WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image
  Synthesis
Paul Friedrich Julia Wolleb Florentin Bieder Alicia Durrer Philippe C. Cattin
Due to the three-dimensional nature of CT- or MR-scans, generative modeling
of medical images is a particularly challenging task. Existing approaches
mostly apply patch-wise, slice-wise, or cascaded generation techniques to fit
the high-dimensional data into the limited GPU memory. However, these
approaches may introduce artifacts and potentially restrict the model's
applicability for certain downstream tasks. This work presents WDM, a
wavelet-based medical image synthesis framework that applies a diffusion model
on wavelet decomposed images. The presented approach is a simple yet effective
way of scaling diffusion models to high resolutions and can be trained on a
single 40 GB GPU. Experimental results on BraTS and LIDC-IDRI unconditional
image generation at a resolution of $128 \times 128 \times 128$ show
state-of-the-art image fidelity (FID) and sample diversity (MS-SSIM) scores
compared to GANs, Diffusion Models, and Latent Diffusion Models. Our proposed
method is the only one capable of generating high-quality images at a
resolution of $256 \times 256 \times 256$.