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Multiparametric MRI
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- Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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- Christina Andica
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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- Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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- Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
Bibliographic Information
- Other Title
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- From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics
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Description
<jats:title>Abstract</jats:title><jats:p>With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.</jats:p>
Journal
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- Investigative Radiology
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Investigative Radiology 2023-02-22
Ovid Technologies (Wolters Kluwer Health)
