Computed tomography principles, design, artifacts, and recent advances
"2021 marks the 50th anniversary of the x-ray computed tomography (CT). Over the years, CT has experienced tremendous technological development, driven mainly by clinical needs but also by technology advancements in other fields. Six years after the third edition of Computed Tomography, this fo...
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| 520 | |a "2021 marks the 50th anniversary of the x-ray computed tomography (CT). Over the years, CT has experienced tremendous technological development, driven mainly by clinical needs but also by technology advancements in other fields. Six years after the third edition of Computed Tomography, this fourth edition captures the most recent advances in technology and clinical applications. New to this edition are descriptions of artificial intelligence, machine learning, and deep learning, and their application to image reconstruction, protocol optimization, and workflow. A new chapter is added to describe the principles and advances in dual-energy and spectral CT. New detector technology, the photon counting detector, is described in details and its impact on CT system and clinical applications is analyzed. Many exciting development in clinical applications, such as cardiac functional imaging and stroke management, are also covered in details"-- | ||
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents Preface Nomenclature and Abbreviations 1 2 3 xi xv 1 Introduction 1.1 Conventional X-ray Tomography 1.2 History of Computed Tomography 1.3 Different Generations of CT Scanners 1.4 Problems References 3 8 16 21 22 Preliminaries 25 2.1 Mathematics Fundamentals 2.1.1 Fourier transform and convolution 2.1.2 Random variables 2.1.3 Linear algebra 2.2 Fundamentals of X-ray Physics 2.2.1 Production of x rays 2.2.2 Interaction of x rays with matter 2.3 Measurement of Line Integrals and DataConditioning 2.4 Sampling Geometry and Sinogram 2.5 Artificial Intelligence, Machine Learning, and Deep Learning 2.5.1 Overview of Al development 2.5.2 Neural network structure 2.5.3 Neural network training 2.5.4 Recent advances in DL 2.6 Problems References 25 25 29 32 36 36 39 45 49 53 53 55 58 64 69 73 Image Reconstruction 79 3.1 3.2 3.3 3.4 Introduction Intuitive Approach to Image Reconstruction The Fourier Slice Theorem The Filtered Backprojection Algorithm 3.4.1 Derivation of the filtered backprojection formula 3.4.2 Computer implementation v 79 81 87 90 93 97
vi Contents 3.4.3 Targeted reconstruction Fan-Beam Reconstruction 3.5.1 Reconstruction formula for equiangularsampling 3.5.2 Reconstruction formula forequally spaced sampling 3.5.3 Fan-beam to parallel-beam rebinning 3.6 Iterative Reconstruction 3.6.1 Mathematics versus reality 3.6.2 The general approach to iterative reconstruction 3.6.3 Algebraic reconstruction 3.6.4 System modeling process 3.6.4.1 Optics modeling 3.6.4.2 Statistics modeling 3.6.4.3 Object and image modeling 3.6.4.4 Physics modeling 3.6.5 Optimization algorithms 3.6.6 Image quality benefit of model-based iterative reconstruction 3.6.7 Reconstruction speedup 3.7 Deep Learning-based Reconstruction 3.7.1 General approach 3.7.2 Training dataset selection 3.7.3 Determination of the training dataset size 3.7.4 Examples of DL-based reconstruction 3.8 Problems References 3.5 4 5 111 113 114 121 122 127 130 131 133 135 136 137 139 142 143 146 147 150 150 152 155 157 159 162 Image Presentation 169 4.1 4.2 CT Image Display Volume Visualization 4.2.1 Multiplanar reformation 4.2.2 МІР, minMIP, and volume rendering 4.2.3 Surface rendering 4.3 Impact of Visualization Tools 4.4 Additive Manufacturing (AM) or 3DPrinting 4.4.1 Clinical utility 4.4.2 Hardware technologies 4.4.3 File format 4.4.4 Typical 3D printing workflow 4.5 Virtual Reality (VR) and AugmentedReality (AR) 4.6 Problems References 169 173 173 178 185 187 190 191 192 193 193 196 198 200 Key Performance Parameters of the CT Scanner 203 5.1 High-Contrast Spatial Resolution 5.1.1 In-plane resolution 203 204
