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Published on: December 15, 2014
Ultrafast MRI and diffusion-weighted imaging: a review of morphological evaluation and image quality in breast MRI
Maya Honda1,2, Masako Kataoka3, Mami Iima4,5
1Preemptive Medicine and Lifestyle-Related Disease Research Center, Kyoto University Hospital, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan. mayah.217@gmail.com.
Abstract:
Breast magnetic resonance imaging (MRI) is an essential tool for evaluating breast lesions, with dynamic contrast-enhanced (DCE) MRI being considered the reference standard. However, conventional DCE-MRI has limitations, including long scan times, high costs, and variable specificity leading to unnecessary biopsies. Emerging techniques such as ultrafast dynamic contrast-enhanced (UF-DCE) MRI and diffusion-weighted imaging (DWI) have recently received attention as possible alternatives. UF-DCE MRI achieves high temporal resolution, improving lesion conspicuity while reducing motion artifacts and background parenchymal enhancement. Advanced acceleration methods, including view sharing and compressed sensing, enhance temporal resolution while maintaining image quality. DWI, a contrast agent-free technique that can be used to assess tissue cellularity, provides high specificity in the differentiation of benign from malignant lesions. Recent developments in DWI, such as readout-segmented echo planar imaging, reduced field of view, and simultaneous multi-slice techniques, have significantly improved spatial resolution and reduced artifacts. These advancements enable morphological assessment and hold the potential for replacing or complementing conventional DCE-MRI, thus reducing patient burden and improving accessibility. Future research should focus on optimizing imaging protocols and integrating artificial intelligence to enhance diagnostic performance. This review discusses the principles, technological advancements, and clinical applications of UF-DCE MRI and DWI, with a particular focus on morphological evaluation and image quality, emphasizing their role in improving the efficiency of breast imaging while maintaining accuracy.
Insights
Ultrafast dynamic contrast-enhanced (UF-DCE) MRI and diffusion-weighted imaging (DWI) offer faster, more accurate breast lesion evaluation. These advanced MRI techniques improve efficiency and accessibility, potentially replacing or complementing conventional methods.
Area of Science:
- Radiology
- Medical Imaging
- Oncology
Background:
- Conventional dynamic contrast-enhanced MRI (DCE-MRI) is standard for breast lesion evaluation but has limitations like long scan times and variable specificity.
- Emerging techniques, ultrafast DCE-MRI (UF-DCE) and diffusion-weighted imaging (DWI), show promise as alternatives.
- UF-DCE MRI offers high temporal resolution, reducing artifacts and improving lesion conspicuity.
Purpose of the Study:
- To review the principles, technological advancements, and clinical applications of UF-DCE MRI and DWI.
- To emphasize their role in morphological evaluation and image quality for breast imaging.
- To highlight their potential to improve diagnostic performance, efficiency, and accessibility.
Main Methods:
- Discussion of advanced acceleration methods in UF-DCE MRI, including view sharing and compressed sensing.
- Review of DWI advancements, such as readout-segmented echo planar imaging and simultaneous multi-slice techniques.
- Focus on morphological assessment and image quality improvements in both techniques.
Main Results:
- UF-DCE MRI enhances temporal resolution and reduces motion artifacts and background enhancement.
- DWI provides high specificity for differentiating benign from malignant lesions using a contrast-agent-free approach.
- Advancements in DWI significantly improve spatial resolution and reduce artifacts, enabling better morphological assessment.
Conclusions:
- UF-DCE MRI and DWI hold potential to replace or complement conventional DCE-MRI, reducing patient burden.
- These techniques can improve the efficiency and accessibility of breast imaging while maintaining diagnostic accuracy.
- Future research should focus on protocol optimization and integrating artificial intelligence for enhanced diagnostic performance.
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