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Computational Molecular Magnetic Resonance Imaging for Neuro-oncology (Hardcover, 1st ed. 2021): Michael O. Dada, Bamidele O.... Computational Molecular Magnetic Resonance Imaging for Neuro-oncology (Hardcover, 1st ed. 2021)
Michael O. Dada, Bamidele O. Awojoyogbe
R3,847 Discovery Miles 38 470 Ships in 18 - 22 working days

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.

Computational Molecular Magnetic Resonance Imaging for Neuro-oncology (Paperback, 1st ed. 2021): Michael O. Dada, Bamidele O.... Computational Molecular Magnetic Resonance Imaging for Neuro-oncology (Paperback, 1st ed. 2021)
Michael O. Dada, Bamidele O. Awojoyogbe
R3,820 Discovery Miles 38 200 Ships in 18 - 22 working days

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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