vii Contents 5.1.2 Slice sensitivity profile Low-Contrast Resolution 5.2.1 Factors impacting low-contrast detectability 5.2.2 LCD phantoms 5.2.3 LCD evaluation methodologies 5.3 Temporal Resolution 5.4 CT Number Accuracy and Noise 5.5 Impact of IR, MBIR, and DL on Performance Measurement 5.5.1 Performance-metric-based approach 5.5.2 Task-based approach 5.5.3 Surrogate task with clinical data 5.5.4 Surrogate task with nonclinical data 5.5 Performance of the Scanogram 5.6 Problems References 211 214 215 220 221 225 232 239 239 243 244 246 248 250 252 Major Components of the CT Scanner 257 System Overview The X-ray Tube and the High-Voltage Generator The X-Ray Detector and Data-Acquisition Electronics 6.3.1 Direct-conversion gas detector 6.3.2 Indirect-conversion solid-state detector 6.3.3 Direct-conversion semiconductor detector 6.3.4 General performance parameters 6.3.4.1 Efficiency 6.3.4.2 Uniformity 6.3.4.3 Stability 6.3.5 Specific performance parameters 6.3.5.1 Crosstalk 6.3.5.2 Linearity 6.3.5.3 Noise 6.4 The Gantry and Slip Ring 6.5 Collimation and Filtration 6.6 The Reconstruction Engine 6.7 The Patient Table 6.8 Problems References 257 259 271 271 273 275 279 279 281 282 283 283 286 288 290 294 297 298 299 302 Image Artifacts: Appearances, Causes, and Corrections 305 5.2 6 6.1 6.2 6.3 7 7.1 7.2 7.3 What Is an Image Artifact? Different Appearances of Image Artifacts Artifacts Related to System Design 7.3.1 Aliasing 7.3.2 Partial volume 305 307 314 314 327
viii Contents 7.3.3 Scatter 7.3.4 Noise-inducedstreaks 7.4 Artifacts Related to X-ray Tubes 7.4.1 Off-focal radiation 7.4.2 Tube arcing 7.4.3 Tube rotor wobble 7.5 Detector-Induced Artifacts 7.5.1 Offset, gain, nonlinearity, and radiation damage 7.5.2 Primary speed and afterglow 7.5.3 Detector response uniformity 7.6 Patient-Induced Artifacts 7.6.1 Patient motion 7.6.2 Beam hardening 7.6.3 Metal and high-density object artifacts 7.6.4 Incomplete projections 7.7 Operator-Induced Artifacts 7.8 Problems References 8 Computer Simulation and Analysis 8.1 What Is Computer Simulation? 8.2 Simulation Overview 8.3 Simulation of Optics 8.4 Simulation ofPhysics-Related Performance 8.5 Simulation of a Clinical Study 8.6 Problems References 9 Helical or Spiral CT Introduction 9.1.1 Clinical needs 9.1.2 Enabling technologies 9.2 Terminology and Reconstruction 9.2.1 Helical pitch 9.2.2 Basic reconstruction approaches 9.3 Slice Sensitivity Profile and Noise 9.4 Helically Related Image Artifacts 9.4.1 High-pitch helical artifacts 9.4.2 Noise-induced artifacts 9.4.3 System-misalignment-induced artifacts 9.4.4 Helical artifacts caused by object slope 9.5 Problems References 9.1 331 337 341 341 345 346 347 347 352 357 362 362 375 386 393 399 402 405 413 413 415 417 428 438 442 444 447 447 447 451 452 452 453 463 469 469 474 479 482 485 487
Contents ix 10 Multislice CT 489 The Need for Multislice CT Detector Configurations of Multislice CT Nonhelical Mode of Reconstruction Multislice Helical Reconstruction 10.4.1 2D backprojection algorithm 10.4.2 Reconstruction algorithms with 3D backprojection 10.4.3 Over-beaming (or over-scanning) compensation 10.5 Multislice Artifacts 10.5.1 General description 10.5.2 Multislice CT cone-beam effects 10.5.3 Interpolation-related image artifacts 10.5.4 Noise-induced multislice artifacts 10.5.5 Tilt artifacts in multislice helical CT 10.5.6 Distortion in step-and-shoot mode SSP 10.5.7 Artifacts due to geometric inaccuracy 10.5.8 Comparison of multislice and single-slice helical CT 10.6 Problems References 10.1 10.2 10.3 10.4 11 X-ray Radiation and