Artificial Intelligence and PET Imaging, Part 1, An Issue of PET Clinics, E-Book

In this issue of PET Clinics, guest editors Arman Rahmim, Babak Saboury, and Eliot Siegel bring their considerable expertise to the topic of Artificial Intelligence and PET Imaging. - Provides in-depth, clinical reviews on the latest updates in AI and PET Imaging, providing actionable insights for clinical practice. - Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field; Authors synthesize and distill the latest research and practice guidelines to create these timely topic-based reviews.

In this issue of PET Clinics, guest editors Drs. Cristina Nanni, Paolo Castellucci, Stefano Fanti, and Neeta Pandit-Taskar bring their considerable expertise to the topic of Novel PET Imaging Techniques in the Management of Hematological Malignancies. Top experts in the field discuss the latest radiotracers for lymphoma and myeloma, novel quantitative imaging techniques, radioligand therapies, total body PET imaging; and more. - Contains 15 relevant, practice-oriented topics including PET imaging for therapy assessment in Hodgkin and non-Hodgkin lymphomas; symptomatic myeloma: PET, WB-MRI with DWI or both; advantages of total body PET imaging in hematological malignancies; applications of FDG-PET imaging in leukemias; PET imaging for therapy assessment in symptomatic myeloma, including Minimal Residual Disease; and more. - Provides in-depth clinical reviews on novel PET imaging techniques in the management of hematological malignancies, offering actionable insights for clinical practice. - Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. - Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems - Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics - Features self-contained chapters with a thorough literature review - Assesses the development of future machine learning techniques and the further application of existing techniques

An introductory account of the rationale and methods for integrating managerial information design in health care information systems, this book contains much important information, including key finds and controversies in the emerging field of study of occupational hazards and the rise in injuries caused at computer workstations.

At last, here is a comprehensive compilation of the accumulated knowledge on PET and PET/CT in oncology. It covers the entire spectrum from solidly documented indications, such as staging and monitoring of lung and colorectal cancer, to the application of PET/CT in head and neck surgery, gynecology, radiation therapy, urology, pediatrics and others. The chapters are supplemented by an introduction into the underlying techniques of both imaging devices and radiopharmacy.