1
|
Zhu Y, Tran Q, Wang Y, Badawi RD, Cherry SR, Qi J, Abbaszadeh S, Wang G. Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging. Neuroimage 2024; 293:120611. [PMID: 38643890 DOI: 10.1016/j.neuroimage.2024.120611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 04/11/2024] [Accepted: 04/12/2024] [Indexed: 04/23/2024] Open
Abstract
Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation. METHODS The conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored. RESULTS The estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method. CONCLUSION The proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.
Collapse
Affiliation(s)
- Yansong Zhu
- Department of Radiology, University of California Davis Medical Center, Sacramento, CA 95817, USA.
| | - Quyen Tran
- Department of Radiology, University of California Davis Medical Center, Sacramento, CA 95817, USA
| | - Yiran Wang
- Department of Radiology, University of California Davis Medical Center, Sacramento, CA 95817, USA; Department of Biomedical Engineering, University of California at Davis, Davis, CA 95616, USA
| | - Ramsey D Badawi
- Department of Radiology, University of California Davis Medical Center, Sacramento, CA 95817, USA; Department of Biomedical Engineering, University of California at Davis, Davis, CA 95616, USA
| | - Simon R Cherry
- Department of Radiology, University of California Davis Medical Center, Sacramento, CA 95817, USA; Department of Biomedical Engineering, University of California at Davis, Davis, CA 95616, USA
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California at Davis, Davis, CA 95616, USA
| | - Shiva Abbaszadeh
- Department of Electrical and Computer Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA
| | - Guobao Wang
- Department of Radiology, University of California Davis Medical Center, Sacramento, CA 95817, USA
| |
Collapse
|
2
|
Reed MB, Handschuh PA, Schmidt C, Murgaš M, Gomola D, Milz C, Klug S, Eggerstorfer B, Aichinger L, Godbersen GM, Nics L, Traub-Weidinger T, Hacker M, Lanzenberger R, Hahn A. Validation of cardiac image-derived input functions for functional PET quantification. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06716-8. [PMID: 38676734 DOI: 10.1007/s00259-024-06716-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/14/2024] [Indexed: 04/29/2024]
Abstract
PURPOSE Functional PET (fPET) is a novel technique for studying dynamic changes in brain metabolism and neurotransmitter signaling. Accurate quantification of fPET relies on measuring the arterial input function (AIF), traditionally achieved through invasive arterial blood sampling. While non-invasive image-derived input functions (IDIF) offer an alternative, they suffer from limited spatial resolution and field of view. To overcome these issues, we developed and validated a scan protocol for brain fPET utilizing cardiac IDIF, aiming to mitigate known IDIF limitations. METHODS Twenty healthy individuals underwent fPET/MR scans using [18F]FDG or 6-[18F]FDOPA, utilizing bed motion shuttling to capture cardiac IDIF and brain task-induced changes. Arterial and venous blood sampling was used to validate IDIFs. Participants performed a monetary incentive delay task. IDIFs from various blood pools and composites estimated from a linear fit over all IDIF blood pools (3VOI) and further supplemented with venous blood samples (3VOIVB) were compared to the AIF. Quantitative task-specific images from both tracers were compared to assess the performance of each input function to the gold standard. RESULTS For both radiotracer cohorts, moderate to high agreement (r: 0.60-0.89) between IDIFs and AIF for both radiotracer cohorts was observed, with further improvement (r: 0.87-0.93) for composite IDIFs (3VOI and 3VOIVB). Both methods showed equivalent quantitative values and high agreement (r: 0.975-0.998) with AIF-derived measurements. CONCLUSION Our proposed protocol enables accurate non-invasive estimation of the input function with full quantification of task-specific changes, addressing the limitations of IDIF for brain imaging by sampling larger blood pools over the thorax. These advancements increase applicability to any PET scanner and clinical research setting by reducing experimental complexity and increasing patient comfort.
Collapse
Affiliation(s)
- Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Patricia Anna Handschuh
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Clemens Schmidt
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - David Gomola
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Christian Milz
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Benjamin Eggerstorfer
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Lisa Aichinger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Godber Mathis Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Lukas Nics
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria.
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| |
Collapse
|
3
|
Holy EN, Li E, Bhattarai A, Fletcher E, Alfaro ER, Harvey DJ, Spencer BA, Cherry SR, DeCarli CS, Fan AP. Non-invasive quantification of 18F-florbetaben with total-body EXPLORER PET. EJNMMI Res 2024; 14:39. [PMID: 38625413 PMCID: PMC11021392 DOI: 10.1186/s13550-024-01104-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/02/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Kinetic modeling of 18F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort. METHODS 18F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer's disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 s after injection. Dynamic time-activity curves (TACs) for 110 min were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (VT, Vs) in key brain regions with early amyloid accumulation. Non-displaceable binding potential ([Formula: see text] was also calculated from the multi-reference tissue model (MRTM). RESULTS Amyloid-positive (AD) patients showed the highest VT and VS in anterior cingulate, posterior cingulate, and precuneus, consistent with [Formula: see text] analysis. [Formula: see text]and VT from kinetic models were correlated (r² = 0.46, P < 2[Formula: see text] with a stronger positive correlation observed in amyloid-positive participants, indicating reliable model fits with the IDIFs. VT from 2TCM was highly correlated ([Formula: see text]= 0.65, P < 2[Formula: see text]) with Logan graphical VT estimation. CONCLUSION Non-invasive quantification of amyloid binding from total-body 18F-florbetaben PET data is feasible using aorta IDIFs with high agreement between kinetic distribution volume parameters compared to [Formula: see text]in amyloid-positive and amyloid-negative older individuals.
