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Moradi H, Vashistha R, Ghosh S, O'Brien K, Hammond A, Rominger A, Sari H, Shi K, Vegh V, Reutens D. Automated extraction of the arterial input function from brain images for parametric PET studies. EJNMMI Res 2024; 14:33. [PMID: 38558200 PMCID: PMC11372015 DOI: 10.1186/s13550-024-01100-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images. RESULTS Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07. CONCLUSIONS Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.
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Affiliation(s)
- Hamed Moradi
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Rajat Vashistha
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Soumen Ghosh
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Kieran O'Brien
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Amanda Hammond
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Hasan Sari
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Viktor Vegh
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia.
| | - David Reutens
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
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Moradi H, Vashistha R, O'Brien K, Hammond A, Vegh V, Reutens D. A short 18F-FDG imaging window triple injection neuroimaging protocol for parametric mapping in PET. EJNMMI Res 2024; 14:1. [PMID: 38169031 PMCID: PMC10761663 DOI: 10.1186/s13550-023-01061-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND In parametric PET, kinetic parameters are extracted from dynamic PET images. It is not commonly used in clinical practice because of long scan times and the requirement for an arterial input function (AIF). To address these limitations, we designed an 18F-fluorodeoxyglucose (18F-FDG) triple injection dynamic PET protocol for brain imaging with a standard field of view PET scanner using a 24-min imaging window and an input function modeled using measurements from a region of interest placed over the left ventricle. METHODS To test the protocol in 6 healthy participants, we examined the quality of voxel-based maps of kinetic parameters in the brain generated using the two-tissue compartment model and compared estimated parameter values with previously published values. We also utilized data from a 36-min validation imaging window to compare (1) the modeled AIF against the input function measured in the validation window; and (2) the net influx rate ([Formula: see text]) computed using parameter estimates from the short imaging window against the net influx rate obtained using Patlak analysis in the validation window. RESULTS Compared to the AIF measured in the validation window, the input function estimated from the short imaging window achieved a mean area under the curve error of 9%. The voxel-wise Pearson's correlation between [Formula: see text] estimates from the short imaging window and the validation imaging window exceeded 0.95. CONCLUSION The proposed 24-min triple injection protocol enables parametric 18F-FDG neuroimaging with noninvasive estimation of the AIF from cardiac images using a standard field of view PET scanner.
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Affiliation(s)
- Hamed Moradi
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Rajat Vashistha
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Kieran O'Brien
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Amanda Hammond
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia.
| | - David Reutens
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
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Pijeira MSO, Nunes PSG, Chaviano SL, Diaz AMA, DaSilva JN, Ricci-Junior E, Alencar LMR, Chen X, Santos-Oliveira R. Medicinal (Radio) Chemistry: Building Radiopharmaceuticals for the Future. Curr Med Chem 2024; 31:5481-5534. [PMID: 37594105 DOI: 10.2174/0929867331666230818092634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/30/2023] [Accepted: 07/13/2023] [Indexed: 08/19/2023]
Abstract
Radiopharmaceuticals are increasingly playing a leading role in diagnosing, monitoring, and treating disease. In comparison with conventional pharmaceuticals, the development of radiopharmaceuticals does follow the principles of medicinal chemistry in the context of imaging-altered physiological processes. The design of a novel radiopharmaceutical has several steps similar to conventional drug discovery and some particularity. In the present work, we revisited the insights of medicinal chemistry in the current radiopharmaceutical development giving examples in oncology, neurology, and cardiology. In this regard, we overviewed the literature on radiopharmaceutical development to study overexpressed targets such as prostate-specific membrane antigen and fibroblast activation protein in cancer; β-amyloid plaques and tau protein in brain disorders; and angiotensin II type 1 receptor in cardiac disease. The work addresses concepts in the field of radiopharmacy with a special focus on the potential use of radiopharmaceuticals for nuclear imaging and theranostics.
