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

arXiv: https://arxiv.org/abs/2307.00895 · HTML: https://mlsanity.com/paper/2307.00895/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)

## Built on by (most cited first, 8 of 8)

- [Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models](https://mlsanity.com/paper/2403.13890/) (2024)
- [Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation](https://mlsanity.com/paper/2311.10879/) (2023)
- [Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2409.18872/) (2024)
- [Interactive Gadolinium-Free MRI Synthesis: A Transformer with Localization Prompt Learning](https://mlsanity.com/paper/2503.01265/) (2025)
- [HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models](https://mlsanity.com/paper/2504.18405/) (2025)
- [SAFE-Diff: Scale-Aware Attention and Feature-Dispersive Diffusion with Uncertainty Estimation for Contrast-Enhanced Breast MRI Synthesis](https://mlsanity.com/paper/2605.25767/) (2026)
- [AA-ViT: Anatomically Aware Vision Transformer with Structural and Frequency Guidance for Contrast Enhanced Brain MRI Synthesis](https://mlsanity.com/paper/2607.07553/) (2026)
- [MIRAGE: Multi-scale Lesion-Informed Representation with Auxiliary Guidance for MRI Contrast Enhancement](https://mlsanity.com/paper/2607.19137/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/Netherlands-Cancer-Institute/CE-MRI.