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Shiyam Sundar LK, Gutschmayer S, Maenle M, Beyer T. Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence. Cancer Imaging 2024; 24:51. [PMID: 38605408 PMCID: PMC11010281 DOI: 10.1186/s40644-024-00684-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/03/2024] [Indexed: 04/13/2024] Open
Abstract
The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI's integration into PET imaging workflows-spanning from image acquisition to data analysis-marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT's functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology's capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI's role in enhancing TB-PET's efficiency and addresses the challenges posed by TB-PET's increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research.
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Affiliation(s)
| | - Sebastian Gutschmayer
- Quantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Vienna, Austria
| | - Marcel Maenle
- Quantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Vienna, Austria
| | - Thomas Beyer
- Quantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Vienna, Austria
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Kas A, Rozenblum L, Pyatigorskaya N. Clinical Value of Hybrid PET/MR Imaging: Brain Imaging Using PET/MR Imaging. Magn Reson Imaging Clin N Am 2023; 31:591-604. [PMID: 37741643 DOI: 10.1016/j.mric.2023.06.004] [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] [Indexed: 09/25/2023]
Abstract
Hybrid PET/MR imaging offers a unique opportunity to acquire MR imaging and PET information during a single imaging session. PET/MR imaging has numerous advantages, including enhanced diagnostic accuracy, improved disease characterization, and better treatment planning and monitoring. It enables the immediate integration of anatomic, functional, and metabolic imaging information, allowing for personalized characterization and monitoring of neurologic diseases. This review presents recent advances in PET/MR imaging and highlights advantages in clinical practice for neuro-oncology, epilepsy, and neurodegenerative disorders. PET/MR imaging provides valuable information about brain tumor metabolism, perfusion, and anatomic features, aiding in accurate delineation, treatment response assessment, and prognostication.
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Affiliation(s)
- Aurélie Kas
- Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, APHP Sorbonne Université, Paris, France; Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale, LIB, Paris F-75006, France.
| | - Laura Rozenblum
- Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, APHP Sorbonne Université, Paris, France; Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale, LIB, Paris F-75006, France
| | - Nadya Pyatigorskaya
- Neuroradiology Department, Pitié-Salpêtrière Hospital, APHP Sorbonne Université, Paris, France; Sorbonne Université, UMR S 1127, CNRS UMR 722, Institut du Cerveau, Paris, France
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Shiyam Sundar LK, Lassen ML, Gutschmayer S, Ferrara D, Calabrò A, Yu J, Kluge K, Wang Y, Nardo L, Hasbak P, Kjaer A, Abdelhafez YG, Wang G, Cherry SR, Spencer BA, Badawi RD, Beyer T, Muzik O. Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies. J Nucl Med 2023; 64:1145-1153. [PMID: 37290795 DOI: 10.2967/jnumed.122.265362] [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: 12/22/2022] [Revised: 03/09/2023] [Indexed: 06/10/2023] Open
Abstract
We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (18F-FDG, 18F-fluciclovine, 68Ga-PSMA, 68Ga-DOTATATE, 11C-Pittsburgh compound B, and 82Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (<2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios.
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Affiliation(s)
- Lalith Kumar Shiyam Sundar
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Martin Lyngby Lassen
- Department of Clinical Physiology, Nuclear Medicine, and PET and Cluster for Molecular Imaging Section 4011, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian Gutschmayer
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Daria Ferrara
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Anna Calabrò
- Department of Radiology, University of California-Davis, Davis, California
| | - Josef Yu
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Kilian Kluge
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Yiran Wang
- Department of Radiology, University of California-Davis, Davis, California
| | - Lorenzo Nardo
- Department of Radiology, University of California-Davis, Davis, California
| | - Philip Hasbak
- Department of Clinical Physiology, Nuclear Medicine, and PET and Cluster for Molecular Imaging Section 4011, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Kjaer
- Department of Clinical Physiology, Nuclear Medicine, and PET and Cluster for Molecular Imaging Section 4011, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | | | - Guobao Wang
- Department of Radiology, University of California-Davis, Davis, California
| | - Simon R Cherry
- Department of Radiology, University of California-Davis, Davis, California
- Department of Biomedical Engineering, University of California-Davis, Davis, California; and
| | - Benjamin A Spencer
- Department of Radiology, University of California-Davis, Davis, California
| | - Ramsey D Badawi
- Department of Radiology, University of California-Davis, Davis, California
- Department of Biomedical Engineering, University of California-Davis, Davis, California; and
| | - Thomas Beyer
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Otto Muzik
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan
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Sundar LKS, Yu J, Muzik O, Kulterer OC, Fueger B, Kifjak D, Nakuz T, Shin HM, Sima AK, Kitzmantl D, Badawi RD, Nardo L, Cherry SR, Spencer BA, Hacker M, Beyer T. Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence. J Nucl Med 2022; 63:1941-1948. [PMID: 35772962 PMCID: PMC9730926 DOI: 10.2967/jnumed.122.264063] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/16/2022] [Indexed: 01/26/2023] Open
Abstract
We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visceral fat, and skeletal muscle, which were semiautomatically segmented using thresholding. A majority-voting algorithm was used to generate a reference-standard segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing. For cerebral structures, 34 18F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only 18F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance as a secondary metric, to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80-0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body 18F-FDG PET/CT images with high accuracy.
