标题： 回归是你需要的全部用于医学图像翻译 显示英文标题

标题： Regression is all you need for medical image translation

摘要： 在有限的时间预算内获取信息丰富的图像对于医学影像是至关重要的。医学图像翻译（MIT）可以通过从获取的数据中生成合成图像来帮助增强和补充现有的数据集。虽然生成对抗网络（GANs）和扩散模型（DMs）在自然图像生成方面取得了显著的成功，但它们的创意性和图像逼真性并不一定适用于需要高度准确解剖信息的医学应用。事实上，模仿采集噪声或内容幻觉会妨碍临床实用性。 在这里，我们介绍YODA（You Only Denoise once - or Average），一种新颖的基于2.5D扩散的体积MIT框架。YODA结合了扩散和回归范式以产生逼真或无噪声的输出。此外，我们提出了期望近似（ExpA）DM采样，该方法借鉴了MRI信号平均的思想。ExpA采样抑制生成的噪声，从而消除了噪声影响图像质量评估的可能性。 通过在四个多样化的多模态数据集上进行广泛的实验——包括多对比度脑部MRI和盆腔MRI-CT——我们展示了扩散和回归采样在实践中产生了相似的结果。因此，扩散采样的计算开销并没有在医学信息翻译中提供系统性的优势。在此基础上，我们证明了YODA优于几种最先进的GAN和DM方法。值得注意的是，YODA生成的图像在几个下游任务中被证明可以与物理采集互换，甚至优于物理采集。我们的发现挑战了DMs在MIT中的假设优势，并为MIT在医学影像中的实际应用铺平了道路。 显示英文摘要

摘要： The acquisition of information-rich images within a limited time budget is crucial in medical imaging. Medical image translation (MIT) can help enhance and supplement existing datasets by generating synthetic images from acquired data. While Generative Adversarial Nets (GANs) and Diffusion Models (DMs) have achieved remarkable success in natural image generation, their benefits - creativity and image realism - do not necessarily transfer to medical applications where highly accurate anatomical information is required. In fact, the imitation of acquisition noise or content hallucination hinder clinical utility. Here, we introduce YODA (You Only Denoise once - or Average), a novel 2.5D diffusion-based framework for volumetric MIT. YODA unites diffusion and regression paradigms to produce realistic or noise-free outputs. Furthermore, we propose Expectation-Approximation (ExpA) DM sampling, which draws inspiration from MRI signal averaging. ExpA-sampling suppresses generated noise and, thus, eliminates noise from biasing the evaluation of image quality. Through extensive experiments on four diverse multi-modal datasets - comprising multi-contrast brain MRI and pelvic MRI-CT - we show that diffusion and regression sampling yield similar results in practice. As such, the computational overhead of diffusion sampling does not provide systematic benefits in medical information translation. Building on these insights, we demonstrate that YODA outperforms several state-of-the-art GAN and DM methods. Notably, YODA-generated images are shown to be interchangeable with, or even superior to, physical acquisitions for several downstream tasks. Our findings challenge the presumed advantages of DMs in MIT and pave the way for the practical application of MIT in medical imaging.