UID:
almahu_9948342718602882
Format:
1 online resource (various pagings) :
,
illustrations (some color).
ISBN:
9780750322164
,
9780750322157
Series Statement:
IOP ebooks. [2020 collection]
Content:
The area of machine learning, especially deep learning, has exploded in recent years, producing advances in everything from speech recognition and gaming to drug discovery. Tomographic imaging is another major area that is being transformed by machine learning, and its potential to revolutionise medical imaging is highly significant. Written by active researchers in the field, Machine Learning for Tomographic Imaging presents a unified overview of deep-learning-based tomographic imaging. Key concepts, including classic reconstruction ideas and human vision inspired insights, are introduced as a foundation for a thorough examination of artificial neural networks and deep tomographic reconstruction. X-ray CT and MRI reconstruction methods are covered in detail, and other medical imaging applications are discussed as well. An engaging and accessible style makes this book an ideal introduction for those in applied disciplines, as well as those in more theoretical disciplines who wish to learn about application contexts. Hands-on projects are also suggested, and links to open source software, working datasets, and network models are included. Part of Series in Physics and Engineering in Medicine and Biology.
Note:
"Version: 20191201"--Title page verso.
,
part I. Background. 1. Background knowledge -- 1.1. Imaging principles and a priori information
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2. Tomographic reconstruction based on a learned dictionary -- 2.1. Prior information guided reconstruction -- 2.2. Single-layer neural network -- 2.3. CT reconstruction via dictionary learning -- 2.4. Final remarks
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3. Artificial neural networks -- 3.1. Basic concepts -- 3.2. Training, validation, and testing of an artificial neural network -- 3.3. Typical artificial neural networks
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part II. X-ray computed tomography. 4. X-ray computed tomography -- 4.1. X-ray data acquisition -- 4.2. Analytical reconstruction -- 4.3. Iterative reconstruction -- 4.4. CT scanner
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5. Deep CT reconstruction -- 5.1. Introduction -- 5.2. Image domain processing -- 5.3. Data domain and hybrid processing -- 5.4. Iterative reconstruction combined with deep learning -- 5.5. Direct reconstruction via deep learning
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part III. Magnetic resonance imaging. 6. Classical methods for MRI reconstruction -- 6.1. The basic physics of MRI -- 6.2. Fast sampling and image reconstruction -- 6.3. Parallel MRI
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7. Deep-learning-based MRI reconstruction -- 7.1. Structured deep MRI reconstruction networks -- 7.2. Leveraging generic network structures -- 7.3. Methods for advanced MRI technologies -- 7.4. Miscellaneous topics -- 7.5. Further readings
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part IV. Others. 8. Modalities and integration -- 8.1. Nuclear emission tomography -- 8.2. Ultrasound imaging -- 8.3. Optical imaging -- 8.4. Integrated imaging -- 8.5. Final remarks
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9. Image quality assessment -- 9.1. General measures -- 9.2. System-specific indices -- 9.3. Task-specific performance -- 9.4. Network-based observers -- 9.5. Final remarks
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10. Quantum computing -- 10.1. Wave-particle duality -- 10.2. Quantum gates -- 10.3. Quantum algorithms -- 10.4. Quantum machine learning -- 10.5. Final remarks
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Appendices. A. Math and statistics basics -- B. Hands-on networks.
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Also available in print.
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Mode of access: World Wide Web.
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System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
Additional Edition:
Print version: ISBN 9780750322140
Additional Edition:
ISBN 9780750322171
Language:
English
DOI:
10.1088/978-0-7503-2216-4
URL:
https://iopscience.iop.org/book/978-0-7503-2216-4
