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Gu F, Wu Q. Quantitation of dynamic total-body PET imaging: recent developments and future perspectives. Eur J Nucl Med Mol Imaging 2023; 50:3538-3557. [PMID: 37460750 PMCID: PMC10547641 DOI: 10.1007/s00259-023-06299-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/05/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades. CHALLENGES The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results. CONCLUSIONS In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.
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Affiliation(s)
- Fengyun Gu
- School of Mathematics and Physics, North China Electric Power University, 102206, Beijing, China.
- School of Mathematical Sciences, University College Cork, T12XF62, Cork, Ireland.
| | - Qi Wu
- School of Mathematical Sciences, University College Cork, T12XF62, Cork, Ireland
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Regional Characterization of the Gottingen Minipig Brain by [18 F]FDG Dynamic Pet Modeling. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00739-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Abstract
Purpose
To determine the best kinetic model to be applied on dynamic brain [18 F]FDG PET images by characterizing the regional brain glucose metabolism of normal Göttingen minipigs.
Methods
Nine Göttingen minipigs were scanned with a clinical PET/CT tomograph, starting from the injection of an intravenous bolus of [18 F]FDG, for about 25 min. Dynamic images were reconstructed and nine brain regions of interest (ROI), plus a vascular region, were defined and time-activity curves (TAC) were determined.
Three kinetic models were considered for fitting with experimental TACs: one-tissue compartment model 1TC, two-tissue irreversible compartment model 2TCi and two-tissue reversible model 2TC. Akaike Information Criterion was considered to evaluate the goodness of each model fitting. Regional and global kinetic parameter values were evaluated, in addition to the partition coefficient, net influx rate and retention index (RI).
Results
Both 2TCi and 2TC models turned out to be good choices for the next analysis. Parameter values were very similar between the different brain regions, with similar values to when the brain as a whole is considered (kinetic parameters mean values, from 2TCi model: K1 = 1.0 ml/g/min, k2 = 0.49 min− 1, k3 = 0.034 min− 1, K1/k2 = 2.14ml/g, Ki =0.069 ml/g/min; from 2TC model: K1 = 1.10 ml/g/min, k2 = 0.54 min− 1, k3 = 0.058 min− 1, k4 = 0.039 min− 1, K1/k2 = 2.18 ml/g, Ki = 0.10 ml/g/min; RI mean ± sd: 0.147 ± 0.037 min− 1), with the exception of the cerebellum (mean values from the 2TCi model: K1 = 0.52 ml/g/min, k2 = 0.56 min− 1, k3 = 0.025 min− 1, K1/k2 = 0.98ml/g, Ki=0.022 ml/g/min; from 2TC model: K1 = 0.54 ml/g/min, k2 = 0.61 min− 1, k3 = 0.044 min− 1, k4 = 0.038 min− 1, K1/k2 = 0.95ml/g, Ki=0.032 ml/g/min; RI mean ± sd: 0.071 ± 0.018 min− 1).
Conclusion
The two-tissue model is able to describe the regional brain metabolism in Göttingen minipigs. Compared to the 2TCi model, in the 2TC model the k4 micro-parameter was also evaluated. This led to adjustments of the other microparameters, especially k3 and consequently the net influx rate Ki. For healthy minipigs, the glucose metabolism was similar in all of the brain regions analyzed, with the exception of the cerebellum, where the FDG uptake was lower.
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Agaronyan A, Syed R, Kim R, Hsu CH, Love SA, Hooker JM, Reid AE, Wang PC, Ishibashi N, Kang Y, Tu TW. A Baboon Brain Atlas for Magnetic Resonance Imaging and Positron Emission Tomography Image Analysis. Front Neuroanat 2022; 15:778769. [PMID: 35095430 PMCID: PMC8795914 DOI: 10.3389/fnana.2021.778769] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/17/2021] [Indexed: 12/14/2022] Open
Abstract
The olive baboon (Papio anubis) is phylogenetically proximal to humans. Investigation into the baboon brain has shed light on the function and organization of the human brain, as well as on the mechanistic insights of neurological disorders such as Alzheimer's and Parkinson's. Non-invasive brain imaging, including positron emission tomography (PET) and magnetic resonance imaging (MRI), are the primary outcome measures frequently used in baboon studies. PET functional imaging has long been used to study cerebral metabolic processes, though it lacks clear and reliable anatomical information. In contrast, MRI provides a clear definition of soft tissue with high resolution and contrast to distinguish brain pathology and anatomy, but lacks specific markers of neuroreceptors and/or neurometabolites. There is a need to create a brain atlas that combines the anatomical and functional/neurochemical data independently available from MRI and PET. For this purpose, a three-dimensional atlas of the olive baboon brain was developed to enable multimodal imaging analysis. The atlas was created on a population-representative template encompassing 89 baboon brains. The atlas defines 24 brain regions, including the thalamus, cerebral cortex, putamen, corpus callosum, and insula. The atlas was evaluated with four MRI images and 20 PET images employing the radiotracers for [11C]benzamide, [11C]metergoline, [18F]FAHA, and [11C]rolipram, with and without structural aids like [18F]flurodeoxyglycose images. The atlas-based analysis pipeline includes automated segmentation, registration, quantification of region volume, the volume of distribution, and standardized uptake value. Results showed that, in comparison to PET analysis utilizing the "gold standard" manual quantification by neuroscientists, the performance of the atlas-based analysis was at >80 and >70% agreement for MRI and PET, respectively. The atlas can serve as a foundation for further refinement, and incorporation into a high-throughput workflow of baboon PET and MRI data. The new atlas is freely available on the Figshare online repository (https://doi.org/10.6084/m9.figshare.16663339), and the template images are available from neuroImaging tools & resources collaboratory (NITRC) (https://www.nitrc.org/projects/haiko89/).
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Affiliation(s)
- Artur Agaronyan
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, United States
- Molecular Imaging Laboratory, Department of Radiology, Howard University, Washington, DC, United States
| | - Raeyan Syed
- Molecular Imaging Laboratory, Department of Radiology, Howard University, Washington, DC, United States
| | - Ryan Kim
- Molecular Imaging Laboratory, Department of Radiology, Howard University, Washington, DC, United States
| | - Chao-Hsiung Hsu
- Molecular Imaging Laboratory, Department of Radiology, Howard University, Washington, DC, United States
| | - Scott A. Love
- CNRS, IFCE, INRAE, Université de Tours, PRC, Nouzilly, France
| | - Jacob M. Hooker
- Department of Radiology, Martinos Center, Boston, MA, United States
| | - Alicia E. Reid
- Department of Chemistry, Medgar Evers College, Brooklyn, NY, United States
| | - Paul C. Wang
- Molecular Imaging Laboratory, Department of Radiology, Howard University, Washington, DC, United States
- Department of Electrical Engineering, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Nobuyuki Ishibashi
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, United States
| | - Yeona Kang
- Department of Mathematics, Howard University, Washington, DC, United States
| | - Tsang-Wei Tu
- Molecular Imaging Laboratory, Department of Radiology, Howard University, Washington, DC, United States
- Department of Pediatrics, School of Medicine and Health Sciences, George Washington University, Washington, DC, United States
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