WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis
<|reference_start|>WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis: 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 3D diffusion models to high resolutions and can be trained on a single \SI{40}{\giga\byte} GPU. Experimental results on BraTS and LIDC-IDRI unconditional image generation at a resolution of $128 \times 128 \times 128$ demonstrate state-of-the-art image fidelity (FID) and sample diversity (MS-SSIM) scores compared to recent GANs, Diffusion Models, and Latent Diffusion Models. Our proposed method is the only one capable of generating high-quality images at a resolution of $256 \times 256 \times 256$, outperforming all comparing methods.<|reference_end|>
@article{friedrich2024wdm:,
    title={WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image
  Synthesis},
    author={Paul Friedrich, Julia Wolleb, Florentin Bieder, Alicia Durrer,
  Philippe C. Cattin},
    journal={arXiv preprint arXiv:2402.19043},
    year={2024},
    doi={10.1007/978-3-031-72744-3_2},
    archivePrefix={arXiv},
    eprint={2402.19043},
    primaryClass={eess.IV cs.CV}
}