Abstract
Image enhancement improves visual quality and helps reveal details that are hard to see in the original image. In medical imaging, it can support clinical decision-making, but current models often over-edit. This can distort organs, create false findings, and miss small tumors because these models do not understand anatomy or contrast dynamics. We propose SMILE, an anatomy-aware diffusion model that learns how organs are shaped and how they take up contrast. It enhances only clinically relevant regions while leaving all other areas unchanged. SMILE introduces three key ideas: (1) structure-aware supervision that follows true organ boundaries and contrast patterns; (2) registration-free learning that works directly with unaligned multi-phase CT scans; (3) unified inference that provides fast and consistent enhancement across all contrast phases. Across six external datasets, SMILE outperforms existing methods in image quality (14.2% higher SSIM, 20.6% higher PSNR, 50% better FID) and in clinical usefulness by producing anatomically accurate and diagnostically meaningful images. SMILE also improves cancer detection from non-contrast CT, raising the F1 score by up to 10 percent.
Abstract (translated)
图像增强可以提高视觉质量，并帮助揭示原图中难以察觉的细节。在医学成像领域，它可以支持临床决策制定，但目前的模型常常过度编辑。这会导致器官变形、产生假阳性结果以及遗漏微小肿瘤，因为这些模型不理解解剖结构或对比度变化动态。我们提出了SMILE，这是一种了解器官形状和对比度吸收方式的解剖学感知扩散模型。它仅增强临床相关的区域，而其他所有区域保持不变。 SMILE引入了三个关键概念：（1）结构感知监督，遵循真正的器官边界和对比模式；（2）无需配准的学习，在不需对齐的情况下直接处理多相CT扫描；（3）统一推理，提供快速且一致的跨所有对比度阶段图像增强效果。在六个外部数据集中，SMILE在图像质量方面超越了现有方法（SSIM提高14.2%，PSNR提高20.6%，FID改善50%），并且通过生成解剖学准确和诊断有意义的图像提高了临床实用性。此外，SMILE还能提升非对比CT检测癌症的能力，最高可将F1分数提高10个百分点。
URL
https://arxiv.org/abs/2512.07251