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Affiliation(s)
- Martha Sahylí Ortega Pijeira
- Laboratory of Nanoradiopharmaceuticals and Synthesis of Novel Radiopharmaceuticals, Brazilian Nuclear Energy Commission, Nuclear Engineering Institute, Rio de Janeiro 21941906, Brazil
| | - Paulo Sérgio Gonçalves Nunes
- Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas SP13083-970, Brazil
| | - Samila Leon Chaviano
- Laboratoire de Biomatériaux pour l'Imagerie Médicale, Axe Médicine Régénératrice, Centre de Recherche du Centre Hospitalier Universitaire de Québec - Université Laval, Québec, QC, Canada
| | - Aida M Abreu Diaz
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
- Département de Pharmacologie et Physiologie, Faculté de Médecine, Université de Montréal, Montréal, Québec, Canada
- Institute de Génie Biomédical, Faculté de Médecine, Université de Montréal, Montréal, Québec, Canada
| | - Jean N DaSilva
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
- Département de Pharmacologie et Physiologie, Faculté de Médecine, Université de Montréal, Montréal, Québec, Canada
- Institute de Génie Biomédical, Faculté de Médecine, Université de Montréal, Montréal, Québec, Canada
| | - Eduardo Ricci-Junior
- Laboratório de Desenvolvimento Galênico, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, RJ, Brazil
| | - Luciana Magalhães Rebelo Alencar
- Laboratory of Biophysics and Nanosystems, Federal University of Maranhão, Av. dos Portugueses, 1966, Vila Bacanga, São Luís MA65080-805, Brazil
| | - Xiaoyuan Chen
- Departments of Diagnostic Radiology, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and Faculty of Engineering, National University of Singapore 117597, Singapore
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 117599, Singapore
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore 117597, Singapore
| | - Ralph Santos-Oliveira
- Laboratory of Nanoradiopharmaceuticals and Synthesis of Novel Radiopharmaceuticals, Brazilian Nuclear Energy Commission, Nuclear Engineering Institute, Rio de Janeiro 21941906, Brazil
- Laboratory of Radiopharmacy and Nanoradiopharmaceuticals, Rio de Janeiro State University, Rio de Janeiro 23070200, Brazil
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Volpi T, Maccioni L, Colpo M, Debiasi G, Capotosti A, Ciceri T, Carson RE, DeLorenzo C, Hahn A, Knudsen GM, Lammertsma AA, Price JC, Sossi V, Wang G, Zanotti-Fregonara P, Bertoldo A, Veronese M. An update on the use of image-derived input functions for human PET studies: new hopes or old illusions? EJNMMI Res 2023; 13:97. [PMID: 37947880 PMCID: PMC10638226 DOI: 10.1186/s13550-023-01050-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome. MAIN BODY This paper summarizes the limitations of IDIF methods for quantitative PET imaging and discusses some of the advances that may make IDIF extraction more reliable. The introduction of automated pipelines (both commercial and open-source) for clinical PET scanners is discussed as a way to improve the reliability of IDIF approaches and their utility for quantitative purposes. Survey data gathered from the PET community are then presented to understand whether the field's opinion of the usefulness and validity of IDIF is improving. Finally, as the introduction of next-generation PET scanners with long axial fields of view, ultra-high sensitivity, and improved spatial and temporal resolution, has also brought IDIF methods back into the spotlight, a discussion of the possibilities offered by these state-of-the-art scanners-inclusion of large vessels, less partial volume in small vessels, better description of the full IDIF kinetics, whole-body modeling of radiometabolite production-is included, providing a pathway for future use of IDIF. CONCLUSION Improvements in PET scanner technology and software for automated IDIF extraction may allow to solve some of the major limitations associated with IDIF, such as partial volume effects and poor temporal sampling, with the exciting potential for accurate estimation of single kinetic rates. Nevertheless, until individualized radiometabolite correction can be performed effectively, IDIF approaches remain confined at best to a few tracers.
