Magnetic Resonance Imaging

Recording, Reconstruction and Assessment

V. Rajinikanth, Nilanjan Dey

• Publisher: Academic Press
• Year: 2022
• Print ISBN: 9780128234013
• eBook ISBN: 9780128234020
• Topic: Technology & Engineering
• Subtopic: Engineering General
• Index: Technology & Engineering

Magnetic Resonance Imaging: Recording, Reconstruction and Assessment gives a detailed overview of magnetic resonance imaging (MRI), along with its applications and challenges. The book explores the abnormalities in internal human organs using MRI techniques while also featuring case studies that illustrate measures used. In addition, it explores precautionary measures used during MRI based imaging, the selection of appropriate contrast agents, and the selection of the appropriate modality during the image registration. Sections introduce medical imaging, the use of MRI in brain, cardiac, lung and kidney detection, and also discuss both 2D and 3D imaging techniques and various MRI modalities. This volume will be of interest to researchers, engineers and medical professionals involved in the development and use of MRI systems. - Discusses challenges and issues faced, as well as safety precautions to be followed - Features case studies with benchmark MRIs existing in the literature - Introduces computer-based assessment (Machine Learning and Deep Learning) of the MRI based on its 2D slices

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Preface
• List of Illustrations
• List of Tables
• 1 : Introduction to image-assisted disease screening
• 2 : Magnetic resonance imaging: recording and reconstruction
• 3 : Image processing methods to enhance disease information in MRI slices
• 4 : A study of the segmentation of tumor in breast MRI using entropy thresholding and the Mayfly algorithm
• 5 : Abnormality detection in heart MRI with spotted hyena algorithm-supported Kapur/Otsu thresholding and level set segmentation
• 6 : CNN-based segmentation of brain tumor from T2-weighted MRI slices
• 7 : Automated detection of ischemic stroke with brain MRI using machine learning and deep learning features
• Index
• A