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 to optimize time-step utilization. Specifically, 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 the training time to 0.2x and the sampling time to 0.01x compared to DDPM. Our code is publicly available at: https://github.com/mirthAI/Fast-DDPM.

翻译：去噪扩散概率模型（DDPMs）在计算机视觉领域取得了前所未有的成功。然而，在医学成像这一对疾病诊断和治疗规划至关重要的领域中，其应用仍然不足。这主要是由于以下两点带来的高计算成本：（1）扩散过程中使用大量时间步（例如1000步），以及（2）医学图像（通常是3D或4D）维度的增加。在医学图像上训练扩散模型通常需要数天到数周，而对每个图像体进行采样则需要数分钟到数小时。为应对这一挑战，我们提出了Fast-DDPM，这是一种简单而有效的方法，能够同时提升训练速度、采样速度以及生成质量。与DDPM需要在1000个时间步上训练图像去噪器不同，Fast-DDPM仅使用10个时间步进行训练和采样。我们方法的关键在于对齐训练和采样过程以优化时间步的利用。具体而言，我们引入了两种高效的、包含10个时间步的噪声调度器：一种采用均匀时间步采样，另一种采用非均匀采样。我们在三个医学图像到图像生成任务上评估了Fast-DDPM：多图像超分辨率、图像去噪和图像到图像转换。在所有任务中，Fast-DDPM均优于DDPM以及当前基于卷积网络和生成对抗网络的先进方法。此外，与DDPM相比，Fast-DDPM将训练时间减少至0.2倍，采样时间减少至0.01倍。我们的代码公开于：https://github.com/mirthAI/Fast-DDPM。