Machine learning for tomographic imaging / Ge Wang, Yi Zhang, Xiaojing Ye, Xuanqin Mou.

Format:

Book

Author/Creator:

Wang, Ge (Ph. D. in electrical and computer engineering), author.

Zhang, Yi (Ph. D. in computer science and technology), author.

Ye, Xiaojing, author.

Mou, Xuanqin, author.

Contributor:

Institute of Physics (Great Britain), publisher.

Series:

IOP ebooks. 2020 collection.

IPEM-IOP series in physics and engineering in medicine and biology

IOP ebooks. [2020 collection]

Language:

English

Subjects (All):

Tomography.

Machine learning.

Artificial intelligence--Medical applications.

Artificial intelligence.

Tomography, X-Ray Computed.

Machine Learning.

Artificial Intelligence.

Medical Subjects:

Tomography, X-Ray Computed.

Machine Learning.

Artificial Intelligence.

Physical Description:

1 online resource (various pagings) : illustrations (some color).

Place of Publication:

Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2020]

System Details:

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

text file

Biography/History:

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.

Summary:

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.

Contents:

part I. Background. 1. Background knowledge

1.1. Imaging principles and a priori information

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

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

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

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

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

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

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

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

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

Appendices. A. Math and statistics basics

B. Hands-on networks.

Notes:

"Version: 20191201"--Title page verso.

Includes bibliographical references.

Title from PDF title page (viewed on January 6, 2020).

Other Format:

Print version:

ISBN:

9780750322164

9780750322157

OCLC:

1135509558

Access Restriction:

Restricted for use by site license.