解剖感知的乳腺MRI增强前后图像合成
Anatomy-Aware Synthesis of Post-Contrast Breast MRI from Pre-Contrast Images
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中文总结 AI 辅助
提出一种解剖感知的深度学习框架，通过整合乳腺掩膜、病灶和BPE区域监督，从增强前MRI合成增强后图像，在649名患者数据上优于现有方法，并支持无对比剂工作流程。
中文摘要 AI 辅助
我们开发了一种解剖感知的深度学习框架，用于从增强前图像合成增强后乳腺MRI，重点强调肿瘤和背景实质增强（BPE）区域。这项回顾性研究纳入了649名患者，共6,251对增强前和增强后图像。该框架将乳腺掩膜一致性、病灶区域监督和BPE区域监督整合到一个图像到图像转换模型中。评估包括定量图像质量指标、由两名乳腺放射科医生进行的读者研究以及下游Ki-67分类。所提出的方法在整体图像和区域评估中均优于Pix2Pix、Pix2PixHD、基于扩散的合成和掩膜监督基线。Ki-67分类显示，在真实和合成图像训练及测试设置之间没有统计学显著差异，尽管这并不确立等价性。这些发现表明，解剖感知的监督提高了合成保真度，并支持进一步研究合成增强后MRI用于无对比剂成像工作流程。
英文摘要
We developed an anatomy-aware deep learning framework to synthesize post-contrast breast MRI from pre-contrast images, emphasizing tumor and background parenchymal enhancement (BPE) regions. This retrospective study included 649 patients with 6,251 paired pre-contrast and post-contrast images. The framework integrates breast mask consistency, lesion-region supervision, and BPE-region supervision into an image-to-image translation model. Evaluation included quantitative image quality metrics, a reader study with two breast radiologists, and downstream Ki-67 classification. The proposed method outperformed Pix2Pix, Pix2PixHD, diffusion-based synthesis, and mask-supervised baselines in whole-image and regional evaluations. Ki-67 classification showed no statistically significant performance differences across real- and synthetic-image training and testing settings, although this does not establish equivalence. These findings suggest that anatomy-aware supervision improves synthesis fidelity and support further investigation of synthetic post-contrast MRI for contrast-free imaging workflows.