Dose-Reduction Techniques 11.1 Biological Effects of X-ray Radiation 11.2 Measurement of X-ray Dose 11.2.1 Terminology and the measurement standard 11.2.2 Other measurement units and methods 11.2.3 Issues with the current CTDI 11.3 Methodologies for Dose Reduction 11.3.1 Tube-current modulation 11.3.2 Tube-voltage optimization 11.3.3 Umbra-penumbra and over-beam issues 11.3.4 Physiological gating 11.3.5 Organ-specific dose reduction 11.3.6 Protocol optimization and impact of theoperator 11.3.7 Postprocessing techniques 11.3.8 Advanced reconstruction 11.4 Problems References 12 Dual-Energy and Spectral CT 12.1 Intuitive 12.1.1 12.1.2 12.2 Theory 12.2.1 Explanation Material differentiation Material representation of Basis Material Decomposition Basis material 489 492 500 514 514 522 528 530 530 532 534 537 537 540 541 544 545 548 557
558 560 560 567 568 572 574 575 580 582 585 587 591 593 595 597 603 603 605 609 611 613
x Contents 12.2.2 Projection-space material decomposition (MD) 12.2.3 Image-space material decomposition 12.2.4 Multimaterial identification and quantification 12.2.5 Noise 12.3 Generation of Derivative Images 12.3.1 Monochromatic image 12.3.2 Basis material transformation 12.3.3 Electron density image 12.3.4 Effective atomic number image 12.4 Data Acquisition 12.4.1 Energy-integrating systems 12.4.2 Photon-counting system 12.5 Clinical Applications 12.6 Problems References 13 Advanced CT Applications 13.1 Introduction 13.2 Cardiac Imaging 13.2.1 Coronary calcium scan 13.2.2 Coronary artery imaging 13.2.2.1 Data acquisition and reconstruction 13.2.2.2 Temporal resolution improvement 13.2.2.3 Spatial resolution improvement 13.2.2.4 Dose and coverage 13.2.3 Cardiac function 13.3 Interventional Procedures 13.4 Stroke: CT Perfusion and Multiphase СТА 13.4.1 Perfusion 13.4.2 Multiphase СТА 13.5 Screening and Quantitative CT 13.5.1 Lung cancer screening 13.5.2 Quantitative CT 13.5.3 CT coIonography 13.6 Impact of Artificial Intelligence 13.7 Problems References 616 619 622 625 628 628 632 635 636 636 637 639 644 651 653 661 661 663 664 669 670 677 685 688 692 697 702 703 711 714 715 719 721 724 732 734 Glossary 745 Index 757
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| illustrated | Illustrated |
| indexdate | 2024-12-24T09:29:39Z |
| institution | BVB |
| language | English |
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| physical | xviii, 766 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
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| publisher | SPIE |
| record_format | marc |
| spellingShingle | Hsieh, Jiang Computed tomography principles, design, artifacts, and recent advances Tomography Tomography / Technological innovations Tomographie Tomographie / Innovations Tomography fast |
| title | Computed tomography principles, design, artifacts, and recent advances |
| title_auth | Computed tomography principles, design, artifacts, and recent advances |
| title_exact_search | Computed tomography principles, design, artifacts, and recent advances |
| title_full | Computed tomography principles, design, artifacts, and recent advances Jiang Hsieh |
| title_fullStr | Computed tomography principles, design, artifacts, and recent advances Jiang Hsieh |
| title_full_unstemmed | Computed tomography principles, design, artifacts, and recent advances Jiang Hsieh |
| title_short | Computed tomography |
| title_sort | computed tomography principles design artifacts and recent advances |
| title_sub | principles, design, artifacts, and recent advances |
| topic | Tomography Tomography / Technological innovations Tomographie Tomographie / Innovations Tomography fast |
| topic_facet | Tomography Tomography / Technological innovations Tomographie Tomographie / Innovations |
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