Author: Farah Deeba
Publisher: Springer Nature
ISBN: 3030615987
Category : Computers
Languages : en
Pages : 170
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Book Description
This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
Author: S. Kevin Zhou
Publisher: Academic Press
ISBN: 0128165863
Category : Computers
Languages : en
Pages : 1074
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Book Description
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. - Presents the key research challenges in medical image computing and computer-assisted intervention - Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society - Contains state-of-the-art technical approaches to key challenges - Demonstrates proven algorithms for a whole range of essential medical imaging applications - Includes source codes for use in a plug-and-play manner - Embraces future directions in the fields of medical image computing and computer-assisted intervention
Author: Nandinee Haq
Publisher: Springer
ISBN: 9783030885519
Category : Computers
Languages : en
Pages : 142
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Book Description
This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
Author: Ge Wang
Publisher: Programme: Iop Expanding Physi
ISBN: 9780750322140
Category : Technology & Engineering
Languages : en
Pages : 250
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Book Description
Machine learning represents a paradigm shift in tomographic imaging, and image reconstruction is a new frontier of machine learning. This book will meet the needs of those who want to catch the wave of smart imaging. The book targets graduate students and researchers in the imaging community. Open network software, working datasets, and multimedia will be included. The first of its kind in the emerging field of deep reconstruction and deep imaging, Machine Learning for Tomographic Imaging presents the most essential elements, latest progresses and an in-depth perspective on this important topic.
Author: Mehmet Akcakaya
Publisher: Academic Press
ISBN: 012822746X
Category : Science
Languages : en
Pages : 518
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Book Description
Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. - Explains the underlying principles of MRI reconstruction, along with the latest research - Gives example codes for some of the methods presented - Includes updates on the latest developments, including compressed sensing, tensor-based reconstruction and machine learning based reconstruction
Author: Gengsheng Zeng
Publisher: Springer Science & Business Media
ISBN: 3642053688
Category : Technology & Engineering
Languages : en
Pages : 204
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Book Description
"Medical Image Reconstruction: A Conceptual Tutorial" introduces the classical and modern image reconstruction technologies, such as two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. This book presents both analytical and iterative methods of these technologies and their applications in X-ray CT (computed tomography), SPECT (single photon emission computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging). Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections, Katsevich's cone-beam filtered backprojection algorithm, and reconstruction with highly undersampled data with l0-minimization are also included. This book is written for engineers and researchers in the field of biomedical engineering specializing in medical imaging and image processing with image reconstruction. Gengsheng Lawrence Zeng is an expert in the development of medical image reconstruction algorithms and is a professor at the Department of Radiology, University of Utah, Salt Lake City, Utah, USA.
Author: Minghua Xu
Publisher: CRC PressI Llc
ISBN: 9781439882245
Category : Technology & Engineering
Languages : en
Pages : 400
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Book Description
Author: Mithun Kuniyil Ajith Singh
Publisher: Springer Nature
ISBN: 9811539847
Category : Science
Languages : en
Pages : 393
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Book Description
This book highlights the use of LEDs in biomedical photoacoustic imaging. In chapters written by key opinion leaders in the field, it covers a broad range of topics, including fundamentals, principles, instrumentation, image reconstruction and data/image processing methods, preclinical and clinical applications of LED-based photoacoustic imaging. Apart from preclinical imaging studies and early clinical pilot studies using LED-based photoacoustics, the book includes a chapter exploring the opportunities and challenges of clinical translation from an industry perspective. Given its scope, the book will appeal to scientists and engineers in academia and industry, as well as medical experts interested in the clinical applications of photoacoustic imaging.
Author: S. Kevin Zhou
Publisher: Academic Press
ISBN: 0323858880
Category : Computers
Languages : en
Pages : 544
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Book Description
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
Author: Markus Haltmeier
Publisher:
ISBN: 9789811203671
Category :
Languages : en
Pages : 250
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Book Description
Many problems in science, engineering and medicine follow an inverse approach to problem by observations the output data to calculate or predict the inputs should be to generated the responses: for example, calculating an image in X-ray computed tomography, source reconstruction in acoustics, or calculating the density of the Earth from measurements of its gravity field. Recent advance in deep learning-based algorithms has emerged as a novel paradigm for image processing.This book compile the state-of-the-art approaches for solving inverse problems by deep learning; from basic concetps to deep learning. algorithms in image processing. It serves as an introduction to researchers working in image processing, and pattern recognition, as well as, students undertaken research in signal processing and AI.The book will include the following:
