WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis

• URL: http://arxiv.org/abs/2402.19043v2
• Date: Fri, 19 Jul 2024 09:42:22 GMT
• Title: WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis
• Authors: Paul Friedrich, Julia Wolleb, Florentin Bieder, Alicia Durrer, Philippe C. Cattin,
• Abstract summary: This work presents WDM, a wavelet-based medical image synthesis framework that applies a diffusion model on wavelet images. 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. 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.
• Score: 1.647759094903376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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