用AI生成乳腺MRI对比增强图像，无创辅助癌症诊断。
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
- 用条件生成对抗网络从非增强MRI预测多时相DCE-MRI序列。
- 生成图像在肿瘤分割任务中表现良好，与真实数据接近。
- 适合对造影剂过敏或不能使用造影剂的患者群体。
本文提出一种虚拟对比增强方法，利用条件生成对抗网络（cGAN）从非增强MRI预测出多时相动态对比增强MRI（DCE-MRI）图像序列，实现无需注射造影剂即可完成肿瘤定位与特征分析，降低健康风险。通过自定义的多指标尺度聚合评估量（SAMe）进行定性与定量评估，并在肿瘤分割下游任务中验证生成图像的实用性。结果表明，该方法能生成逼真且有效的DCE-MRI序列，尤其适用于无法使用传统造影剂的患者，具有提升乳腺癌诊疗潜力。
原文摘要 · Abstract (English)
This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition. Using a conditional generative adversarial network, we predict DCE-MRI images, including jointly-generated sequences of multiple corresponding DCE-MRI timepoints, from non-contrast-enhanced MRIs, enabling tumor localization and characterization without the associated health risks. Furthermore, we qualitatively and quantitatively evaluate the synthetic DCE-MRI images, proposing a multi-metric Scaled Aggregate Measure (SAMe), assessing their utility in a tumor segmentation downstream task, and conclude with an analysis of the temporal patterns in multi-sequence DCE-MRI generation. Our approach demonstrates promising results in generating realistic and useful DCE-MRI sequences, highlighting the potential of virtual contrast enhancement for improving breast cancer diagnosis and treatment, particularly for patients where contrast agent administration is contraindicated.
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