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Affiliation(s)
- Lalith Kumar Shiyam Sundar
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Josef Yu
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria;,Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Otto Muzik
- Department of Pediatrics, Wayne State University School of Medicine, Children’s Hospital of Michigan, Detroit, Michigan
| | - Oana C. Kulterer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Barbara Fueger
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Daria Kifjak
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria;,Department of Radiology, University of Massachusetts Chan Medical School/UMass Memorial Health Care, Worcester, Massachusetts
| | - Thomas Nakuz
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Hyung Min Shin
- Division of General Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria; and
| | - Annika Katharina Sima
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Daniela Kitzmantl
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ramsey D. Badawi
- Department of Biomedical Engineering and Radiology, University of California–Davis, Davis, California
| | - Lorenzo Nardo
- Department of Biomedical Engineering and Radiology, University of California–Davis, Davis, California
| | - Simon R. Cherry
- Department of Biomedical Engineering and Radiology, University of California–Davis, Davis, California
| | - Benjamin A. Spencer
- Department of Biomedical Engineering and Radiology, University of California–Davis, Davis, California
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Beyer
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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Wei S, Joshi N, Salerno M, Ouellette D, Saleh L, Delorenzo C, Woody C, Schlyer D, Purschke ML, Pratte JF, Junnarkar S, Budassi M, Cao T, Fried J, Karp JS, Vaska P. PET Imaging of Leg Arteries for Determining the Input Function in PET/MRI Brain Studies Using a Compact, MRI-Compatible PET System. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022; 6:583-591. [PMID: 36212108 PMCID: PMC9541963 DOI: 10.1109/trpms.2021.3111841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this study, we used a compact, high-resolution, and MRI-compatible PET camera (VersaPET) to assess the feasibility of measuring the image-derived input function (IDIF) from arteries in the leg with the ultimate goal of enabling fully quantitative PET brain imaging without blood sampling. We used this approach in five 18F-FDG PET/MRI brain studies in which the input function was also acquired using the gold standard of serial arterial blood sampling. After accounting for partial volume, dispersion, and calibration effects, we compared the metabolic rates of glucose (MRglu) quantified from VersaPET IDIFs in 80 brain regions to those using the gold standard and achieved a bias and variability of <5% which is within the range of reported test-retest values for this type of study. We also achieved a strong linear relationship (R2 >0.97) against the gold standard across regions. The results of this preliminary study are promising and support further studies to optimize methods, validate in a larger cohort, and extend to the modeling of other radiotracers.
