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Titlebook: Digital Molecular Magnetic Resonance Imaging; Bamidele O. Awojoyogbe,Michael O. Dada Book 2024 The Editor(s) (if applicable) and The Autho

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书目名称Digital Molecular Magnetic Resonance Imaging
编辑Bamidele O. Awojoyogbe,Michael O. Dada
视频video
概述Presents computational and digital algorithms enabling the understanding of intricate structures in the human body.Discusses enhanced capabilities of MRI visualization to a level of anatomical visuali
丛书名称Series in BioEngineering
图书封面Titlebook: Digital Molecular Magnetic Resonance Imaging;  Bamidele O. Awojoyogbe,Michael O. Dada Book 2024 The Editor(s) (if applicable) and The Autho
描述.This book pushes the limits of conventional MRI visualization methods by completely changing the medical imaging landscape and leads to innovations that will help patients and healthcare providers alike. It enhances the capabilities of MRI anatomical visualization to a level that has never before been possible for researchers and clinicians. The computational and digital algorithms developed can enable a more thorough understanding of the intricate structures found within the human body, surpassing the constraints of traditional 2D methods. The Physics-informed Neural Networks as presented can enhance three-dimensional rendering for deeper understanding of the spatial relationships and subtle abnormalities of anatomical features and sets the stage for upcoming advancements that could impact a wider range of digital heath modalities. This book opens the door to ultra-powerful digital molecular MRI powered by quantum computing that can perform calculations that would take supercomputers millions of years..
出版日期Book 2024
关键词Bloch NMR; Physics-informed neural networks (PINNs); Magnetic resonance relaxometry; Machine learning m
版次1
doihttps://doi.org/10.1007/978-981-97-6370-2
isbn_softcover978-981-97-6372-6
isbn_ebook978-981-97-6370-2Series ISSN 2196-8861 Series E-ISSN 2196-887X
issn_series 2196-8861
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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New Methodology and Modelling in Magnetic Resonance Imaging,problems solved in this chapter are described by a Bloch related equation based on close observation of how the system usually behaves in the presence of NMR magnetic fields. A few numbers of realistic assumptions have been made in order to obtain a solution to each differential equation that has be
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Machine Learning Model for Diagnosis of Pulmonary Arterial Hypertension and Severe Aortic-Valve Ste) from Magnetic resonance relaxometry data that classifies the selected human diseases. The goal is to (i) develop machine learning models to improve the accuracy of predictions and decision-making in different models, (ii) use machine learning techniques for sorting across big datasets to identify
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,A Convolution Neural Network for Artificial Intelligence-Based Classification of Alzheimer’s Diseasn of medical pictures related to Alzheimer’s disease. The CNN model will be used to train MRI brain images. In order to construct and deploy the model, this chapter makes use of specialized application software and libraries including Jupyter notebook, Python 3, Keras, and TensorFlow. The trained mo
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Neural Radiance Fields (NeRFs) Technique to Render 3D Reconstruction of Magnetic Resonance Images,s, the thorough investigation of the intrinsic three-dimensional complexity of MRI data is restricted by conventional 2D visualization techniques. This restriction makes it difficult to interpret and comprehend complex anatomical details accurately, which reduces the possibility of making better cli
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