose a diffusion deformable model (DDM)
by adapting the denoising diffusion probabilistic model that has recently been
widely investigated for realistic image generation. Our proposed DDM is
composed of the diffusion and the deformation modules so that DDM can learn
spatial deformation information between the source and target volumes and
provide a latent code for generating intermediate frames along a geodesic path.
Once our model is trained, the latent code estimated from the diffusion module
is simply interpolated and fed into the deformation module, which enables DDM
to generate temporal frames along the continuous trajectory while preserving
the topology of the source image. We demonstrate the proposed method with the
4D cardiac MR image generation between the diastolic and systolic phases for
each subject. Compared to the existing deformation methods, our DDM achieves
high performance on temporal volume generation.
