About this Research Topic
Positron emission tomography (PET) has gradually developed into a powerful tool for diagnosis, prognosis, and therapeutic response assessment in a variety of diseases. However, traditional image diagnosis is based on individual empirics, which may lead to poor repeatability. Artificial intelligence (AI) is an emerging computer science technology aiming to develop systems that mimic human intelligence and is helpful in medical imaging, including computer-aided diagnosis, radiomics, and radiogenomics. The deep learning of AI in PET is to screen image features, extract quantitative image features, and further build a model with clinical information to accurately guide the diagnosis and treatment of diseases. With the increase of cases, the AI model will be continuously optimized, which could improve the diagnostic accuracy and repeatability. Thus AI technology is gaining increasing attention in the application of PET including PET/CT and PET/MR in the past decade.
This Research Topic aims to create an interdisciplinary forum of discussion on recent developments and advances of AI technology in PET imaging, including but not limited to cancer, cardiovascular, endocrine, hematologic, or neurological diseases. The Research Topic also presents a comprehensive summary of the state-of-the-art technology in the analysis of quantitative image characteristics for disease diagnosis, staging, therapy, therapeutic decisions, and evaluations. The published papers will show a diversity of new developments in these areas from small-scale study to multicenter, large sample model and up-to-date clinical application.
Authors are welcome to contribute with original research articles as well as review articles that will illustrate and stimulate the continuing effort to develop radiomics and AI use in PET.
Potential themes include, but are not limited to:
• Artificial intelligence technology in PET, PET/CT, or PET/MR
• Artificial intelligence of PET in clinical cancer application
• Artificial intelligence of PET in cardiovascular,endocrine, hematologic, or neurological diseases
• Artificial intelligence of PET in neural network or cardiac physiology
• Artificial intelligence of PET in diagnosis, staging, therapy, and prognosis
• Artificial intelligence and molecular imaging
• Image processing, machine learning, and radiomics
This Research Topic aims to create an interdisciplinary forum of discussion on recent developments and advances of AI technology in PET imaging, including but not limited to cancer, cardiovascular, endocrine, hematologic, or neurological diseases. The Research Topic also presents a comprehensive summary of the state-of-the-art technology in the analysis of quantitative image characteristics for disease diagnosis, staging, therapy, therapeutic decisions, and evaluations. The published papers will show a diversity of new developments in these areas from small-scale study to multicenter, large sample model and up-to-date clinical application.
Authors are welcome to contribute with original research articles as well as review articles that will illustrate and stimulate the continuing effort to develop radiomics and AI use in PET.
Potential themes include, but are not limited to:
• Artificial intelligence technology in PET, PET/CT, or PET/MR
• Artificial intelligence of PET in clinical cancer application
• Artificial intelligence of PET in cardiovascular,endocrine, hematologic, or neurological diseases
• Artificial intelligence of PET in neural network or cardiac physiology
• Artificial intelligence of PET in diagnosis, staging, therapy, and prognosis
• Artificial intelligence and molecular imaging
• Image processing, machine learning, and radiomics
Keywords: PET, radiomics, radiogenomics, artificial intelligence, diagnosis
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