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Affiliation(s)
- Tommaso Volpi
- Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, PO Box 208048, New Haven, CT, 06520-8048, USA.
| | - Lucia Maccioni
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Maria Colpo
- Department of Information Engineering, University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
| | - Giulia Debiasi
- Department of Information Engineering, University of Padova, Padua, Italy
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padua, Italy
| | - Amedeo Capotosti
- Department of Information Engineering, University of Padova, Padua, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Tommaso Ciceri
- Department of Information Engineering, University of Padova, Padua, Italy
- Neuroimaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, LC, Italy
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, PO Box 208048, New Haven, CT, 06520-8048, USA
| | - Christine DeLorenzo
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Healthy (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Gitte Moos Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Adriaan A Lammertsma
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, Netherlands
| | - Julie C Price
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Guobao Wang
- Department of Radiology, University of California Davis Medical Center, Sacramento, CA, USA
| | | | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
| | - Mattia Veronese
- Department of Information Engineering, University of Padova, Padua, Italy
- Department of Neuroimaging, King's College London, London, UK
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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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Wu Y, Feng T, Shen Y, Fu F, Meng N, Li X, Xu T, Sun T, Gu F, Wu Q, Zhou Y, Han H, Bai Y, Wang M. Total-body parametric imaging using the patlak model: Feasibility of reduced scan time. Med Phys 2022; 49:4529-4539. [PMID: 35394071 DOI: 10.1002/mp.15647] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 01/17/2022] [Accepted: 03/19/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE This study explored the feasibility of reducing the scan time of Patlak parametric imaging on the uEXPLORER. METHODS A total of 65 patients (27 females and 38 males, age 56.1±10.4) were recruited in this study. 18 F-FDG was injected and its dose was adjusted by body weight (4.07 MBq / kg).Total-body dynamic scanning was performed on the uEXPLORER Total-Body PET/CT scanner with a total scan time of 60 minutes from the injection. The image derived input function (IDIF) was obtained from the aortic arch. The voxelwise Patlak analysis was applied to generate the Ki images designated as GIDIF with different acquisition times (20-60, 30-60, 40-60, and 44-60 min). The population-based input function (PBIF) was constructed from the mean value of the IDIF from the population, and Ki images designated as GPBIF were generated using the PBIF. Non-localmeans (NLM) denoising was applied to the generated images to get two extra groups of (NLM-designated) images: GIDIF+NLM and GPBIF+NLM . Two radiologists evaluated the overall image quality, noise, and lesion detectability of the Ki images from different groups. The 20-60 min scans in GIDIF were selected as the gold standard for each patient. We determined that image quality is at sufficient level if all the lesions can be recognized and meet the clinical criteria. Ki values in muscle and lesion were compared across different groups to evaluate the quantitative accuracy. RESULTS The overall image quality, image noise, and lesion conspicuity were significantly better in long time series than short time series in all 4 groups (all p<0.001). The Ki images in the GIDIF and GPBIF groups generated from 30-min scans showed diagnostic value equivalent to the 40-min scans of GIDIF . While the image quality of the 16-min scans was poor, all lesions could still be detected. No significant difference was found between Ki values estimated with GIDIF and GPBIF in muscle and lesion regions (all p>0.5). After applying the NLM filter, The coefficient of variation could be reduced on the order of [1%, 15%, 19%,37%] and [110%, 125%, 94%, 69%] with four acquisition time schemes for lesion and muscle. The reduction percentage did not have a substantial difference in IDIF and PBIF group. The Ki images in