Denoising diffusion probabilistic models (DDPMs) have achieved unprecedented success in computer vision. However, they remain underutilized in medical imaging, a field crucial for disease diagnosis and treatment planning. This is primarily due to the high computational cost associated with (1) the use of large number of time steps (e.g., 1,000) in diffusion processes and (2) the increased dimensionality of medical images, which are often 3D or 4D. Training a diffusion model on medical images typically takes days to weeks, while sampling each image volume takes minutes to hours. To address this challenge, we introduce Fast-DDPM, a simple yet effective approach capable of improving training speed, sampling speed, and generation quality simultaneously. Unlike DDPM, which trains the image denoiser across 1,000 time steps, Fast-DDPM trains and samples using only 10 time steps. The key to our method lies in aligning the training and sampling procedures. We introduced two efficient noise schedulers with 10 time steps: one with uniform time step sampling and another with non-uniform sampling. We evaluated Fast-DDPM across three medical image-to-image generation tasks: multi-image super-resolution, image denoising, and image-to-image translation. Fast-DDPM outperformed DDPM and current state-of-the-art methods based on convolutional networks and generative adversarial networks in all tasks. Additionally, Fast-DDPM reduced training time by a factor of 5 and sampling time by a factor of 100 compared to DDPM. Our code is publicly available at: https://github.com/mirthAI/Fast-DDPM.

Fast-DDPM是一种简单而有效的方法，可同时提高训练速度、采样速度和生成质量，通过仅使用10个时间步进行训练和采样，相比DDPM，Fast-DDPM能够在医学图像生成任务中优于基于卷积网络和生成对抗网络的当前最先进方法，并将训练时间缩短了5倍，采样时间缩短了100倍。

医学图像生成的快速去噪扩散概率模型