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|a 10.1007/978-3-030-76728-0
|2 doi
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|a (OCoLC)1262726344
|z (OCoLC)1263027933
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|b D33 2021
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|a Dada, Michael O.,
|e author.
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1 |
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|a Computational molecular magnetic resonance imaging for neuro-oncology /
|c Michael O. Dada, Bamidele O. Awojoyogbe.
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| 264 |
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1 |
|a Cham, Switzerland :
|b Springer,
|c 2021.
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| 300 |
|
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|a 1 online resource
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| 336 |
|
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|a text
|b txt
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|2 rdamedia
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|a online resource
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|a Biological and Medical Physics, Biomedical Engineering
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0 |
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|a 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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| 520 |
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|a 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.
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| 504 |
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|a Includes bibliographical references and index.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed August 11, 2021).
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| 650 |
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|a Magnetic resonance imaging.
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| 650 |
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|a Brain
|x Cancer
|x Magnetic resonance imaging.
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| 650 |
|
7 |
|a Magnetic resonance imaging
|2 fast
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| 700 |
1 |
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|a Awojoyogbe, Bamidele O.,
|e author
|0 (orcid)0000-0003-3699-0897
|1 https://orcid.org/0000-0003-3699-0897
|
| 758 |
|
|
|i has work:
|a Computational molecular magnetic resonance imaging for neuro-oncology (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGkwk8JbRHbYQRq7bqp773
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
8 |
|i Print version:
|a Dada, Michael O.
|t Computational molecular magnetic resonance imaging for neuro-oncology.
|d Cham, Switzerland : Springer, 2021
|z 3030767272
|z 9783030767273
|w (OCoLC)1247665753
|
| 830 |
|
0 |
|a Biological and medical physics, biomedical engineering.
|
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4 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-76728-0
|y Click for online access
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|a SPRING-PHYSICS2021
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