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Affiliation(s)
- Shouyi Wei
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Nandita Joshi
- Department of Electrical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Michael Salerno
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - David Ouellette
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Lemise Saleh
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Christine Delorenzo
- Department of Psychiatry and Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Craig Woody
- Department of Physics, Brookhaven National Laboratory, Upton, NY, USA
| | - David Schlyer
- Department of Physics, Brookhaven National Laboratory, Upton, NY, USA
| | | | - Jean-Francois Pratte
- Interdisciplinary Institute for Technological Innovation, Université de Sherbrooke, Sherbrooke, Canada
| | | | - Michael Budassi
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | | | - Jack Fried
- Department of Physics, Brookhaven National Laboratory, Upton, NY, USA
| | - Joel S. Karp
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Vaska
- Department of Biomedical Engineering and Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
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Henriksen AC, Lonsdale MN, Fuglø D, Kondziella D, Nersesjan V, Marner L. Non-invasive quantification of cerebral glucose metabolism using Gjedde-Patlak plot and image-derived input function from the aorta. Neuroimage 2022; 253:119079. [PMID: 35276368 DOI: 10.1016/j.neuroimage.2022.119079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/16/2022] [Accepted: 03/07/2022] [Indexed: 11/29/2022] Open
Abstract
INTRODUCTION We aimed at evaluating a Gjedde-Patlak plot and non-invasive image-derived input functions (IDIF) from the aorta to quantify cerebral glucose metabolic rate (CMRglc) in comparison to the reference standard based on sampling the arterial input function (AIF). METHOD Six healthy subjects received 200 MBq [18F]FDG simultaneously with the initiation of a three-part dynamic PET recording consisting of a 15 min-recording of the aorta, a 40 min-recording of the brain and finally 2 min-recording of the aorta. Simultaneously, the arterial 18F concentration was measured via arterial cannulation. Regions of interest were drawn in the aorta and the brain and time-activity curves extracted. The IDIF was obtained by fitting a triple exponential function to the aorta time-activity curve after the initial peak including the late aorta frame, thereby interpolating the arterial blood activity concentration during the brain scan. CMRglc was calculated from Gjedde-Patlak plots using AIF and IDIF, respectively and the predictive value was examined. Results from frontal cortex, insula, hippocampus and cerebellum were compared by paired t-test and agreement between the methods was analyzed by Bland-Altman plot statistics. RESULTS There was a strong linear relationship and an excellent agreement between the methods (mean±SD of CMRglcIDIF (μmol 100 g-1 min-1), mean difference, mean relative difference, 95% limits of agreement): frontal cortex: 30.8 ± 3.3, 0.5, 2.2%, [-1,6:2.5], insula: 25.4 ± 2.2, 0.4, 2.4%, [-1.4:2.2], hippocampus: 16.9 ± 1.2, 0.4, 3.8%, [-1.1:2.0] and cerebellum: 23.4 ± 1.9, 0.5, 3.1%, [-1.4:2.5]). CONCLUSION We found excellent agreement between CMRglc obtained with an IDIF from the aorta and the reference standard with AIF. A non-invasive three-part dynamic [18F]FDG PET recording is feasible as a non-invasive alternative for reliable quantification of cerebral glucose metabolism in all scanner systems. This is useful in patients with presumed global cerebral changes owing to systemic disease or for the monitoring of treatment effects.
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Affiliation(s)
| | - Markus Nowak Lonsdale
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital, Bispebjerg, Denmark
| | - Dan Fuglø
- Department of Nuclear Medicine, Copenhagen University Hospital, Herlev, Copenhagen, Denmark
| | - Daniel Kondziella
- Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Vardan Nersesjan
- Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Lisbeth Marner
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital, Bispebjerg, Denmark.
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Daines SA. The Therapeutic Potential and Limitations of Ketones in Traumatic Brain Injury. Front Neurol 2021; 12:723148. [PMID: 34777197 PMCID: PMC8579274 DOI: 10.3389/fneur.2021.723148] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/13/2021] [Indexed: 12/21/2022] Open
Abstract
Traumatic brain injury (TBI) represents a significant health crisis. To date, no FDA approved pharmacotherapies are available to prevent the neurological deficits caused by TBI. As an alternative to pharmacotherapy treatment of TBI, ketones could be used as a metabolically based therapeutic strategy. Ketones can help combat post-traumatic cerebral energy deficits while also reducing inflammation, oxidative stress, and neurodegeneration. Experimental models of TBI suggest that administering ketones to TBI patients may provide significant benefits to improve recovery. However, studies evaluating the effectiveness of ketones in human TBI are limited. Unanswered questions remain about age- and sex-dependent factors, the optimal timing and duration of ketone supplementation, and the optimal levels of circulating and cerebral ketones. Further research and improvements in metabolic monitoring technology are also needed to determine if ketone supplementation can improve TBI recovery outcomes in humans.
