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Sarikaya I. Letter to the Editor. Clin Nucl Med 2024:00003072-990000000-01214. [PMID: 39010323 DOI: 10.1097/rlu.0000000000005382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Affiliation(s)
- Ismet Sarikaya
- Department of Nuclear Medicine, Kirklareli University Faculty of Medicine, Kirklareli, Turkey
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Issa ASM, Scheins J, Tellmann L, Brambilla CR, Lohmann P, Rota-Kops E, Herzog H, Neuner I, Shah NJ, Lerche C. Impact of improved dead time correction on the quantification accuracy of a dedicated BrainPET scanner. PLoS One 2024; 19:e0296357. [PMID: 38578749 PMCID: PMC10997125 DOI: 10.1371/journal.pone.0296357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/11/2023] [Indexed: 04/07/2024] Open
Abstract
OBJECTIVE Quantitative values derived from PET brain images are of high interest for neuroscientific applications. Insufficient DT correction (DTC) can lead to a systematic bias of the output parameters obtained by a detailed analysis of the time activity curves (TACs). The DTC method currently used for the Siemens 3T MR BrainPET insert is global, i.e., differences in DT losses between detector blocks are not considered, leading to inaccurate DTC and, consequently, to inaccurate measurements masked by a bias. However, following careful evaluation with phantom measurements, a new block-pairwise DTC method has demonstrated a higher degree of accuracy compared to the global DTC method. APPROACH Differences between the global and the block-pairwise DTC method were studied in this work by applying several radioactive tracers. We evaluated the impact on [11C]ABP688, O-(2-[18F]fluoroethyl)-L-tyrosine (FET), and [15O]H2O TACs. RESULTS For [11C]ABP688, a relevant bias of between -0.0034 and -0.0053 ml/ (cm3 • min) was found in all studied brain regions for the volume of distribution (VT) when using the current global DTC method. For [18F]FET-PET, differences of up to 10% were observed in the tumor-to-brain ratio (TBRmax), these differences depend on the radial distance of the maximum from the PET isocenter. For [15O]H2O, differences between +4% and -7% were observed in the GM region. Average biases of -4.58%, -3.2%, and -1.2% for the regional cerebral blood flow (CBF (K1)), the rate constant k2, and the volume of distribution VT were observed, respectively. Conversely, in the white matter region, average biases of -4.9%, -7.0%, and 3.8% were observed for CBF (K1), k2, and VT, respectively. CONCLUSION The bias introduced by the global DTC method leads to an overestimation in the studied quantitative parameters for all applications compared to the block-pairwise method. SIGNIFICANCE The observed differences between the two DTC methods are particularly relevant for research applications in neuroscientific studies as they affect the accuracy of quantitative Brain PET images.
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Affiliation(s)
- Ahlam Said Mohamad Issa
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
- JARA, BRAIN, Translational Medicine, Aachen, Germany
- Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Jürgen Scheins
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
| | - Lutz Tellmann
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
| | | | - Philipp Lohmann
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
| | - Elena Rota-Kops
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
| | - Hans Herzog
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
- JARA, BRAIN, Translational Medicine, Aachen, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
- JARA, BRAIN, Translational Medicine, Aachen, Germany
- Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Jülich, Germany
| | - Christoph Lerche
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
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Rossano SM, Johnson AS, Smith A, Ziaggi G, Roetman A, Guzman D, Okafor A, Klein J, Tomljanovic Z, Stern Y, Brickman AM, Lee S, Kreisl WC, Lao P. Microglia measured by TSPO PET are associated with Alzheimer's disease pathology and mediate key steps in a disease progression model. Alzheimers Dement 2024; 20:2397-2407. [PMID: 38298155 PMCID: PMC11032543 DOI: 10.1002/alz.13699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/30/2023] [Accepted: 12/18/2023] [Indexed: 02/02/2024]
Abstract
INTRODUCTION Evidence suggests microglial activation precedes regional tau and neurodegeneration in Alzheimer's disease (AD). We characterized microglia with translocator protein (TSPO) positron emission tomography (PET) within an AD progression model where global amyloid beta (Aβ) precedes local tau and neurodegeneration, resulting in cognitive impairment. METHODS Florbetaben, PBR28, and MK-6240 PET, T1 magnetic resonance imaging, and cognitive measures were performed in 19 cognitively unimpaired older adults and 22 patients with mild cognitive impairment or mild AD to examine associations among microglia activation, Aβ, tau, and cognition, adjusting for neurodegeneration. Mediation analyses evaluated the possible role of microglial activation along the AD progression model. RESULTS Higher PBR28 uptake was associated with higher Aβ, higher tau, and lower MMSE score, independent of neurodegeneration. PBR28 mediated associations between tau in early and middle Braak stages, between tau and neurodegeneration, and between neurodegeneration and cognition. DISCUSSION Microglia are associated with AD pathology and cognition and may mediate relationships between subsequent steps in AD progression.
