Machine learning for tomographic imaging /

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 med...

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Bibliographic Details
Call Number:Libro Electrónico
Main Authors: 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)
Format: Electronic eBook
Language:Inglés
Published: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2020]
Series:IOP ebooks. 2020 collection.
IPEM-IOP series in physics and engineering in medicine and biology.
Subjects:
Tomography.
Machine learning.
Artificial intelligence > Medical applications.
Tomography, X-Ray Computed.
Machine Learning.
Artificial Intelligence.
Medical imaging.
TECHNOLOGY & ENGINEERING / Imaging Systems.
Online Access:Texto completo
Table of 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.

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