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Affiliation(s)
- Savannah Anne Daines
- Department of Biology, Utah State University, Logan, UT, United States
- Department of Kinesiology and Health Science, Utah State University, Logan, UT, United States
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Chen MK, Mecca AP, Naganawa M, Gallezot JD, Toyonaga T, Mondal J, Finnema SJ, Lin SF, O’Dell RS, McDonald JW, Michalak HR, Vander Wyk B, Nabulsi NB, Huang Y, Arnsten AFT, van Dyck CH, Carson RE. Comparison of [ 11C]UCB-J and [ 18F]FDG PET in Alzheimer's disease: A tracer kinetic modeling study. J Cereb Blood Flow Metab 2021; 41:2395-2409. [PMID: 33757318 PMCID: PMC8393289 DOI: 10.1177/0271678x211004312] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/29/2021] [Accepted: 02/21/2021] [Indexed: 11/16/2022]
Abstract
[11C]UCB-J PET for synaptic vesicle glycoprotein 2 A (SV2A) has been proposed as a suitable marker for synaptic density in Alzheimer's disease (AD). We compared [11C]UCB-J binding for synaptic density and [18F]FDG uptake for metabolism (correlated with neuronal activity) in 14 AD and 11 cognitively normal (CN) participants. We assessed both absolute and relative outcome measures in brain regions of interest, i.e., K1 or R1 for [11C]UCB-J perfusion, VT (volume of distribution) or DVR to cerebellum for [11C]UCB-J binding to SV2A; and Ki or KiR to cerebellum for [18F]FDG metabolism. [11C]UCB-J binding and [18F]FDG metabolism showed a similar magnitude of reduction in the medial temporal lobe of AD -compared to CN participants. However, the magnitude of reduction of [11C]UCB-J binding in neocortical regions was less than that observed with [18F]FDG metabolism. Inter-tracer correlations were also higher in the medial temporal regions between synaptic density and metabolism, with lower correlations in neocortical regions. [11C]UCB-J perfusion showed a similar pattern to [18F]FDG metabolism, with high inter-tracer regional correlations. In summary, we conducted the first in vivo PET imaging of synaptic density and metabolism in the same AD participants and reported a concordant reduction in medial temporal regions but a discordant reduction in neocortical regions.
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Affiliation(s)
- Ming-Kai Chen
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Adam P Mecca
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Mika Naganawa
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Jean-Dominique Gallezot
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Jayanta Mondal
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Sjoerd J Finnema
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Shu-fei Lin
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Ryan S O’Dell
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Julia W McDonald
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Hannah R Michalak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Brent Vander Wyk
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Nabeel B Nabulsi
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Yiyun Huang
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Amy FT Arnsten
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | | | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
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9
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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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Smart K, Liu H, Matuskey D, Chen MK, Torres K, Nabulsi N, Labaree D, Ropchan J, Hillmer AT, Huang Y, Carson RE. Binding of the synaptic vesicle radiotracer [ 11C]UCB-J is unchanged during functional brain activation using a visual stimulation task. J Cereb Blood Flow Metab 2021; 41:1067-1079. [PMID: 32757741 PMCID: PMC8054713 DOI: 10.1177/0271678x20946198] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 06/25/2020] [Accepted: 06/30/2020] [Indexed: 12/14/2022]
Abstract
The positron emission tomography radioligand [11C]UCB-J binds to synaptic vesicle glycoprotein 2 A (SV2A), a regulator of vesicle release. Increased neuronal firing could potentially affect tracer concentrations if binding site availability is altered during vesicle exocytosis. This study assessed whether physiological brain activation induces changes in [11C]UCB-J tissue influx (K1), volume of distribution (VT), or binding potential (BPND). Healthy volunteers (n = 7) underwent 60-min [11C]UCB-J PET scans at baseline and during intermittent presentation of 8-Hz checkerboard visual stimulation. Sensitivity to intermittent changes in kinetic parameters was assessed in simulations, and visual stimulation was repeated using functional magnetic resonance imaging to characterize neural responses. VT and K1 were determined using the one-tissue compartment model and BPND using the simplified reference tissue model. In primary visual cortex, K1 increased 34.3 ± 15.5% (p = 0.001) during stimulation, with no change in other regions (ps > 0.12). K1 change was correlated with fMRI BOLD response (r = 0.77, p = 0.043). There was no change in VT (-3.9 ± 8.8%, p = 0.33) or BPND (-0.2 ± 9.6%, p = 0.94) in visual cortex nor other regions (ps > 0.19). Therefore, despite robust increases in regional tracer influx due to blood flow increases, binding measures were unchanged during stimulation. [11C]UCB-J VT and BPND are likely to be stable in vivo measures of synaptic density.