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Affiliation(s)
- Samantha M. Rossano
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Aubrey S. Johnson
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Anna Smith
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Galen Ziaggi
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Andrew Roetman
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Diana Guzman
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Amarachukwu Okafor
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Julia Klein
- Department of Anesthesiology and Perioperative MedicineUniversity of California Los Angeles HealthLos AngelesCaliforniaUSA
| | - Zeljko Tomljanovic
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Yaakov Stern
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Adam M. Brickman
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Seonjoo Lee
- Department of Psychiatry and BiostatisticsColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - William C. Kreisl
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Patrick Lao
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
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Cerina V, Crivellaro C, Morzenti S, Pozzi FE, Bigiogera V, Jonghi-Lavarini L, Moresco RM, Basso G, De Bernardi E. A ROI-based quantitative pipeline for 18F-FDG PET metabolism and pCASL perfusion joint analysis: Validation of the 18F-FDG PET line. Heliyon 2024; 10:e23340. [PMID: 38163125 PMCID: PMC10755331 DOI: 10.1016/j.heliyon.2023.e23340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024] Open
Abstract
In Mild Cognitive Impairment (MCI), the study of brain metabolism, provided by 18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) can be integrated with brain perfusion through pseudo-Continuous Arterial Spin Labeling Magnetic Resonance sequences (MR pCASL). Cortical hypometabolism identification generally relies on wide control group datasets; pCASL control groups are instead not publicly available yet, due to lack of standardization in the acquisition parameters. This study presents a quantitative pipeline to be applied to PET and pCASL data to coherently analyze metabolism and perfusion inside 16 matching cortical regions of interest (ROIs) derived from the AAL3 atlas. The PET line is tuned on 36 MCI patients and 107 healthy control subjects, to agree in identifying hypometabolic regions with clinical reference methods (visual analysis supported by a vendor tool and Statistical Parametric Mapping, SPM, with two parametrizations here identified as SPM-A and SPM-B). The analysis was conducted for each ROI separately. The proposed PET analysis pipeline obtained accuracy 78 % and Cohen's к 60 % vs visual analysis, accuracy 79 % and Cohen's к 58 % vs SPM-A, accuracy 77 % and Cohen's к 54 % vs SPM-B. Cohen's к resulted not significantly different from SPM-A and SPM-B Cohen's к when assuming visual analysis as reference method (p-value 0.61 and 0.31 respectively). Considering SPM-A as reference method, Cohen's к is not significantly different from SPM-B Cohen's к as well (p-value = 1.00). The complete PET-pCASL pipeline was then preliminarily applied on 5 MCI patients and metabolism-perfusion regional correlations were assessed. The proposed approach can be considered as a promising tool for PET-pCASL joint analyses in MCI, even in the absence of a pCASL control group, to perform metabolism-perfusion regional correlation studies, and to assess and compare perfusion in hypometabolic or normo-metabolic areas.
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Affiliation(s)
- Valeria Cerina
- PhD program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Cinzia Crivellaro
- Nuclear Medicine, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
| | - Sabrina Morzenti
- Medical Physics, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
| | - Federico E. Pozzi
- PhD program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Italy
- Neurology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
- Milan center for Neuroscience (NeuroMI), University of Milano-Bicocca, Italy
| | | | | | - Rosa M. Moresco
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Gianpaolo Basso
- Milan center for Neuroscience (NeuroMI), University of Milano-Bicocca, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
- Neuroradiology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
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Castrillon G, Epp S, Bose A, Fraticelli L, Hechler A, Belenya R, Ranft A, Yakushev I, Utz L, Sundar L, Rauschecker JP, Preibisch C, Kurcyus K, Riedl V. An energy costly architecture of neuromodulators for human brain evolution and cognition. SCIENCE ADVANCES 2023; 9:eadi7632. [PMID: 38091393 PMCID: PMC10848727 DOI: 10.1126/sciadv.adi7632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 11/10/2023] [Indexed: 12/18/2023]
Abstract
In comparison to other species, the human brain exhibits one of the highest energy demands relative to body metabolism. It remains unclear whether this heightened energy demand uniformly supports an enlarged brain or if specific signaling mechanisms necessitate greater energy. We hypothesized that the regional distribution of energy demands will reveal signaling strategies that have contributed to human cognitive development. We measured the energy distribution within the brain functional connectome using multimodal brain imaging and found that signaling pathways in evolutionarily expanded regions have up to 67% higher energetic costs than those in sensory-motor regions. Additionally, histology, transcriptomic data, and molecular imaging independently reveal an up-regulation of signaling at G-protein-coupled receptors in energy-demanding regions. Our findings indicate that neuromodulator activity is predominantly involved in cognitive functions, such as reading or memory processing. This study suggests that an up-regulation of neuromodulator activity, alongside increased brain size, is a crucial aspect of human brain evolution.
