Literature DB >> 33763624

PET Parametric Imaging: Past, Present, and Future.

Guobao Wang1, Arman Rahmim2, Roger N Gunn3.   

Abstract

Positron emission tomography (PET) is actively used in a diverse range of applications in oncology, cardiology, and neurology. The use of PET in the clinical setting focuses on static (single time frame) imaging at a specific time-point post radiotracer injection and is typically considered as semi-quantitative; e.g. standardized uptake value (SUV) measures. In contrast, dynamic PET imaging requires increased acquisition times but has the advantage that it measures the full spatiotemporal distribution of a radiotracer and, in combination with tracer kinetic modeling, enables the generation of multiparametric images that more directly quantify underlying biological parameters of interest, such as blood flow, glucose metabolism, and receptor binding. Parametric images have the potential for improved detection and for more accurate and earlier therapeutic response assessment. Parametric imaging with dynamic PET has witnessed extensive research in the past four decades. In this paper, we provide an overview of past and present activities and discuss emerging opportunities in the field of parametric imaging for the future.

Entities:  

Keywords:  PET; dynamic imaging; image reconstruction; kinetic modeling; parametric imaging

Year:  2020        PMID: 33763624      PMCID: PMC7983029          DOI: 10.1109/trpms.2020.3025086

Source DB:  PubMed          Journal:  IEEE Trans Radiat Plasma Med Sci        ISSN: 2469-7303


  10 in total

1.  Expert consensus on oncological [18F]FDG total-body PET/CT imaging (version 1).

Authors:  Haojun Yu; Yushen Gu; Wei Fan; Yongju Gao; Meiyun Wang; Xiaohua Zhu; Zhifang Wu; Jianjun Liu; Biao Li; Hubing Wu; Zhaoping Cheng; Shuxia Wang; Yiqiu Zhang; Baixuan Xu; Sijin Li; Hongcheng Shi
Journal:  Eur Radiol       Date:  2022-06-25       Impact factor: 5.315

Review 2.  Positron emission tomography in multiple sclerosis - straight to the target.

Authors:  Benedetta Bodini; Matteo Tonietto; Laura Airas; Bruno Stankoff
Journal:  Nat Rev Neurol       Date:  2021-09-20       Impact factor: 42.937

3.  First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients.

Authors:  Hasan Sari; Clemens Mingels; Ian Alberts; Jicun Hu; Dorothee Buesser; Vijay Shah; Robin Schepers; Patrik Caluori; Vladimir Panin; Maurizio Conti; Ali Afshar-Oromieh; Kuangyu Shi; Lars Eriksson; Axel Rominger; Paul Cumming
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-01-04       Impact factor: 10.057

4.  Total-Body PET Multiparametric Imaging of Cancer Using a Voxelwise Strategy of Compartmental Modeling.

Authors:  Guobao Wang; Lorenzo Nardo; Mamta Parikh; Yasser G Abdelhafez; Elizabeth Li; Benjamin A Spencer; Jinyi Qi; Terry Jones; Simon R Cherry; Ramsey D Badawi
Journal:  J Nucl Med       Date:  2021-11-18       Impact factor: 11.082

5.  Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions.

Authors:  Hui Wang; Ying Miao; Wenjing Yu; Gan Zhu; Tao Wu; Xuefeng Zhao; Guangjie Yuan; Biao Li; Huiqin Xu
Journal:  Front Oncol       Date:  2022-04-28       Impact factor: 5.738

6.  Spatiotemporal Kernel Reconstruction for Linear Parametric Neurotransmitter PET Kinetic Modeling in Motion Correction Brain PET of Awake Rats.

Authors:  Alan Miranda; Daniele Bertoglio; Sigrid Stroobants; Steven Staelens; Jeroen Verhaeghe
Journal:  Front Neurosci       Date:  2022-05-12       Impact factor: 5.152

Review 7.  Total-Body PET Kinetic Modeling and Potential Opportunities Using Deep Learning.

Authors:  Yiran Wang; Elizabeth Li; Simon R Cherry; Guobao Wang
Journal:  PET Clin       Date:  2021-08-03

8.  68Ga-P15-041, A Novel Bone Imaging Agent for Diagnosis of Bone Metastases.

Authors:  Rui Guo; Xiangxi Meng; Fei Wang; Jiangyuan Yu; Qing Xie; Wei Zhao; Lin Zhu; Hank F Kung; Zhi Yang; Nan Li
Journal:  Front Oncol       Date:  2021-11-25       Impact factor: 6.244

9.  Quantitation of multiple injection dynamic PET scans: an investigation of the benefits of pooling data from separate scans when mapping kinetics.

Authors:  Fengyun Gu; Finbarr O'Sullivan; Mark Muzi; David A Mankoff
Journal:  Phys Med Biol       Date:  2021-07-01       Impact factor: 3.609

10.  A Generalized Linear modeling approach to bootstrapping multi-frame PET image data.

Authors:  Finbarr O'Sullivan; Fengyun Gu; Qi Wu; Liam D O'Suilleabhain
Journal:  Med Image Anal       Date:  2021-06-12       Impact factor: 8.545

  10 in total

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