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Magnetic resonance imaging (MRI) is the most sensitive technique for breastcancer detection among current clinical imaging modalities. Contrast-enhancedMRI (CE-MRI) provides superior differentiation between tumors and invadedhealthy tissue, and has become an indispensable technique in the detection andevaluation of cancer. However, the use of gadolinium-based contrast agents(GBCA) to obtain CE-MRI may be associated with nephrogenic systemic fibrosisand may lead to bioaccumulation in the brain, posing a potential risk to humanhealth. Moreover, and likely more important, the use of gadolinium-basedcontrast agents requires the cannulation of a vein, and the injection of thecontrast media which is cumbersome and places a burden on the patient. Toreduce the use of contrast agents, diffusion-weighted imaging (DWI) is emergingas a key imaging technique, although currently usually complementing breastCE-MRI. In this study, we develop a multi-sequence fusion network to synthesizeCE-MRI based on T1-weighted MRI and DWIs. DWIs with different b-values arefused to efficiently utilize the difference features of DWIs. Rather thanproposing a pure data-driven approach, we invent a multi-sequence attentionmodule to obtain refined feature maps, and leverage hierarchical representationinformation fused at different scales while utilizing the contributions fromdifferent sequences from a model-driven approach by introducing the weighteddifference module. The results show that the multi-b-value DWI-based fusionmodel can potentially be used to synthesize CE-MRI, thus theoretically reducingor avoiding the use of GBCA, thereby minimizing the burden to patients. Ourcode is available at\url{https://github.com/Netherlands-Cancer-Institute/CE-MRI}.

Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism