让医学影像增强只改该改的部分，避免误判和漏诊。
See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement
- 基于器官结构和对比度动态，精准识别需增强区域。
- 在多期CT上无需配准，直接学习增强效果，提升一致性。
- 适合临床医生使用，能提升肿瘤检测准确率10%。
图像增强可提升视觉质量并揭示原始图像中难以察觉的细节。在医学影像中，它有助于临床决策，但现有模型常过度处理，导致器官失真、产生假象或遗漏小肿瘤，因缺乏解剖结构与对比度变化的理解。本文提出SMILE，一种解剖结构感知的扩散模型，学习器官形态及对比剂摄取规律，仅增强临床相关区域，其余区域保持不变。SMILE引入三项关键设计：(1) 结构感知监督，遵循真实器官边界与对比度模式；(2) 无配准学习，直接处理未对齐的多期CT扫描；(3) 统一推理，实现各对比相位下快速一致的增强。在六个外部数据集上，SMILE在图像质量上优于现有方法（SSIM高14.2%，PSNR高20.6%，FID优50%），且生成更具解剖准确性与诊断意义的图像。此外，其还能提升非对比增强CT中的癌症检测能力，使F1得分最高提升10%。
原文摘要 · Abstract (English)
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.
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