the GIDIF+NLM and GPBIF+NLM groups generated from the 20-min acquisitions showed acceptable quality. All lesions could be found on the NLM processed images of the 16-min scans. No significant difference was found between Ki values produced with GIDIF+NLM and GPBIF+NLM in muscle and lesion regions(all p>0.7). CONCLUSIONS The Ki images generated by the PBIF-based Patlak model using a 20-min dynamic scan with the NLM filter achieved a similar diagnostic efficiency to images with GIDIF from 40-min dynamic data, and there is no significant difference between Ki images generated using IDIF or PBIF (p>0.5). This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yaping Wu
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Tao Feng
- UIH America Inc., Houston, TX, 77054, USA
| | - Yu Shen
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Fangfang Fu
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Nan Meng
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Xiaochen Li
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Tianyi Xu
- United Imaging Healthcare Group, Shanghai, 201807, China
| | - Tao Sun
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Fengyun Gu
- United Imaging Healthcare Group, Shanghai, 201807, China.,Department of Statistics, School of Mathematical Sciences, University College Cork, Cork, T12XF62, Ireland
| | - Qi Wu
- United Imaging Healthcare Group, Shanghai, 201807, China.,Department of Statistics, School of Mathematical Sciences, University College Cork, Cork, T12XF62, Ireland
| | - Yun Zhou
- United Imaging Healthcare Group, Shanghai, 201807, China
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Yan Bai
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
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Wu Y, Feng T, Zhao Y, Xu T, Fu F, Huang Z, Meng N, Li H, Shao F, Wang M. Whole-body Parametric Imaging of FDG PET using uEXPLORER with Reduced Scan Time. J Nucl Med 2021; 63:622-628. [PMID: 34385335 PMCID: PMC8973287 DOI: 10.2967/jnumed.120.261651] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 06/14/2021] [Indexed: 11/25/2022] Open
Abstract
Parametric imaging of the net influx rate (Ki) in 18F-FDG PET has been shown to provide improved quantification and specificity for cancer detection compared with SUV imaging. Current methods of generating parametric images usually require a long dynamic scanning time. With the recently developed uEXPLORER scanner, a dramatic increase in sensitivity has reduced the noise in dynamic imaging, making it more robust to use a nonlinear estimation method and flexible protocols. In this work, we explored 2 new possible protocols besides the standard 60-min one for the possibility of reducing scanning time for Ki imaging. Methods: The gold standard protocol (protocol 1) was conventional dynamic scanning with a 60-min scanning time. The first proposed protocol (protocol 2) included 2 scanning periods: 0–4 min and 54–60 min after injection. The second proposed protocol (protocol 3) consisted of a single scanning period from 50 to 60 min after injection, with a second injection applied at 56 min. The 2 new protocols were simulated from the 60-min standard scans. A hybrid input function combining the population-based input function and the image-derived input function (IDIF) was used. The results were also compared with the IDIF acquired from protocol 1. A previously developed maximum-likelihood approach was used to estimate the Ki images. In total, 7 cancer patients imaged using the uEXPLORER scanner were enrolled in this study. Lesions were identified from the patient data, and the lesion Ki values were compared among the different protocols. Results: The acquired hybrid input function was comparable in shape to the IDIF for each patient. The average difference in area under the curve was about 3%, suggesting good quantitative accuracy. The visual difference between the Ki images generated using IDIF and those generated using the hybrid input function was also minimal. The acquired Ki images using different protocols were visually comparable. The average Ki difference in the lesions was 2.8% ± 2.1% for protocol 2 and 1% ± 2.2% for protocol 3. Conclusion: The results suggest that it is possible to acquire Ki images using the nonlinear estimation approach with a much-reduced scanning time. Between the 2 new protocols, the protocol with dual injection shows the greatest promise in terms of practicality.
