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Biol."],"published-print":{"date-parts":[[2021,3,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET\/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative \u2018hot spot\u2019 imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET\/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.<\/jats:p>","DOI":"10.1088\/1361-6560\/abd4f7","type":"journal-article","created":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T17:16:56Z","timestamp":1608311816000},"page":"06RM01","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":77,"title":["Quantitative PET in the 2020s: a roadmap"],"prefix":"10.1088","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7397-0364","authenticated-orcid":false,"given":"Steven R","family":"Meikle","sequence":"first","affiliation":[{"name":"The University of Sydney"},{"name":"The University of 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