Diffusion models have demonstrated superior fidelity for medical image-to-image translation, but their extension to high-resolution 3D volumes is severely constrained by prohibitive computational cost and GPU memory requirements. Existing memory-efficient strategies often compromise global volumetric consistency or fine anatomical detail. In this work, we propose the Pixel Puzzling Diffusion Model (PPDM), a simple and effective framework for memory- and speed-efficient 3D medical image translation. PPDM introduces a reversible pixel puzzle-unpuzzle operator that trades spatial resolution for channel dimensionality, substantially reducing activation memory while preserving global context. To further improve efficiency and stability, we adopt a direct bridge diffusion formulation that starts from the conditional input rather than pure noise, enabling the model to focus on task-relevant residuals. In addition, a puzzle-gradient loss is incorporated to enforce spatial coherence and suppress grid-like artifacts introduced by spatial rearrangement. We evaluate PPDM on multiple challenging 3D medical image translation tasks, including low-count PET denoising, joint PET denoising and attenuation correction, and cross-modal MRI translation. Across all tasks, PPDM consistently matches or outperforms full 3D diffusion models while reducing training GPU memory usage by up to an order of magnitude and significantly accelerating inference, and it outperforms existing memory-efficient diffusion approaches based on latent compression or frequency decomposition. These results demonstrate that PPDM provides a practical and scalable solution for high-fidelity 3D diffusion-based medical image translation under limited computational resources.

翻译：扩散模型在医学图像到图像的翻译中展现出了卓越的保真度，但其向高分辨率三维体积图像的扩展受到高昂计算成本和GPU内存需求的严重制约。现有的内存高效策略常常牺牲全局体积一致性或精细的解剖细节。在这项工作中，我们提出了像素拼图扩散模型（PPDM），一个用于内存和速度高效的三维医学图像翻译的简单而有效的框架。PPDM引入了一个可逆的像素拼图-解拼图算子，该算子将空间分辨率替换为通道维度，从而在保留全局上下文的同时大幅减少激活内存。为了进一步提升效率和稳定性，我们采用了一种直接桥接扩散公式，该公式从条件输入而非纯噪声开始，使模型能够专注于任务相关的残差。此外，我们引入了拼图梯度损失来强制空间连贯性并抑制由空间重排引入的网格状伪影。我们在多个具有挑战性的三维医学图像翻译任务上评估了PPDM，包括低计数PET去噪、联合PET去噪与衰减校正，以及跨模态MRI翻译。在所有任务中，PPDM consistently能够匹配或超越全三维扩散模型的性能，同时将训练GPU内存使用量降低高达一个数量级，并显著加速推理，且性能优于基于潜在压缩或频率分解的现有内存高效扩散方法。这些结果表明，PPDM在有限计算资源下为基于扩散的高保真三维医学图像翻译提供了一种实用且可扩展的解决方案。