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Affiliation(s)
- Yaping Wu
- Department of Medical Imaging, Henan Provincial People's Hospital, China
| | | | | | | | - Fangfang Fu
- Department of Medical Imaging, Henan Provincial People's Hospital, China
| | - Zhun Huang
- Department of Medical Imaging, Henan Provincial People's Hospital, China
| | - Nan Meng
- Department of Medical Imaging, Henan Provincial People's Hospital, China
| | | | - Fengmin Shao
- Department of Medical Imaging, Henan Provincial People's Hospital, China, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, China
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Mertens N, Schmidt ME, Hijzen A, Van Weehaeghe D, Ravenstijn P, Depre M, de Hoon J, Van Laere K, Koole M. Minimally invasive quantification of cerebral P2X7R occupancy using dynamic [ 18F]JNJ-64413739 PET and MRA-driven image derived input function. Sci Rep 2021; 11:16172. [PMID: 34373571 PMCID: PMC8352986 DOI: 10.1038/s41598-021-95715-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 07/29/2021] [Indexed: 01/21/2023] Open
Abstract
[18F]JNJ-64413739 has been evaluated as PET-ligand for in vivo quantification of purinergic receptor subtype 7 receptor (P2X7R) using Logan graphical analysis with a metabolite-corrected arterial plasma input function. In the context of a P2X7R PET dose occupancy study, we evaluated a minimally invasive approach by limiting arterial sampling to baseline conditions. Meanwhile, post dose distribution volumes (VT) under blocking conditions were estimated by combining baseline blood to plasma ratios and metabolite fractions with an MR angiography driven image derived input function (IDIF). Regional postdose VT,IDIF values were compared with corresponding VT,AIF estimates using a arterial input function (AIF), in terms of absolute values, test–retest reliability and receptor occupancy. Compared to an invasive AIF approach, postdose VT,IDIF values and corresponding receptor occupancies showed only limited bias (Bland–Altman analysis: 0.06 ± 0.27 and 3.1% ± 6.4%) while demonstrating a high correlation (Spearman ρ = 0.78 and ρ = 0.98 respectively). In terms of test–retest reliability, regional intraclass correlation coefficients were 0.98 ± 0.02 for VT,IDIF compared to 0.97 ± 0.01 for VT,AIF. These results confirmed that a postdose IDIF, guided by MR angiography and using baseline blood and metabolite data, can be considered for accurate [18F]JNJ-64413739 PET quantification in a repeated PET study design, thus avoiding multiple invasive arterial sampling and increasing dosing flexibility.
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Affiliation(s)
- Nathalie Mertens
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, University Hospital and KU Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | | | - Anja Hijzen
- Janssen Research and Development, Beerse, Belgium
| | - Donatienne Van Weehaeghe
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, University Hospital and KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | | | - Marleen Depre
- Center for Clinical Pharmacology, University Hospital and KU Leuven, Leuven, Belgium
| | - Jan de Hoon
- Center for Clinical Pharmacology, University Hospital and KU Leuven, Leuven, Belgium
| | - Koen Van Laere
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, University Hospital and KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Michel Koole
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, University Hospital and KU Leuven, Herestraat 49, 3000, Leuven, Belgium
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9
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Yao S, Feng T, Zhao Y, Wu R, Wang R, Wu S, Li C, Xu B. Simplified protocol for whole-body Patlak parametric imaging with 18 F-FDG PET/CT: Feasibility and error analysis. Med Phys 2021; 48:2160-2169. [PMID: 32304095 DOI: 10.1002/mp.14187] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 03/24/2020] [Accepted: 03/28/2020] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Parametric imaging using the Patlak model has been shown to provide improved lesion detectability and specificity. The Patlak model requires both tissue time-activity curves (TACs) after equilibrium and knowledge of the input function from the start of injection. Therefore, the conventional dynamic scanning protocol typically starts from the radiotracer injection all the way to equilibrium. In this paper, we propose the use of hybrid population-based and model-based input function estimation and evaluate its use for whole-body Patlak analysis, in order to reduce the total scan time and simplify clinical Patlak parametric imaging protocols. Possible quantitative errors caused by the simplified scanning protocol were also analyzed both theoretically and with the use of clinical data. MATERIALS AND METHODS Clinical data from 24 patients referred for tumor staging were included in this study. The patients underwent a whole-body dynamic PET study, 20 min after FDG injection (0.13 mCi/kg). The proposed whole-body scanning protocol includes 6 passes with 4-5 bed positions, depending on the size of the patient, with 2 min for each bed position. An input function from the literature was selected as the shape of the population-based input function. The descending aorta from the corresponding CT image was segmented and applied on the reconstructed dynamic PET images to acquire an image-based input function, which was later fitted using an exponential model. Due to the late scan time, only the later portion of the input function was available, which was used to scale the population-based input function. The hybrid input function was used to derive the whole-body Patlak images. Assuming a given error in the population-based input function, its influence on the final Patlak images were also derived theoretically and verified using the clinical data sets. Finally, the image quality of the reconstructed Patlak slope image was evaluated by an experienced radiologist in four different aspects: image artifacts, image noise, lesion sharpness, and lesion detectability. RESULTS It was found that errors in the population-based input function only affect the absolute scale of the Patlak slope image. The induced error is proportional to the percentage area-under-curve (AUC) error in the input function. These findings were also confirmed by numerical analysis. The predicted global scale was in good agreement with results from both image-based Patlak and direct Patlak approach. The fractions of the AUC from the early portion population-based input function were also found to be around 18% of the total AUC of the input function, further limiting the propagation of quantitation error from population-based input function to the final Patlak slope image. The reconstructed Patlak images were also found by the radiologist to provide excellent confidence in lesion detection tasks. CONCLUSIONS We have proposed a simplified whole-body scanning protocol that utilizes both population-based input function and model-based input function. The error from the population-based function was found to only affect the global scale and the overall quantitative impact can be predicted using our proposed formulas.
