Info
Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging / / edited by Patrick Veit-Haibach, Ken Herrmann
| Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging / / edited by Patrick Veit-Haibach, Ken Herrmann |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (216 pages) |
| Disciplina | 610.28563 |
| Collana | Medicine Series |
| Soggetto topico |
Nuclear medicine
Medical informatics Nuclear Medicine Health Informatics |
| ISBN | 3-031-00119-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface: Benefits and Challenges of AI/ML in Hybrid Imaging and Molecular Imaging -- Contents -- Part I: Technology -- 1: Role and Influence of Artificial Intelligence in Healthcare, Hybrid Imaging, and Molecular Imaging -- 1.1 AI Applications Support the Infrastructure and Interventions of Healthcare, Including Molecular Imaging -- 1.1.1 Drug Development -- 1.1.2 Clinical Workflow -- 1.2 AI's Clinical Applications with a Focus on Molecular Imaging -- 1.2.1 Understanding Disease -- 1.2.2 Diagnosis -- 1.2.3 Radiologic-Pathology Correlation -- 1.2.4 Characterization -- 1.2.5 Treatment Planning -- 1.2.6 Prediction of Response to Treatment -- 1.2.7 Overall Prognosis -- 1.2.8 Reporting -- 1.3 Conclusion -- References -- 2: Introduction to Machine Learning: Definitions and Hybrid Imaging Applications -- 2.1 Introduction -- 2.2 History and Basic Definitions -- 2.3 Learning Paradigms -- 2.4 General Concepts of Machine Learning Methods -- 2.5 Classical Machine Learning Approaches -- 2.6 Artificial Neural Networks -- 2.7 Radiomics and Radiogenomics -- 2.8 Imaging Applications -- 2.9 Conclusions and Perspectives -- References -- 3: Radiomics in Nuclear Medicine, Robustness, Reproducibility, and Standardization -- 3.1 Introduction -- 3.2 Robustness of Radiomic Features -- 3.3 Image Acquisition -- 3.4 Image Reconstruction -- 3.5 Segmentation -- 3.6 Image Processing -- 3.7 Discretization -- 3.8 Software -- 3.9 Pitfalls -- 3.10 Standardization -- 3.11 Discussion -- 3.12 Conclusion -- References -- 4: Evolution of AI in Medical Imaging -- 4.1 Disease Characterization -- 4.2 Segmentation -- 4.3 Image Generation/Reconstruction -- 4.4 Data Corrections -- 4.5 Image Registration -- 4.6 Radiology Reporting -- 4.7 Conclusion -- References -- 5: The Basic Principles of Machine Learning -- 5.1 Introduction.
5.1.1 The Task of ML -- 5.1.1.1 A Question -- 5.1.1.2 A Computer -- 5.1.1.3 An Algorithm or Model -- 5.1.1.4 Data to Interpret -- 5.1.2 Supervised Learning -- 5.1.3 Unsupervised Learning -- 5.1.4 Radiomics and Texture Analysis -- 5.1.5 Feature Reduction -- 5.1.6 Scaling and Normalization -- 5.1.7 Training, Validation, and Testing -- 5.2 Linear Regression -- 5.2.1 Under- and Overfitting -- 5.2.2 Linear Regression Mathematics -- 5.2.3 The Neural Network -- 5.2.4 The Objective Function -- 5.2.5 Gradient Descent -- 5.2.6 Deep Learning with Convolutional Neural Networks -- 5.2.7 Advanced Deep Learning Architectures -- 5.2.7.1 Autoencoders -- 5.2.7.2 ResNet -- 5.2.7.3 U-Net -- 5.2.7.4 Generative Adversarial Networks -- 5.2.7.5 Deep Boltzmann Machines -- 5.2.8 Deep Learning in Medical Image Analysis -- 5.2.8.1 Classification, Localization and Detection -- 5.2.8.2 Segmentation -- 5.2.8.3 Registration -- 5.2.8.4 Image Synthesis -- 5.2.9 Federated Learning -- References -- Part II: Clinical Applications -- 6: Imaging Biomarkers and Their Meaning for Molecular Imaging -- 6.1 Introduction -- 6.2 Imaging Biomarkers, Paradigm Shift in Medical Imaging -- 6.3 Imaging Biomarkers in Hybrid Molecular Imaging -- References -- 7: Integration of Artificial Intelligence, Machine Learning, and Deep Learning into Clinically Routine Molecular Imaging -- 7.1 Introduction -- 7.2 Classification -- 7.3 Segmentation -- 7.4 Detection and Localization -- 7.5 Applications of ML and DL in Molecular Imaging -- 7.6 Internal Department Applications -- 7.7 A Glance at Tomorrow -- 7.8 Workforce -- Redundancy, Displacement, Transformation, and Opportunity -- 7.9 Summary -- References -- 8: Imaging Biobanks for Molecular Imaging: How to Integrate ML/AI into Our Databases -- 8.1 Introduction -- 8.2 Imaging Biobanks in Molecular Imaging. 