Machine learning for tomographic imaging. ([2020])

• Record Type: Book
• Further Information: Note: Ge Wang, Yi Zhang, Xiaojing Ye, Xuanqin Mou.
• Authors: (Ph. D. in electrical and computer engineering), Wang, Ge; (Ph. D. in computer science and technology), Zhang, Yi; Ye, Xiaojing; Mou, Xuanqin
• Other Names: Institute of Physics (Great Britain), publisher.
• 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.
• Publisher Details: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing
• Publication Date: 2020
• Extent: 1 online resource, illustrations (some color)
• Subjects: 616.07/54; Tomography; Machine learning; Artificial intelligence -- Medical applications; Tomography, X-Ray Computed; Machine Learning; Artificial Intelligence; Medical imaging; TECHNOLOGY & ENGINEERING / Imaging Systems; Artificial intelligence -- Medical applications; Machine learning; Tomography; Electronic books
• Languages: English
• ISBNs: 9780750322164; 0750322160; 9780750322157; 0750322152
• Related ISBNs: 9780750322140; 0750322144; 9780750322171; 0750322179
• Notes: Note: Includes bibliographical references.; Note: Title from PDF title page (viewed on January 6, 2020).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.