Ultrafast MRI and diffusion-weighted imaging: a review of morphological evaluation and image quality in breast MRI.

Journal: Japanese journal of radiology
Published Date:

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.

Authors

  • Maya Honda
    From the Department of Fundamental Development for Advanced Low Invasive Diagnostic Imaging, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan (M.I.); Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan (M.I., M.K., M.H., Y.N.); A.I. System Research, Kyoto, Japan (R.M.); Kyoto University Faculty of Medicine, Kyoto, Japan (K.T., T.Y.); Department of Diagnostic Radiology, Kyoto City Hospital, Kyoto, Japan (A.M.); Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan (M.H.); e-Growth, Kyoto, Japan (K.I.); and Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan (M.T.).
  • Masako Kataoka
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University, Graduate School of Medicine, Kyoto, Japan.
  • Mami Iima
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University, Graduate School of Medicine, Kyoto, Japan.
  • Marcel Dominik Nickel
    MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany.
  • Tsutomu Okada
    Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan.
  • Yuji Nakamoto
    Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University.

Keywords

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