8.3 Bioethical Issues -- 8.4 Proposed Architecture -- References -- 9: Artificial Intelligence/Machine Learning in Nuclear Medicine -- 9.1 Introduction -- 9.2 Classification -- 9.2.1 Alzheimer's Disease -- 9.2.2 Parkinson's Disease -- 9.3 Segmentation -- 9.4 Image Generation and Processing -- 9.5 Low-Dose Imaging -- References -- 10: AI/ML Imaging Applications in Body Oncology -- 10.1 General Principles -- 10.2 Brain -- 10.2.1 Glioma -- 10.3 Neck -- 10.3.1 Head and Neck Cancer -- 10.3.2 Thyroid Cancer -- 10.4 Thorax -- 10.4.1 Lung Cancer -- 10.5 Abdomen -- 10.5.1 Esophageal Cancer -- 10.5.2 Liver Tumor -- 10.5.3 Prostate Cancer -- 10.6 Skeleton -- 10.6.1 Bone Metastases -- 10.7 Hematopoietic System -- 10.7.1 Lymphoma -- 10.7.2 Multiple Myeloma -- References -- 11: Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging -- 11.1 Introduction to AI -- 11.2 AI to Improve Image Quality and Processing -- 11.2.1 Image Denoising -- 11.2.2 Image Reconstruction -- 11.2.3 AI Applications in Attenuation Correction -- 11.2.4 Image Segmentation -- 11.2.5 CT Segmentation: Coronary Artery Calcium -- 11.2.6 CT Segmentation: Epicardial Adipose Tissue -- 11.3 AI to Improve Physician Interpretation -- 11.3.1 Structured Reporting -- 11.3.2 Disease Diagnosis -- 11.3.3 Risk Prediction -- 11.4 Protocol Optimization: Application to Rest Scan Cancellation -- 11.5 Explainable AI -- 11.6 Summary -- References -- Part III: Impact of AI and ML on Molecular Imaging and Theranostics -- 12: Artificial Intelligence Will Improve Molecular Imaging, Therapy and Theranostics. Which Are the Biggest Advantages for Therapy? -- 12.1 Introduction -- 12.2 Literature Review -- 12.2.1 Morphological and Metabolic Tumor Volume Tracking -- 12.2.1.1 Volumetry-Based Oncological Response Assessment Frameworks. 12.2.1.2 Automated Segmentation-Based Volumetry Techniques -- 12.2.1.3 Evolution of Automated Segmentation Using Neural Networks -- 12.3 Quantitative Image and Texture Analysis in Oncological Therapy Response Monitoring -- 12.3.1 Neuro-Oncology -- 12.3.2 Head and Neck Cancers -- 12.3.3 Lung Cancer -- 12.3.4 Prostate Cancer -- 12.3.5 Breast Cancer -- 12.3.6 Gastrointestinal Oncology -- 12.4 Discussion and Outlook -- References -- 13: Integrative Computational Biology, AI, and Radiomics: Building Explainable Models by Integration of Imaging, Omics, and Clinical Data -- 13.1 Introduction -- 13.2 Artificial Intelligence and Data-Driven Science -- 13.3 Multimodal Imaging and Radiomics -- 13.4 Integrative Computational Biology -- 13.5 Patient-Centric Medicine: Preventive and Data-Driven -- References -- 14: Legal and Ethical Aspects of Machine Learning: Who Owns the Data? -- 14.1 Introduction -- 14.2 Opening the "Ethics Bubble": What Are the Concerns? -- 14.3 Going Beyond FAT: Beyond Medical Ethics -- 14.4 Who Owns Patient Data? -- 14.5 Conclusion -- References -- 15: Artificial Intelligence and the Nuclear Medicine Physician: Clever Is as Clever Does -- 15.1 I Am Looking Forward to More A.I. in My Practice Because... -- 15.1.1 The Images Will Look Prettier -- 15.1.2 My Life Will Be Easier -- 15.1.3 My Patients Will Be Better Off -- 15.2 I Am Wary of More A.I. Because... -- 15.2.1 I Don't Understand It -- 15.2.2 I Don't Trust It -- 15.2.3 I Don't Want It -- 15.3 How to Proceed? Let's Be Practical! -- References -- Correction to: Radiomics in Nuclear Medicine, Robustness, Reproducibility, and Standardization -- Correction to: Chapter 3 in: P. Veit-Haibach, K. Herrmann (eds.), Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging, https://doi.org/10.1007/978-3-031-00119-2_3. |
| Record Nr. | UNINA-9910580165603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
PET/MRI in Oncology : Current Clinical Applications / / edited by Andrei Iagaru, Thomas Hope, Patrick Veit-Haibach
| PET/MRI in Oncology : Current Clinical Applications / / edited by Andrei Iagaru, Thomas Hope, Patrick Veit-Haibach |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (IX, 433 p. 95 illus., 71 illus. in color.) |
| Disciplina | 616.07548 |
| Soggetto topico |
Nuclear medicine
Oncology Nuclear Medicine |
| ISBN | 3-319-68517-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- PET technology designs for PET/MRI -- MR hardware for PET/MR -- MR pulse sequences for PET/MR -- MR contrast agents -- PET/MRI: attenuation correction -- PET/MRI: motion correction -- PET/MRI: reliability/reproducibility of SUV measurements -- PET/MRI: Safety considerations -- Functional/Heterogeneity MR techniques in Oncology -- Workflow and protocols considerations -- Whole body applications -- Neuro: Brain Oncology -- Neuro: Head and Neck Oncology -- Lung nodules and lung cancer -- Breast cancer -- Liver – Tom -- Neuroendocrine tumors -- Oesophageal, Gastric, and Pancreatic Cancer -- Rectal cancer -- Gynecologic applications -- Prostate cancer -- Melanoma and multiple myeloma -- MSK oncology -- Lymphoma -- Pediatric oncology -- Future directions. |
| Record Nr. | UNINA-9910300437603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||