Deep generative modeling has emerged as a powerful tool for synthesizing realistic medical images, driving advances in medical image analysis, disease diagnosis, and treatment planning. This chapter explores various deep generative models for 3D medical image synthesis, with a focus on Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Models (DDMs). We discuss the fundamental principles, recent advances, as well as strengths and weaknesses of these models and examine their applications in clinically relevant problems, including unconditional and conditional generation tasks like image-to-image translation and image reconstruction. We additionally review commonly used evaluation metrics for assessing image fidelity, diversity, utility, and privacy and provide an overview of current challenges in the field.

本研究针对当前医学图像合成中的深度生成模型面临的挑战进行了探讨，尤其是变分自编码器（VAEs）、生成对抗网络（GANs）和去噪扩散模型（DDMs）。通过分析这些模型的基本原理及其在临床相关问题中的应用，研究指出了其在图像生成任务中的优势与局限性，旨在为未来的医学图像分析提供新思路。

深度生成模型在3D医学图像合成中的应用