Deep Learning for Advanced X-ray Detection and Imaging Applications

• Titel: Deep Learning for Advanced X-ray Detection and Imaging Applications
• Mitwirkende: Iniewski, Krzysztof (Kris). [HerausgeberIn]
• Mitwirkende: Cai, Liang (Kevin). [HerausgeberIn]
• Verf.angabe: edited by Krzysztof (Kris) Iniewski, Liang (Kevin) Cai
• Ausgabe: 1st ed. 2024.
• Verlagsort: Cham
• Verlagsort: Cham
• Verlag: Springer Nature Switzerland
• Verlag: Imprint: Springer
• E-Jahr: 2024
• Jahr: 2024.
• Jahr: 2024.
• Umfang: 1 Online-Ressource(VII, 261 p. 75 illus. in color.)
• ISBN: 978-3-031-75653-5
• DOI: doi:10.1007/978-3-031-75653-5
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• K10plus-PPN: 1915752892
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4657324470
• Lokale URL Inst.: Zum Volltext

Abstract

Deep Learning Techniques for CT Image Denoising and Resolution Enhancement -- , Physically interpretable deep learning reconstruction for photon counting spectral CT -- , Deep learning methods in dual energy CT imaging -- , Performance Evaluation of Implicit Neural Representations in Diagnostic Fan-Beam CT Imaging -- , Learning-Based Material Decomposition for Spectral X-ray Imaging -- , Learning-Based Material Decomposition for Spectral X-ray Imaging -- , Correcting Charge Sharing Distortions in Photon Counting Detectors Utilizing a Spatial-Temporal CNN -- , Machine Learning Approaches for CdZnTe / CdTe Radiation Detectors -- , Enhanced 3D X-ray Tomography: Deep Learning-based Advanced Algorithms for Fiber Instance Segmentation -- , Machine Learning-Based Image Processing in Radiotherapy -- , Deep learning-based image reconstruction of coded-aperture imaging in nuclear security applications -- , Artificial Intelligence for X-ray Photon Counting Technology: Current Status and Future Perspectives.

Abstract

This book provides a comprehensive overview of the latest advances in applying Artificial Intelligence (AI) to advanced X-ray imaging, with a particular focus on its medical applications. Readers will discover why AI is set to revolutionize traditional signal processing and image reconstruction with vastly improved performance. The authors illustrate how Machine Learning (ML) and Deep Learning (DL) significantly advance X-ray detection analysis, image reconstruction, and other crucial steps. This book also reveals how these technologies enable photon counting detector-based X-ray Computed Tomography (CT), which has the potential not only to improve current CT images but also enable new clinical applications, such as providing higher spatial resolution, better soft tissue contrast, K-edge imaging, and simultaneous multi-contrast agent imaging. Explores the latest advances in applying Artificial Intelligence to advanced X-ray imaging; Provides reviews on innovative techniques for signal formation and image reconstruction process; Showcases the application of deep learning algorithms in Photon-Counting CT.