Magnetic resonance imaging (MRI) is the most sensitive technique for breast cancer detection among current clinical imaging modalities. Contrast-enhanced MRI (CE-MRI) provides superior differentiation between tumors and invaded healthy tissue, and has become an indispensable technique in the detection and evaluation of cancer. However, the use of gadolinium-based contrast agents (GBCA) to obtain CE-MRI may be associated with nephrogenic systemic fibrosis and may lead to bioaccumulation in the brain, posing a potential risk to human health. Moreover, and likely more important, the use of gadolinium-based contrast agents requires the cannulation of a vein, and the injection of the contrast media which is cumbersome and places a burden on the patient. To reduce the use of contrast agents, diffusion-weighted imaging (DWI) is emerging as a key imaging technique, although currently usually complementing breast CE-MRI. In this study, we develop a multi-sequence fusion network to synthesize CE-MRI based on T1-weighted MRI and DWIs. DWIs with different b-values are fused to efficiently utilize the difference features of DWIs. Rather than proposing a pure data-driven approach, we invent a multi-sequence attention module to obtain refined feature maps, and leverage hierarchical representation information fused at different scales while utilizing the contributions from different sequences from a model-driven approach by introducing the weighted difference module. The results show that the multi-b-value DWI-based fusion model can potentially be used to synthesize CE-MRI, thus theoretically reducing or avoiding the use of GBCA, thereby minimizing the burden to patients. Our code is available at \url{https://github.com/Netherlands-Cancer-Institute/CE-MRI}.

翻译：磁共振成像（MRI）是目前临床影像学手段中检测乳腺癌最灵敏的技术。对比增强MRI（CE-MRI）能够更好地区分肿瘤与受侵健康组织，已成为癌症检测与评估不可或缺的技术。然而，使用钆基对比剂（GBCA）获取CE-MRI可能与肾源性系统性纤维化相关，并可能在脑内产生生物蓄积，对人体健康构成潜在风险。更关键的是，钆基对比剂的使用需要进行静脉穿刺并注射对比介质，操作繁琐且增加患者负担。为减少对比剂使用，弥散加权成像（DWI）正成为关键成像技术，尽管目前通常作为乳腺CE-MRI的补充手段。本研究开发了一种多序列融合网络，基于T1加权MRI和DWI合成CE-MRI。通过融合不同b值的DWI图像，有效利用DWI的差异性特征。我们并未采用纯数据驱动方法，而是创新性设计了多序列注意力模块以获取精细化特征图，同时引入加权差分模块，从模型驱动角度融合不同尺度的分层表征信息，并利用不同序列的贡献度。结果表明，基于多b值DWI的融合模型有望用于合成CE-MRI，从而在理论上减少或避免GBCA的使用，最大限度减轻患者负担。我们的代码开源地址为：\url{https://github.com/Netherlands-Cancer-Institute/CE-MRI}。