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Computational Molecular Magnetic Resonance Imaging for Neuro-oncology.

By: Contributor(s): Material type: TextTextLanguage: English Series: Biological and Medical Physics, Biomedical EngineeringPublication details: Department of Physics Federal University of Technology Minna, Nigeria | Springer Nature Switzerland AG | 2021.Description: 412 Pages; Includes IndexISBN:
  • 978-3-030-76728-0 (eBook)
  • 978-3-030-76727-3
ISSN:
  • 1618-7210
  • 2197-5647 (electronic)
Subject(s): Online resources: Summary: Based on the analytical methods and the computer programs presented in this book, all that may be needed to perform MRI tissue diagnosis is the availability of relaxometric data and simple computer program proficiency. These programs are easy to use, highly interactive and the data processing is fast and unambiguous. Laboratories (with or without sophisticated facilities) can perform computational magnetic resonance diagnosis with only T1 and T2 relaxation data. The results have motivated the use of data to produce data-driven predictions required for machine learning, artificial intelligence (AI) and deep learning for multidisciplinary and interdisciplinary research. Consequently, this book is intended to be very useful for students, scientists, engineers, the medical personnel and researchers who are interested in developing new concepts for deeper appreciation of computational magnetic resonance imaging for medical diagnosis, prognosis, therapy and management of tissue diseases.
Item type: E-BOOKS
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Item type Current library URL Status Barcode
E-BOOKS MWALIMU NYERERE LEARNING RESOURCES CENTRE-CUHAS BUGANDO Link to resource Not for loan 20241028110930.0
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Based on the analytical methods and the computer programs presented in this book, all that may be needed to perform MRI tissue diagnosis is the availability of relaxometric data and simple computer program proficiency. These programs are easy to use, highly interactive and the data processing is fast and unambiguous. Laboratories (with or without sophisticated facilities) can perform computational magnetic resonance diagnosis with only T1 and T2 relaxation data. The results have motivated the use of data to produce data-driven predictions required for machine learning, artificial intelligence (AI) and deep learning for multidisciplinary and interdisciplinary research. Consequently, this book is intended to be very useful for students, scientists, engineers, the medical personnel and researchers who are interested in developing new concepts for deeper appreciation of computational magnetic resonance imaging for medical diagnosis, prognosis, therapy and management of tissue diseases.

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