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Affiliation(s)
- Shulin Yao
- Department of Nuclear Medicine, Chinese PLA General Hospital, Beijing, 100853, China
| | - Tao Feng
- UIH America, Inc, Houston, TX, 75054, USA
| | - Yizhang Zhao
- Shanghai United Imaging Healthcare, Shanghai, 201807, China
| | - Runze Wu
- Shanghai United Imaging Healthcare, Shanghai, 201807, China
| | - Ruimin Wang
- Department of Nuclear Medicine, Chinese PLA General Hospital, Beijing, 100853, China
| | - Shina Wu
- Department of Nuclear Medicine, Chinese PLA General Hospital, Beijing, 100853, China
| | - Can Li
- Department of Nuclear Medicine, Chinese PLA General Hospital, Beijing, 100853, China
| | - Baixuan Xu
- Department of Nuclear Medicine, Chinese PLA General Hospital, Beijing, 100853, China
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10
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Feng T, Zhao Y, Shi H, Li H, Zhang X, Wang G, Price PM, Badawi RD, Cherry SR, Jones T. Total-Body Quantitative Parametric Imaging of Early Kinetics of 18F-FDG. J Nucl Med 2020; 62:738-744. [PMID: 32948679 DOI: 10.2967/jnumed.119.238113] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 08/06/2020] [Indexed: 02/01/2023] Open
Abstract
Parametric imaging has been shown to provide better quantitation physiologically than SUV imaging in PET. With the increased sensitivity from a recently developed total-body PET scanner, whole-body scans with higher temporal resolution become possible for dynamic analysis and parametric imaging. In this paper, we focus on deriving the parameter k 1 using compartmental modeling and on developing a method to acquire whole-body 18F-FDG PET parametric images using only the first 90 s of the postinjection scan data with the total-body PET system. Methods: Dynamic projections were acquired with a time interval of 1 s for the first 30 s and a time interval of 2 s for the following minute. Image-derived input functions were acquired from the reconstructed dynamic sequences in the ascending aorta. A 1-tissue-compartment model with 4 parameters (k 1, k 2, blood fraction, and delay time) was used. A maximum-likelihood-based estimation method was developed with the 1-tissue-compartment model solution. The accuracy of the acquired parameters was compared with the ones estimated using a 2-tissue-compartment irreversible model with 1-h-long data. Results: All 4 parametric images were successfully calculated using data from 2 volunteers. By comparing the time-activity curves acquired from the volumes of interest, we showed that the parameters estimated using our method were able to predict the time-activity curves of the early dynamics of 18F-FDG in different organs. The delay-time effects for different organs were also clearly visible in the reconstructed delay-time image with delay variations of as large as 40 s. The estimated parameters using both 90-s data and 1-h data agreed well for k 1 and blood fraction, whereas a large difference in k 2 was found between the 90-s and 1-h data, suggesting k 2 cannot be reliably estimated from the 90-s scan. Conclusion: We have shown that with total-body PET and the increased sensitivity, it is possible to estimate parametric images based on the very early dynamics after 18F-FDG injection. The estimated k 1 might potentially be used clinically as an indicator for identifying abnormalities.
