Machine learning for tomographic imaging /

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Machine learning for tomographic imaging /

Wang, Ge - Personal Name; Institute of Physics (Great Britain), - Personal Name; Zhang, Yi - Personal Name; Ye, Xiaojing, - Personal Name; Mou, Xuanqin, - Personal Name;

"Version: 20191201"--Title page verso.Includes bibliographical references.part I. Background. 1. Background knowledge -- 1.1. Imaging principles and a priori information2. 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 remarks3. Artificial neural networks -- 3.1. Basic concepts -- 3.2. Training, validation, and testing of an artificial neural network -- 3.3. Typical artificial neural networkspart 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 scanner5. 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 learningpart 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 MRI7. 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 readingspart 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 remarks9. 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 remarks10. Quantum computing -- 10.1. Wave-particle duality -- 10.2. Quantum gates -- 10.3. Quantum algorithms -- 10.4. Quantum machine learning -- 10.5. Final remarksAppendices. A. Math and statistics basics -- B. Hands-on networks.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.Undergraduate and graduate students in the biomedical imaging field.Also available in print.Mode of access: World Wide Web.System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.Ge Wang is the Clark and Crossan Endowed Chair Professor and the Director of the Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA. Among his 480 journal papers, he published the first spiral/helical cone-beam/multi-slice CT paper in 1991 and many follow-up papers on this important topic. He is a Fellow of the National Academy of Inventors. Yi Zhang is an Associate Professor with the College of Computer Science, Sichuan University, and is the Dean of the Software Engineering Department. His group published the first peer-reviewed journal paper on deep learning based low-dose CT and subsequently published more than 20 papers in this rapidly expanding area. Xiaojing Ye is an Associate Professor with the Department of Mathematics and Statistics at Georgia State University, Atlanta, USA. His research focuses on applied and computational mathematics, in particular variational methods for imaging problems, numerical optimization and analysis, and computational problems in machine learning. Xuanqin Mou is a Professor with Xi'an Jiaotong University. He is the Director of the National Data Broadcasting Engineering and Technology Research Center, and the Director of the Institute of Image Processing and Pattern Recognition. He published over 200 peer-reviewed journal and conference papers on CT reconstruction algorithms, artifact reductions, and image quality assessments.Title from PDF title page (viewed on January 6, 2020).

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Series Title: -
Call Number: -
Publisher: : .,
Collation: 1 online resource (various pagings) :illustrations (some color).
Language: English
ISBN/ISSN: 9780750322164
Classification: 616.07/54
Content Type: -
Media Type: -
Carrier Type: -
Edition: -
Subject(s): Tomography.
Tomography, X-Ray Computed.
TECHNOLOGY & ENGINEERING / Imaging Systems.
Medical imaging.
Machine learning.
Artificial intelligence
Artificial Intelligence.
Specific Detail Info: -
Statement of Responsibility: Ge Wang, Yi Zhang, Xiaojing Ye, Xuanqin Mou.

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