PREAMBLE

The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and professional organization founded in 1954 to promote the science, technology, and practical application of nuclear medicine. The European Association of Nuclear Medicine (EANM) is a professional nonprofit medical association that facilitates communication worldwide between individuals pursuing clinical and research excellence in nuclear medicine. The EANM was founded in 1985. The EANM was founded in 1985. SNMMI and EANM members are physicians, technologists, and scientists specializing in the research and practice of nuclear medicine.

The SNMMI and EANM will periodically define new guidelines for nuclear medicine practice to help advance the science of nuclear medicine and to improve the quality of service to patients throughout the world. Existing practice guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated.

Each practice guideline, representing a policy statement by the SNMMI/EANM, has undergone a thorough consensus process in which it has been subjected to extensive review. The SNMMI and EANM recognize that the safe and effective use of diagnostic nuclear medicine imaging requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guideline by those entities not providing these services is not authorized.

These guidelines are an educational tool designed to assist practitioners in providing appropriate care for patients. They are not inflexible rules or requirements of practice and are not intended, nor should they be used, to establish a legal standard of care. For these reasons and those set forth below, both the SNMMI and the EANM caution against the use of these guidelines in litigation in which the clinical decisions of a practitioner are called into question.

The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by the physician or medical physicist in light of all the circumstances presented. Thus, there is no implication that an approach differing from the guidelines, standing alone, is below the standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources, or advances in knowledge or technology subsequent to publication of the guidelines.

The practice of medicine includes both the art and the science of the prevention, diagnosis, alleviation, and treatment of disease. The variety and complexity of human conditions make it impossible to always reach the most appropriate diagnosis or to predict with certainty a particular response to treatment.

Therefore, it should be recognized that adherence to these guidelines will not ensure an accurate diagnosis or a successful outcome. All that should be expected is that the practitioner will follow a reasonable course of action based on current knowledge, available resources, and the needs of the patient to deliver effective and safe medical care. The sole purpose of these guidelines is to assist practitioners in achieving this objective.

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Researchers use dynamic PET imaging with target-selective tracer molecules to probe molecular processes. Kinetic models have been developed to describe these processes. The models are typically fitted to the measured PET data with the assumption that the brain is in a steady-state condition for the duration of the scan. The end results are quantitative parameters that characterize the molecular processes. The most common kinetic modeling endpoints are estimates of volume of distribution or the binding potential of a tracer. If the steady state is violated during the scanning period, the standard kinetic models may not apply. To address this issue, time-variant kinetic models have been developed for the characterization of dynamic PET data acquired while significant changes (e.g., short-lived neurotransmitter changes) are occurring in brain processes. These models are intended to extract a transient signal from data. This work in the PET field dates back at least to the 1990s. As interest has grown in imaging nonsteady events, development and refinement of time-variant models has accelerated. These new models, which we classify as belonging to the first, second, or third generation according to their innovation, have used the latest progress in mathematics, image processing, artificial intelligence, and statistics to improve the sensitivity and performance of the earliest practical time-variant models to detect and describe nonsteady phenomena. This review provides a detailed overview of the history of time-variant models in PET. It puts key advancements in the field into historical and scientific context. The sum total of the methods is an ongoing attempt to better understand the nature and implications of neurotransmitter fluctuations and other brief neurochemical phenomena.

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Sophos X-Ops unveils five-year investigation tracking China-based groups targeting perimeter devices

Decades of obsolete and unpatched hardware and software endanger us all

Jessica Y. Franco
Dec 1, 2020; 19:1936-1951
Research

Wenhe Zhu
Dec 29, 2020; 0:RA120.002353v1-mcp.RA120.002353
Research

Tricia Rowlison
Dec 1, 2020; 19:2090-2103
Research

Simone Kurz
Dec 29, 2020; 0:RA120.002266v1-mcp.RA120.002266
Research

Toma Keser
Dec 29, 2020; 0:RA120.002433v1-mcp.RA120.002433
Research

Yadong Yu
Dec 1, 2020; 19:1997-2014
Research

Xiao-Ting Wen
Dec 17, 2020; 0:RA120.002119v1-mcp.RA120.002119
Research

Yi-Han Lin
Dec 1, 2020; 19:2030-2046
Research

Alba Simats
Dec 1, 2020; 19:1921-1935
Research

Eugen Netz
Dec 1, 2020; 19:2157-2167
Technological Innovation and Resources

Yi Liu
Dec 1, 2020; 19:1968-1985
Research

Zilu Ye
Dec 28, 2020; 0:R120.002204v1-mcp.R120.002204
Review

Charlotte A. G. H. van Gelder
Dec 1, 2020; 19:1952-1967
Research

Litong Nie
Dec 1, 2020; 19:2015-2029
Research

Peter Lasch
Dec 1, 2020; 19:2125-2138
Technological Innovation and Resources

Sylwia Struk
Dec 28, 2020; 0:RA119.001766v1-mcp.RA119.001766
Research

Weixian Deng
Dec 22, 2020; 0:RA120.002411v1-mcp.RA120.002411
Research

Julia Knöckel
Dec 22, 2020; 0:RA120.002432v1-mcp.RA120.002432
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Nadine Prust
Dec 17, 2020; 0:RA120.002232v1-mcp.RA120.002232
Research

R. Greg Stacey
Nov 4, 2020; 0:RA120.002275v1-mcp.RA120.002275
Research

David Durán
Nov 19, 2020; 0:RA120.002276v1-mcp.RA120.002276
Research

Sean A Burnap
Dec 7, 2020; 0:RA120.002305v1-mcp.RA120.002305
Research

Xinting Zhang
Dec 8, 2020; 0:RA120.002306v1-mcp.RA120.002306
Research

Mingkun Yang
Nov 24, 2020; 0:RA120.002144v1-mcp.RA120.002144
Research