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Affiliation(s)
- Tao Feng
- UIH America Inc., Houston, Texas
| | | | - Hongcheng Shi
- Zhongshan Hospital, Fudan University, Shanghai, China
| | | | - Xuezhu Zhang
- Department of Biomedical Engineering, University of California Davis, Davis, California
| | - Guobao Wang
- Department of Biomedical Engineering, University of California Davis, Davis, California
| | | | - Ramsey D Badawi
- Department of Biomedical Engineering, University of California Davis, Davis, California.,Department of Radiology, University of California Davis Medical Center, Davis, California
| | - Simon R Cherry
- Department of Biomedical Engineering, University of California Davis, Davis, California.,Department of Radiology, University of California Davis Medical Center, Davis, California
| | - Terry Jones
- Department of Radiology, University of California Davis Medical Center, Davis, California
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11
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Chen X, Hirano M, Werner RA, Decker M, Higuchi T. Novel 18F-Labeled PET Imaging Agent FV45 Targeting the Renin-Angiotensin System. ACS OMEGA 2018; 3:10460-10470. [PMID: 30288456 PMCID: PMC6166228 DOI: 10.1021/acsomega.8b01885] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 08/14/2018] [Indexed: 06/08/2023]
Abstract
Renin-angiotensin system (RAS) plays an important role in the regulation of blood pressure and hormonal balance. Using positron emission tomography (PET) technology, it is possible to monitor the physiological and pathological distribution of angiotensin II type 1 receptors (AT1), which reflects the functionality of RAS. A new 18F-labeled PET tracer derived from the clinically used AT1 antagonist valsartan showing the least possible chemical alteration from the valsartan structure has been designed and synthesized with several strategies, which can be applied for the syntheses of further derivatives. Radioligand binding study showed that the cold reference FV45 (K i 14.6 nM) has almost equivalent binding affinity as its lead valsartan (K i 11.8 nM) and angiotensin II (K i 1.7 nM). Successful radiolabeling of FV45 in a one-pot radiofluorination followed by the deprotection procedure with 21.8 ± 8.5% radiochemical yield and >99% radiochemical purity (n = 5) enabled a distribution study in rats and opened a path to straightforward large-scale production. A fast and clear kidney uptake could be observed, and this renal uptake could be selectively blocked by pretreatment with AT1-selective antagonist valsartan. Overall, as the first 18F-labeled PET tracer based on a derivation from clinically used drug valsartan with almost identical chemical structure, [18F]FV45 will be a new tool for assessing the RAS function by visualizing AT1 receptor distributions and providing further information regarding cardiovascular system malfunction as well as possible applications in inflammation research and cancer diagnosis.
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Affiliation(s)
- Xinyu Chen
- Department
of Nuclear Medicine, Comprehensive Heart Failure Center, University
Hospital of Würzburg, Würzburg 97080, Germany
| | - Mitsuru Hirano
- Department
of Bio-Medical Imaging, National Cerebral
and Cardiovascular Center, Osaka 565-0873, Japan
| | - Rudolf A. Werner
- Department
of Nuclear Medicine, Comprehensive Heart Failure Center, University
Hospital of Würzburg, Würzburg 97080, Germany
- The
Russell H. Morgan Department of Radiology and Radiological Science,
Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Michael Decker
- Institute of Pharmacy and Food Chemistry, University of Würzburg, Würzburg 97074, Germany
| | - Takahiro Higuchi
- Department
of Nuclear Medicine, Comprehensive Heart Failure Center, University
Hospital of Würzburg, Würzburg 97080, Germany
- Department
of Bio-Medical Imaging, National Cerebral
and Cardiovascular Center, Osaka 565-0873, Japan
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