Collapse
Affiliation(s)
- Emily Nicole Holy
- Department of Neurology, University of California (UC) Davis Health, 1590 Drew Avenue, Davis, CA, 95618, USA.
- Department of Biomedical Engineering, UC Davis, Davis, USA.
| | - Elizabeth Li
- Department of Biomedical Engineering, UC Davis, Davis, USA
| | - Anjan Bhattarai
- Department of Neurology, University of California (UC) Davis Health, 1590 Drew Avenue, Davis, CA, 95618, USA
- Department of Biomedical Engineering, UC Davis, Davis, USA
| | - Evan Fletcher
- Department of Neurology, University of California (UC) Davis Health, 1590 Drew Avenue, Davis, CA, 95618, USA
| | - Evelyn R Alfaro
- Department of Neurology, University of California (UC) Davis Health, 1590 Drew Avenue, Davis, CA, 95618, USA
| | | | - Benjamin A Spencer
- Department of Biomedical Engineering, UC Davis, Davis, USA
- Department of Radiology, UC Davis Health, Davis, USA
| | - Simon R Cherry
- Department of Biomedical Engineering, UC Davis, Davis, USA
- Department of Radiology, UC Davis Health, Davis, USA
| | - Charles S DeCarli
- Department of Neurology, University of California (UC) Davis Health, 1590 Drew Avenue, Davis, CA, 95618, USA
| | - Audrey P Fan
- Department of Neurology, University of California (UC) Davis Health, 1590 Drew Avenue, Davis, CA, 95618, USA
- Department of Biomedical Engineering, UC Davis, Davis, USA
| |
Collapse
|
4
|
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 DOI: 10.1186/s13550-024-01100-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
5
|
Kogan F, Yoon D, Teeter MG, Chaudhari AJ, Hales L, Barbieri M, Gold GE, Vainberg Y, Goyal A, Watkins L. Multimodal positron emission tomography (PET) imaging in non-oncologic musculoskeletal radiology. Skeletal Radiol 2024:10.1007/s00256-024-04640-4. [PMID: 38492029 DOI: 10.1007/s00256-024-04640-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 03/18/2024]
Abstract
Musculoskeletal (MSK) disorders are associated with large impacts on patient's pain and quality of life. Conventional morphological imaging of tissue structure is limited in its ability to detect pain generators, early MSK disease, and rapidly assess treatment efficacy. Positron emission tomography (PET), which offers unique capabilities to evaluate molecular and metabolic processes, can provide novel information about early pathophysiologic changes that occur before structural or even microstructural changes can be detected. This sensitivity not only makes it a powerful tool for detection and characterization of disease, but also a tool able to rapidly assess the efficacy of therapies. These benefits have garnered more attention to PET imaging of MSK disorders in recent years. In this narrative review, we discuss several applications of multimodal PET imaging in non-oncologic MSK diseases including arthritis, osteoporosis, and sources of pain and inflammation. We also describe technical considerations and recent advancements in technology and radiotracers as well as areas of emerging interest for future applications of multimodal PET imaging of MSK conditions. Overall, we present evidence that the incorporation of PET through multimodal imaging offers an exciting addition to the field of MSK radiology and will likely prove valuable in the transition to an era of precision medicine.
