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Titlebook: Computational Molecular Magnetic Resonance Imaging for Neuro-oncology; Michael O. Dada,Bamidele O. Awojoyogbe Book 2021 The Editor(s) (if

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發(fā)表于 2025-3-21 18:35:27 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Computational Molecular Magnetic Resonance Imaging for Neuro-oncology
編輯Michael O. Dada,Bamidele O. Awojoyogbe
視頻videohttp://file.papertrans.cn/233/232848/232848.mp4
概述Facilitates fast, cost effective, reliable diagnosis, therapy and patient management.Provides computer programs that are easy to use and highly interactive, with fast and unambiguous data processing.A
叢書(shū)名稱(chēng)Biological and Medical Physics, Biomedical Engineering
圖書(shū)封面Titlebook: Computational Molecular Magnetic Resonance Imaging for Neuro-oncology; Michael O. Dada,Bamidele O. Awojoyogbe Book 2021 The Editor(s) (if
描述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 T.1. and T.2. 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.
出版日期Book 2021
關(guān)鍵詞Computational Magnetic Resonance Imaging; Neurocomputing; Neuro oncology; Bloch NMR flow equation; Machi
版次1
doihttps://doi.org/10.1007/978-3-030-76728-0
isbn_softcover978-3-030-76730-3
isbn_ebook978-3-030-76728-0Series ISSN 1618-7210 Series E-ISSN 2197-5647
issn_series 1618-7210
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Radiofrequency Identification System for Computational Diffusion Magnetic Resonance Imaging Based oflow equation and Hermite functions has been developed for detailed studies of processes taking place at the molecular level in living tissues. The goal is to explore the fundamental physics of RFID in MRI so that they can be further developed for individuals or hospitals to benefit from novel situa
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Radio-Frequency Identification System for Computational Magnetic Resonance Imaging of Blood Flow atle and described the physical quantities that affect the boundary layer thickness of blood flow system. We study the flow properties of the time-independent Bloch NMR flow equations which describe the dynamics of blood flow under the influence of radio frequency identification (RFID) system subject
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,A Computational MRI Based on Bloch’s NMR Flow Equation, MRI Fingerprinting and Python Deep Learning tumors based on origin and histopathologic grading. Tissue discrimination is a more significant challenge after treatment, because pseudo-progression, pseudo-response and development of radiation necrosis are real possibilities. In this chapter we have demonstrated practical ways in which computati
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,Application of “R” Machine Learning for Magnetic Resonance Relaxometry Data Representation and Clas simulations is now required to complement laboratory and clinical observations. Such mathematical models have the potential of providing insights into the imaging of the molecular interactions through the analysis of the behaviour of relaxation processes as observed on magnetic resonance (MR) scans
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Advanced Magnetic Resonance Image Processing and Quantitative Analysis in Avizo for Demonstrating R tumor progression on standard anatomical MRIs. Currently, it is clinically difficult to differentiate between tumor and necrosis; no imaging technique is able to provide a conclusive resolution of the problem. Furthermore, available imaging techniques require long follow-ups which are not only stre
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