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This book will provide readers with a good overview of some of most recent advances in the field of Photon Counting CT technology for X-ray medical imaging, especially as it pertains to new detectors. There will be a good mixture of general chapters in both technology and applications in CT medical imaging. The book will have an in-depth review of the research topics from world-leading specialists in the field. The conversion of the X-ray signal into analogue/digital value will be covered in some chapters. The authors also provide a review of CMOS chips for X-ray image sensors, methods of material discrimination and image reconstruction techniques.
Covers a broad range of topics, including an introduction to novel spectral Computed TomographyIncludes in-depth analysis on how to optimize X-ray detectionDiscusses analysis of electronics for X-ray detection.
Editors Biography
Clinical Applications
Medical Photon-Counting CT: Status and Clinical ApplicationsReview
Coronary Artery Calcifications Assessment with Photon-counting Detector Computed Tomography
MARS for Orthopaedic Pathology
MARS for Molecular Imaging and Preclinical Studies
Quantitative Breast Lesion Characterization with Spectral Mammography: A Feasibility Study
Quantitative Breast Imaging with Low-Dose Spectral Mammography
Image Reconstruction and Material Discrimination
An Overview of CT Reconstruction with Applications to Photon Counting Detectors
On the Choice of Base Materials for Alvarez–Macovski and DIRA Dual-energy Reconstruction Algorithms in CT
Spectral Imaging in Photon-Counting CT with Data Acquired in Interleaved/Gapped Spectral Channels
One-Step Basis Image Reconstruction in Spectral CT Based on MAP-EM Algorithm and Polar Coordinate Transformation
Quantitative Analysis Methodology of X-Ray Attenuation for Medical Diagnostic Imaging: Algorithm to Derive Effective Atomic Number, Soft Tissue and Bone Images
K-Edge Imaging in Spectral Photon-Counting Computed Tomography: A Benchtop System Study
Image Domain Performance Evaluation of a Photon-counting ASIC Using a Multilayer Perceptron Network
Index