用扩散模型提升光声成像重建质量，修复伪影并恢复细节
Using Spatial Diffusions for Optoacoustic Tomography Image Reconstruction
- 基于初始重建结果设计条件扩散模型，引导生成过程
- 在PSNR和SSIM上优于传统方法，有效修复伪影并增强细节
- 适合图像重建与医学成像领域研究者参考
光声成像重建近年来备受关注。本文利用近期提出的扩散模型强大的生成能力，提出一种基于条件扩散过程的方案。通过简单的延迟求和（Delay and Sum）方法进行初始重建，设计特定的自编码器架构生成潜在表示，并将其作为扩散模型的条件信息。数值实验表明，该方法在PSNR和SSIM等质量指标上表现优异，初始重建结果提供的条件信息能有效引导扩散过程，提升图像质量，修正伪影，并恢复初始方法无法获取的细微结构。
原文摘要 · Abstract (English)
Optoacoustic tomography image reconstruction has been a problem of interest in recent years. By exploiting the exceptional generative power of the recently proposed diffusion models we consider a scheme which is based on a conditional diffusion process. Using a simple initial image reconstruction method such as Delay and Sum, we consider a specially designed autoencoder architecture which generates a latent representation which is used as conditional information in the generative diffusion process. Numerical results show the merits of our proposal in terms of quality metrics such as PSNR and SSIM, showing that the conditional information generated in terms of the initial reconstructed image is able to bias the generative process of the diffusion model in order to enhance the image, correct artifacts and even recover some finer details that the initial reconstruction method is not able to obtain.
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