Computational molecular magnetic resonance imaging for neuro-oncology / Michael O. Dada, Bamidele O. Awojoyogbe.
Dada, Michael O., author.; Awojoyogbe, Bamidele O., author
2021
RC78.7.N83 D33 2021
Available Online

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Title
Computational molecular magnetic resonance imaging for neuro-oncology / Michael O. Dada, Bamidele O. Awojoyogbe.
Author
Dada, Michael O., author.
ISBN
9783030767280 (electronic bk.)
3030767280 (electronic bk.)
3030767272
9783030767273
DOI
https://doi.org/10.1007/978-3-030-76728-0
Published
Cham, Switzerland : Springer, 2021.
Language
English
Description
1 online resource
Item Number
10.1007/978-3-030-76728-0 doi
Call Number
RC78.7.N83 D33 2021
Dewey Decimal Classification
616.07/548
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 medial 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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed August 11, 2021).
Added Author
Awojoyogbe, Bamidele O., author
Series
Biological and medical physics, biomedical engineering.
Available in Other Form
Computational molecular magnetic resonance imaging for neuro-oncology.
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Chapter 1. General Introduction
Chapter 2. Fundamental Of Nmr
Chapter 3. Computational Diffusion Magnetic Resonance Imaging
Chapter 4. Radiofrequency Identification (Rfid) System For Computational Magnetic Resonance Imaging Of Blood Flow At Suction Points
Chapter 5. A Computational Magnetic Resonance Imaging Based On Bloch Nmr Flow Equation, Mri Finger Printing, Python Deep Learning For The Classification Of Adult Brain Tumours
Chapter 6. Analysis Of Hydrogen-Like Ions For Neurocomputing Based On Bloch Nmr Flow Equation
Chapter 7. Quantum Mechanical Model Of Bloch Nmr Flow Equations For The Transport Analysis Of Quantm-Drugs In Microscopic Blood Vessels Applicable In Nanomedicine
Chapter 8. Application Of Machine Learning For Magnetic Resonance Relaxometry Data-Representation And Classification Of Human Brain Tumours
Chapter 9. Advanced Magnetic Resonance Image Processing And Quantitative Analysis In Avizo For Demonstrating Radiomic Contrast Between Radiation Necrosis And Tumor Progression
Chapter 10. Computational Analysis of Magnetic Resonance Imaging Contrast Agents and their Physico-Chemical Variables
Chapter 11. General Conclusion.

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