Abstract
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 https://github.com/Netherlands-Cancer-Institute/CE-MRI.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings |
Editors | Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor |
Publisher | Springer Verlag |
Pages | 79-88 |
Number of pages | 10 |
Volume | 14226 LNCS |
ISBN (Print) | 9783031439896 |
DOIs | |
Publication status | Published - 1 Jan 2023 |
Event | 26th International Conference on Medical Image Computing and Computer Assisted Intervention - Vancouver, Canada Duration: 8 Oct 2023 → 12 Oct 2023 Conference number: 26 https://conferences.miccai.org/2023/en/ |
Publication series
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 14226 LNCS |
ISSN | 0302-9743 |
Conference
Conference | 26th International Conference on Medical Image Computing and Computer Assisted Intervention |
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Abbreviated title | MICCAI 2023 |
Country/Territory | Canada |
City | Vancouver |
Period | 8/10/23 → 12/10/23 |
Internet address |
Keywords
- Breast cancer
- Contrast-enhanced MRI
- Deep learning
- Diffusion-weighted imaging
- Multi-sequence fusion