Diffusion Models in Medical Imaging: A Comprehensive Survey
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摘要:
以扩散模型为代表的生成式人工智能近年来在医学成像领域取得了迅猛的进展。为了帮助更多学者全面的了解扩散模型这一先进技术，本文旨在提供扩散模型在医学成像领域的详细概述。首先以扩散模型的起源演变为主线介绍扩散建模框架的基础理论和基本概念；其次根据扩散模型的特点提供其在医学成像领域的系统分类，并涵盖不同成像模态如磁共振成像、计算机断层扫描、正电子发射计算机断层显像和光声成像等的广泛应用；最后讨论目前扩散模型的局限性并展望未来研究的潜在发展方向，为研究者后续的探索提供了一个直观的起始点。部分代码开源在网址：https://github.com/yqx7150/Diffusion-Models-for-Medical-Imaging。
Abstract:Generative artificial intelligence represented by diffusion models has significantly contributed to medical imaging reconstruction. To help researchers comprehensively understand the rich content of diffusion models, this review provides a detailed overview of diffusion models used in medical imaging reconstruction. The theoretical foundation and fundamental concepts underlying the diffusion modeling framework were first introduced, describing their origin and evolution. Second, a systematic characteristic-based taxonomy of diffusion models used in medical imaging reconstruction is provided, broadly covering their application to imaging modalities, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission computed tomography (PET), and photoacoustic imaging (PAI). Finally, we discuss the limitations of current diffusion models and anticipate potential directions of future research, providing an intuitive starting point for subsequent exploratory research. Related codes are available at GitHub: https://github.com/yqx7150/Diffusion-Models-for-Medical-Imaging.
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图 1 （a）扩散模型起源演变的时间轴；（b）按照关键词（diffusion model | medical imaging）、（score-based model | medical imaging）、（diffusion | medical | probabilistic model）在Google Scholar和Arxiv Sanity Preserver进行近5年相关论文检索，筛选并删除相同的结果最终统计扩散模型应用于不同成像模态分类的论文比例
Figure 1. (a) Timeline of the origin and evolution of the diffusion model; (b) Relevant papers published within the last five years found via Google Scholar and Arxiv Sanity Preserver searches using the keywords (diffusion model | medical imaging), (score-based model | medical imaging), and (diffusion | medical | probabilistic model). Duplicate results were identified and deleted before calculating the proportion of papers applying a diffusion model to different imaging modality classifications
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图 2 扩散模型应用于不同成像模态的论文分类
Figure 2. Classification of papers describing the application of diffusion models to different imaging modalities
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图 3 扩散模型分别在图像域和K空间域的基本框架。其中前向过程从初始数据开始逐步添加噪声，使其最终接近纯噪声分布；逆向过程通过学习逐步去噪，从随机噪声中还原为高质量的目标数据
Figure 3. Basic framework of the diffusion model in the image and K-space domain. The forward process gradually adds noise from the initial data to approach a pure noise distribution; the reverse process gradually removes noise through learning and recovers high-quality target data from random noise
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图 4 单线圈脑部数据集在伪径向采样模式下欠采样倍数为5时不同方法的重建结果比较。绿色框和红色框分别表示感兴趣区域及其残差图
Figure 4. Comparison of reconstruction results using different methods on a single-coil brain dataset at pseudo-radial under-sampling of 5-fold. The green and red boxes indicate the region of interest and its residual map, respectively
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图 5 不同方法生成的CT图像的数值比较
Figure 5. Numerical comparison of CT images generated by different methods
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图 6 不同算法在泊松采样模式下欠采样倍数为4的重建结果
Figure 6. Reconstruction results of different methods at Poisson under-sampling of 4-fold
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图 7 随机采样模式下欠采样倍数为5和均匀采样模式下欠采样倍数为4的fastMRI膝关节和SIAT脑部重建结果
Figure 7. Reconstruction results of fastMRI knee data and SIAT brain data at random under-sampling of 5-fold and uniform under-sampling of 4-fold
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图 8 不同算法下人体CT扫描有限角度（60°和90°）重建结果（右下角数值为PSNR值）
Figure 8. Reconstruction results of limited angles (60° and 90°) of human CT scan data using different algorithms (the value in each lower right corner is the PSNR value)
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图 9 5 e3剂量下的重建结果及残差图
Figure 9. Reconstruction results and residual images at a dose of 5 e3
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图 10 稀疏角度为32°时重建血管的过程（数字分别代表PSNR值和SSIM值）
Figure 10. Process of blood vessel reconstruction when the sparse angle is 32° (The numbers represent PSNR and SSIM values, respectively)
表 1 五大生成模型全方位对比
Table 1 Comprehensive comparison of five generative models
特点/模型 VAE GAN EBM Flow Diffusion Model 基本原理 由编码器将数据映射到潜在空间概率分布，再由解码器生成数据 生成器和判别器对抗训练，生成器生成数据，判别器区分真假 定义一个可微的能量函数，将数据点的概率分布与其能量值联系 通过可逆变换将简单分布映射到复杂数据分布。 从噪声分布起，逐步去噪恢复目标数据 优点 1.生成质量高
2.训练稳定
3.潜在空间连续1.生成质量高
2.多样性好
3.应用广泛1.灵活性高
2.隐式生成
3.表示能力强1.高效样本生成和密度估计
2.可解释性强1.生成质量高
2.强大的建模能力
3.广泛的应用场景缺点 1.生成样本模糊
2.计算复杂度高
3.难捕捉复杂分布1.训练困难
2.对数据敏感
3.计算资源消耗大1.配分函数难计算
2.训练不稳定
3.采样效率低1.设计合适的变换
2.模块具有挑战性
3.计算资源需求高1.训练过程复杂
2.对噪声模型依赖性
3.生成速度较慢训练稳定性 稳定 不稳定 稳定 稳定 稳定 生成质量 较高 高 较高 高 高 计算资源需求 中等 高 中等 高 高 模型复杂度 中等 高 中等 高 高 可解释性 较好 较差 较好 好 较差 灵活性 较低 高 高 高 高 对数据分布假设 较强 较弱 较强 较强 较弱 数据质量敏感性 较低 高 较低 较低 较低 生成速度 中等 中等 中等 中等 较慢 
下载: 导出CSV
表 2 不同重建算法结果定量比较
Table 2 Quantitative comparison between the results of different reconstruction algorithms
ESPIRiT LINDBERG EBMRec SAKE WKGM SVD-WKGM T1 GE brain 2D random R=4 39.08/0.933 38.98/0.961 40.17/0.968 41.54/0.952 40.67/0.969 43.85/0.970 2D random R=6 36.01/0.921 35.16/0.958 36.55/0.952 38.09/0.932 37.14/0.957 39.94/0.960 T2 transverse brain 2D Poisson R=4 31.74/0.819 32.87/0.901 33.19/0.915 33.91/0.896 33.35/0.907 34.58/0.917 2D Poisson R=10 28.95/0.798 26.17/0.822 29.59/0.839 29.75/0.823 29.17/0.823 31.69/0.841
下载: 导出CSV
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