Clinical Application of Artificial Intelligence in Breast MRI
Breast MRI is the most sensitive imaging modality for detecting breast cancer. However, its widespread use is limited by factors such as extended examination times, need for contrast agents, and susceptibility to motion artifacts. Artificial intelligence (AI) has emerged as a promising solution f...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
The Korean Society of Radiology
2025-03-01
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| Series: | Journal of the Korean Society of Radiology |
| Subjects: | |
| Online Access: | https://doi.org/10.3348/jksr.2025.0012 |
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| Summary: | Breast MRI is the most sensitive imaging modality for detecting breast cancer. However, its
widespread use is limited by factors such as extended examination times, need for contrast
agents, and susceptibility to motion artifacts. Artificial intelligence (AI) has emerged as a
promising solution for these challenges by enhancing the efficiency and accuracy of breast
MRI in multiple domains. AI-driven image reconstruction techniques have significantly reduced
scan times while preserving image quality. This method outperforms traditional parallel
imaging and compressed sensing. AI has also shown great promise for lesion classification
and segmentation, with convolutional neural networks and U-Net architectures
improving the differentiation between benign and malignant lesions. AI-based segmentation
methods enable accurate tumor detection and characterization, thereby aiding personalized
treatment planning. An AI triaging system has demonstrated the potential to streamline
workflow efficiency by identifying low-suspicion cases and reducing the workload of
radiologists. Another promising application is synthetic breast MR image generation, which
aims to generate contrast enhanced images from non-contrast sequences, thereby improving
accessibility and patient safety. Further research is required to validate AI models across
diverse populations and imaging protocols. As AI continues to evolve, it is expected to play
an important role in the optimization of breast MRI. |
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| ISSN: | 2951-0805 |