Literature DB >> 33339012

Quantitative PET in the 2020s: a roadmap.

Steven R Meikle1,2, Vesna Sossi3, Emilie Roncali4, Simon R Cherry4,5, Richard Banati1,2,6, David Mankoff7, Terry Jones5, Michelle James8,9, Julie Sutcliffe4,10, Jinsong Ouyang11, Yoann Petibon11, Chao Ma11, Georges El Fakhri11, Suleman Surti7, Joel S Karp7, Ramsey D Badawi4,5, Taiga Yamaya12, Go Akamatsu12, Georg Schramm13, Ahmadreza Rezaei13, Johan Nuyts13, Roger Fulton2,14, André Kyme2,15, Cristina Lois11, Hasan Sari16,17, Julie Price16,17, Ronald Boellaard18, Robert Jeraj19,20, Dale L Bailey1,21,22, Enid Eslick21, Kathy P Willowson21,22, Joyita Dutta23.   

Abstract

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 'hot spot' 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.

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Year:  2021        PMID: 33339012      PMCID: PMC9358699          DOI: 10.1088/1361-6560/abd4f7

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   4.174


  131 in total

1.  Joint reconstruction of image and motion in gated positron emission tomography.

Authors:  Moritz Blume; Axel Martinez-Möller; Andreas Keil; Nassir Navab; Magdalena Rafecas
Journal:  IEEE Trans Med Imaging       Date:  2010-06-17       Impact factor: 10.048

2.  Super-resolution in PET imaging.

Authors:  John A Kennedy; Ora Israel; Alex Frenkel; Rachel Bar-Shalom; Haim Azhari
Journal:  IEEE Trans Med Imaging       Date:  2006-02       Impact factor: 10.048

3.  Fast reconstruction of 3D time-of-flight PET data by axial rebinning and transverse mashing.

Authors:  Stefaan Vandenberghe; Margaret E Daube-Witherspoon; Robert M Lewitt; Joel S Karp
Journal:  Phys Med Biol       Date:  2006-03-01       Impact factor: 3.609

4.  Motion tracking for medical imaging: a nonvisible structured light tracking approach.

Authors:  Oline Vinter Olesen; Rasmus R Paulsen; Liselotte Højgaard; Bjarne Roed; Rasmus Larsen
Journal:  IEEE Trans Med Imaging       Date:  2011-08-18       Impact factor: 10.048

5.  Automated classification of benign and malignant lesions in 18F-NaF PET/CT images using machine learning.

Authors:  Timothy Perk; Tyler Bradshaw; Song Chen; Hyung-Jun Im; Steve Cho; Scott Perlman; Glenn Liu; Robert Jeraj
Journal:  Phys Med Biol       Date:  2018-11-20       Impact factor: 3.609

6.  MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac 18F-FDG imaging.

Authors:  Y Petibon; T Sun; P K Han; C Ma; G El Fakhri; J Ouyang
Journal:  Phys Med Biol       Date:  2019-10-04       Impact factor: 3.609

7.  DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.

Authors:  Ida Häggström; C Ross Schmidtlein; Gabriele Campanella; Thomas J Fuchs
Journal:  Med Image Anal       Date:  2019-03-30       Impact factor: 8.545

Review 8.  PET Tracers for Imaging of ABC Transporters at the Blood-Brain Barrier: Principles and Strategies.

Authors:  Gert Luurtsema; Philip Elsinga; Rudi Dierckx; Ronald Boellaard; Aren van Waarde
Journal:  Curr Pharm Des       Date:  2016       Impact factor: 3.116

9.  Non-local means denoising of dynamic PET images.

Authors:  Joyita Dutta; Richard M Leahy; Quanzheng Li
Journal:  PLoS One       Date:  2013-12-05       Impact factor: 3.240

Review 10.  Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.

Authors:  Dmitry Grapov; Johannes Fahrmann; Kwanjeera Wanichthanarak; Sakda Khoomrung
Journal:  OMICS       Date:  2018-08-20
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  5 in total

1.  New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies.

Authors:  Go Akamatsu; Naoki Shimada; Keiichi Matsumoto; Hiromitsu Daisaki; Kazufumi Suzuki; Hiroshi Watabe; Keiichi Oda; Michio Senda; Takashi Terauchi; Ukihide Tateishi
Journal:  Ann Nucl Med       Date:  2022-01-14       Impact factor: 2.668

2.  Performance evaluation of dedicated brain PET scanner with motion correction system.

Authors:  Yuya Onishi; Takashi Isobe; Masanori Ito; Fumio Hashimoto; Tomohide Omura; Etsuji Yoshikawa
Journal:  Ann Nucl Med       Date:  2022-06-13       Impact factor: 2.258

3.  Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging.

Authors:  Tao Sun; Zhenguo Wang; Yaping Wu; Fengyun Gu; Xiaochen Li; Yan Bai; Chushu Shen; Zhanli Hu; Dong Liang; Xin Liu; Hairong Zheng; Yongfeng Yang; Georges El Fakhri; Yun Zhou; Meiyun Wang
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-05-14       Impact factor: 10.057

Review 4.  Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging.

Authors:  Irene Polycarpou; Georgios Soultanidis; Charalampos Tsoumpas
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2021-07-05       Impact factor: 4.226

5.  Motion correction and its impact on quantification in dynamic total-body 18F-fluorodeoxyglucose PET.

Authors:  Tao Sun; Yaping Wu; Wei Wei; Fangfang Fu; Nan Meng; Hongzhao Chen; Xiaochen Li; Yan Bai; Zhenguo Wang; Jie Ding; Debin Hu; Chaojie Chen; Zhanli Hu; Dong Liang; Xin Liu; Hairong Zheng; Yongfeng Yang; Yun Zhou; Meiyun Wang
Journal:  EJNMMI Phys       Date:  2022-09-14
  5 in total

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