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Affiliation(s)
- Kelly Smart
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Heather Liu
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, CT, USA
| | - David Matuskey
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Ming-Kai Chen
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Kristen Torres
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Nabeel Nabulsi
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - David Labaree
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jim Ropchan
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Ansel T Hillmer
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Yiyun Huang
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Richard E Carson
- Yale PET Center, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, CT, USA
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Shiyam Sundar LK, Iommi D, Muzik O, Chalampalakis Z, Klebermass EM, Hienert M, Rischka L, Lanzenberger R, Hahn A, Pataraia E, Traub-Weidinger T, Hummel J, Beyer T. Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic 18F-FDG PET Brain Studies. J Nucl Med 2020; 62:871-879. [PMID: 33246982 PMCID: PMC8729870 DOI: 10.2967/jnumed.120.248856] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 10/07/2020] [Indexed: 11/16/2022] Open
Abstract
This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic 18F-FDG brain studies. Methods: Ten healthy volunteers (5 men/5 women; mean age ± SD, 27 ± 7 y; weight, 70 ± 10 kg) underwent a test–retest 18F-FDG PET/MRI examination of the brain (n = 20). The imaging protocol consisted of a 60-min PET list-mode acquisition contemporaneously acquired with MRI, including MR navigators and a 3-dimensional time-of-flight MR angiography sequence. Arterial blood samples were collected as a reference standard representing the arterial input function (AIF). Training of the cGAN was performed using 70% of the total datasets (n = 16, randomly chosen), which was corrected for motion using MR navigators. The resulting cGAN mappings (between individual frames and the reference frame [55–60 min after injection]) were then applied to the test dataset (remaining 30%, n = 6), producing artificially generated low-noise images from early high-noise PET frames. These low-noise images were then coregistered to the reference frame, yielding 3-dimensional motion vectors. Performance of cGAN-aided motion correction was assessed by comparing the image-derived input function (IDIF) extracted from a cGAN-aided motion-corrected dynamic sequence with the AIF based on the areas under the curves (AUCs). Moreover, clinical relevance was assessed through direct comparison of the average cerebral metabolic rates of glucose (CMRGlc) values in gray matter calculated using the AIF and the IDIF. Results: The absolute percentage difference between AUCs derived using the motion-corrected IDIF and the AIF was (1.2% + 0.9%). The gray matter CMRGlc values determined using these 2 input functions differed by less than 5% (2.4% + 1.7%). Conclusion: A fully automated data-driven motion-compensation approach was established and tested for 18F-FDG PET brain imaging. cGAN-aided motion correction enables the translation of noninvasive clinical absolute quantification from PET/MR to PET/CT by allowing the accurate determination of motion vectors from the PET data itself.
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Affiliation(s)
- Lalith Kumar Shiyam Sundar
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - David Iommi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Otto Muzik
- Department of Pediatrics, Children's Hospital of Michigan, The Detroit Medical Center, Wayne State University School of Medicine, Detroit, Michigan
| | - Zacharias Chalampalakis
- Service Hospitalier Frédéric Joliot, CEA, Inserm, CNRS, Univ. Paris Sud, Université Paris Saclay, Orsay, France
| | - Eva-Maria Klebermass
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Marius Hienert
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; and
| | - Lucas Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; and
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; and
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; and
| | | | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Johann Hummel
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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Abstract
PURPOSE OF REVIEW Hybrid PET- MRI is a technique that has the ability to improve diagnostic accuracy in many applications, whereas PET and MRI performed separately often fail to provide accurate responses to clinical questions. Here, we review recent studies and current developments in PET-MRI, focusing on clinical applications. RECENT FINDINGS The combination of PET and MRI imaging methods aims at increasing the potential of each individual modality. Combined methods of image reconstruction and correction of PET-MRI attenuation are being developed, and a number of applications are being introduced into clinical practice. To date, the value of PET-MRI has been demonstrated for the evaluation of brain tumours in epilepsy and neurodegenerative diseases. Continued advances in data analysis regularly improve the efficiency and the potential application of multimodal biomarkers. SUMMARY PET-MRI provides simultaneous of anatomical, functional, biochemical and metabolic information for the personalized characterization and monitoring of neurological diseases. In this review, we show the advantage of the complementarity of different biomarkers obtained using PET-MRI data. We also present the recent advances made in this hybrid imaging modality and its advantages in clinical practice compared with MRI and PET separately.
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