03814nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001800153072001600171072002300187072001500210245015400225250001800379264006900397300005400466336002600520337002600546338003600572347002400608505101600632520112501648650002302773650001502796650002502811650001202836650005902848650003902907650002602946700008902972700008103061710003403142773002603176776003603202776003603238776003603274856004603310978-3-031-75653-5DE-He21320251030114928.0cr nn 008mamaa250122s2024    sz |    s    |||| 0|eng d  a97830317565357 a10.1007/978-3-031-75653-52doi 4aTK5101-5105.9 7aTJF2bicssc 7aTEC0240002bisacsh 7aTJF2thema10aDeep Learning for Advanced X-ray Detection and Imaging Applicationsh[electronic resource] /cedited by Krzysztof (Kris) Iniewski, Liang (Kevin) Cai.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aVII, 261 p. 75 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aDeep 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.  aThis 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. 0aTelecommunication. 0aPhotonics. 0aOptical engineering. 0aOptics.14aMicrowaves, RF Engineering and Optical Communications.24aPhotonics and Optical Engineering.24aOptics and Photonics.1 aIniewski, Krzysztof (Kris).eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aCai, Liang (Kevin).eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303175652808iPrinted edition:z978303175654208iPrinted edition:z978303175655940uhttps://doi.org/10.1007/978-3-031-75653-5