Collapse
Affiliation(s)
- Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | - Daehyun Yoon
- Department of Radiology, University of California-San Francisco, San Francisco, CA, USA
| | - Matthew G Teeter
- Department of Medical Biophysics, Western University, London, ON, Canada
| | | | - Laurel Hales
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Marco Barbieri
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Yael Vainberg
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Ananya Goyal
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Lauren Watkins
- Department of Radiology, Stanford University, Stanford, CA, USA
| |
Collapse
|
6
|
Zhao R, Xia Z, Ke M, Lv J, Zhong H, He Y, Gu D, Liu Y, Zeng G, Zhu L, Alexoff D, Kung HF, Wang X, Sun T. Determining the optimal pharmacokinetic modelling and simplified quantification method of [ 18F]AlF-P16-093 for patients with primary prostate cancer (PPCa). Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06624-x. [PMID: 38285206 DOI: 10.1007/s00259-024-06624-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/20/2024] [Indexed: 01/30/2024]
Abstract
PURPOSE This paper discusses the optimization of pharmacokinetic modelling and alternate simplified quantification method for [18F]AlF-P16-093, a novel tracer for in vivo imaging of prostate cancer. METHODS Dynamic PET/CT scans were conducted on eight primary prostate cancer patients, followed by a whole-body scan at 60 min post-injection. Time-activity curves (TACs) were obtained by drawing volumes of interest for primary prostatic and metastatic lesions. Optimal kinetic modelling involved evaluating three compartmental models (1T2K, 2T3K, and 2T4K) accounting for fractional blood volume (Vb). The simplified quantification method was then determined based on the correlation between the static uptake measure and total distribution volume (Vt) obtained from the optimal pharmacokinetic analysis. RESULTS In total, 17 intraprostatic lesions, 10 lymph nodes, and 36 osseous metastases were evaluated. Visually, the contrast of the tumor increased and showed the steepest incline within the first few minutes, whereas background activity decreased over time. Full pharmacokinetic analysis revealed that a reversible two-compartmental (2T4K) model is the preferred kinetic model for the given tracer. The kinetic parameters K1, k3, Vb, and Vt were all significantly higher in lesions when compared with normal tissue (P < 0.01). Several simplified protocols were tested for approximating comprehensive dynamic quantification in tumors, with image-based SURmean (the ratio of tumor SUVmean to blood SUVmean) within the 28-34 min window found to be sufficient for approximating the total distribution Vt values (R2 = 0.949, P < 0.01). Both Vt and SURmean correlated significantly with the total serum prostate-specific antigen (tPSA) levels (P < 0.01). CONCLUSIONS This study introduced an optimized pharmacokinetic modelling approach and a simplified acquisition method for [18F]AlF-P16-093, a novel PSMA-targeted radioligand, highlighting the feasibility of utilizing one static PET imaging (between 30 and 60 min) for the diagnosis of prostate cancer. Note that the image-derived input function in this study may not reflect the true corrected plasma input function, therefore the interpretation of the associated kinetic parameter estimates should be done with caution.
Collapse
Affiliation(s)
- Ruiyue Zhao
- Department of Nuclear Medicine, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Zeheng Xia
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Miao Ke
- Department of Nuclear Medicine, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Jie Lv
- Department of Nuclear Medicine, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Huizhen Zhong
- Department of Nuclear Medicine, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Yulu He
- Department of Nuclear Medicine, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Di Gu
- Department of Urology and Guangdong Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510230, Guangdong, China
| | - Yongda Liu
- Department of Urology and Guangdong Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510230, Guangdong, China
| | - Guohua Zeng
- Department of Urology and Guangdong Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510230, Guangdong, China
| | - Lin Zhu
- Department of Nuclear Medicine, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
- College of Chemistry, Beijing Normal University, Beijing, 100875, China
| | - David Alexoff
- Five Eleven Pharma Inc., 3700 Market St., Philadelphia, PA, 19104, USA
| | - Hank F Kung
- Five Eleven Pharma Inc., 3700 Market St., Philadelphia, PA, 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xinlu Wang
- Department of Nuclear Medicine, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
| | - Tao Sun
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China.
| |
Collapse
|
7
|
Holy EN, Li E, Bhattarai A, Fletcher E, Alfaro ER, Harvey DJ, Spencer BA, Cherry SR, DeCarli CS, Fan AP. Non-invasive quantification of 18F-florbetaben with total-body EXPLORER PET. Res Sq 2023:rs.3.rs-3764930. [PMID: 38234716 PMCID: PMC10793501 DOI: 10.21203/rs.3.rs-3764930/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Purpose Kinetic modeling of 18F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort. Methods 18F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer's disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 seconds after injection. Dynamic time-activity curves (TACs) for 110 minutes were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (VT, Vs) in key brain regions with early amyloid accumulation. Non-displaceable binding potential (BPND) was also calculated from the multi-reference tissue model (MRTM). Results Amyloid-positive (AD) patients showed the highest VT and VS in anterior cingulate, posterior cingulate, and precuneus, consistent with BPND analysis. BPND and VT from kinetic models were correlated (r2 = 0.46, P<2e-16) with a stronger positive correlation observed in amyloid-positive participants, indicating reliable model fits with the IDIFs. VT from 2TCM was highly correlated (r2 = 0.65, P< 2e-16) with Logan graphical VT estimation. Conclusion Non-invasive quantification of amyloid binding from total-body 18F-florbetaben PET data is feasible using aorta IDIFs with high agreement between kinetic distribution volume parameters compared to BPND in amyloid-positive and negative older individuals.
Collapse
Affiliation(s)
- Emily N Holy
- Department of Neurology, University of California (UC) Davis Health
- Department of Biomedical Engineering, UC Davis
| | | | - Anjan Bhattarai
- Department of Neurology, University of California (UC) Davis Health
- Department of Biomedical Engineering, UC Davis
| | - Evan Fletcher
- Department of Neurology, University of California (UC) Davis Health
| | - Evelyn R Alfaro
- Department of Neurology, University of California (UC) Davis Health
| | | | - Benjamin A Spencer
- Department of Biomedical Engineering, UC Davis
- Department of Radiology, UC Davis Health
| | - Simon R Cherry
- Department of Biomedical Engineering, UC Davis
- Department of Radiology, UC Davis Health
| | | | - Audrey P Fan
- Department of Neurology, University of California (UC) Davis Health
- Department of Biomedical Engineering, UC Davis
| |
Collapse
|