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Affiliation(s)
- Gabriel Castrillon
- Department of Neuroradiology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Research Group in Medical Imaging, SURA Ayudas Diagnósticas, Medellin, Colombia
- Department of Neuroradiology at Uniklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Samira Epp
- Department of Neuroradiology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Antonia Bose
- Department of Neuroradiology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Laura Fraticelli
- Department of Neuroradiology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Munich, Germany
| | - André Hechler
- Department of Neuroradiology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Roman Belenya
- Department of Neuroradiology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Andreas Ranft
- Department of Anesthesiology and Intensive Care Medicine at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Lukas Utz
- Department of Neuroradiology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Lalith Sundar
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Josef P Rauschecker
- Center for Neuroengineering, Georgetown University, Washington, DC, USA
- Institute for Advanced Study, Technical University of Munich, Munich, Germany
| | - Christine Preibisch
- Department of Neuroradiology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Department of Neurology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Katarzyna Kurcyus
- Department of Neuroradiology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Valentin Riedl
- Department of Neuroradiology at Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Department of Neuroradiology at Uniklinikum Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
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Delva A, Van Laere K, Vandenberghe W. Longitudinal Imaging of Regional Brain Volumes, SV2A, and Glucose Metabolism In Huntington's Disease. Mov Disord 2023; 38:1515-1526. [PMID: 37382295 DOI: 10.1002/mds.29501] [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: 03/03/2023] [Revised: 05/03/2023] [Accepted: 05/18/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND Development of disease-modifying treatments for Huntington's disease (HD) could be aided by the use of imaging biomarkers of disease progression. Positron emission tomography (PET) with 11 C-UCB-J, a radioligand for the brain-wide presynaptic marker synaptic vesicle protein 2A (SV2A), detects more widespread brain changes in early HD than volumetric magnetic resonance imaging (MRI) and 18 F-fludeoxyglucose (18 F-FDG) PET, but longitudinal 11 C-UCB-J PET data have not been reported. The aim of this study was to compare the sensitivity of 11 C-UCB-J PET, 18 F-FDG PET, and volumetric MRI for detection of longitudinal changes in early HD. METHODS Seventeen HD mutation carriers (six premanifest and 11 early manifest) and 13 healthy controls underwent 11 C-UCB-J PET, 18 F-FDG PET, and volumetric MRI at baseline (BL) and after 21.4 ± 2.7 months (Y2). Within-group and between-group longitudinal clinical and imaging changes were assessed. RESULTS The HD group showed significant 2-year worsening of Unified Huntington's Disease Rating Scale motor scores. There was significant longitudinal volume loss within the HD group in caudate (-4.5% ± 3.8%), putamen (-3.6% ± 3.5%), pallidum (-3.0% ± 2.7%), and frontal cortex (-2.0% ± 2.1%) (all P < 0.001). Within the HD group there was longitudinal loss of putaminal SV2A binding (6.4% ± 8.8%, P = 0.01) and putaminal glucose metabolism (-2.8% ± 4.4%, P = 0.008), but these changes were not significant after correction for multiple comparisons. Premanifest subjects at BL only had significantly lower SV2A binding than controls in basal ganglia structures, but at Y2 additionally had significant SV2A loss in frontal and parietal cortex, indicating spread of SV2A loss from subcortical to cortical regions. CONCLUSIONS Volumetric MRI may be more sensitive than 11 C-UCB-J PET and 18 F-FDG PET for detection of 2-year brain changes in early HD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Aline Delva
- Department of Neurosciences, KU Leuven, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Koen Van Laere
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Wim Vandenberghe
- Department of Neurosciences, KU Leuven, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
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Sanaat A, Shooli H, Böhringer AS, Sadeghi M, Shiri I, Salimi Y, Ginovart N, Garibotto V, Arabi H, Zaidi H. A cycle-consistent adversarial network for brain PET partial volume correction without prior anatomical information. Eur J Nucl Med Mol Imaging 2023; 50:1881-1896. [PMID: 36808000 PMCID: PMC10199868 DOI: 10.1007/s00259-023-06152-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 02/12/2023] [Indexed: 02/23/2023]
Abstract
PURPOSE Partial volume effect (PVE) is a consequence of the limited spatial resolution of PET scanners. PVE can cause the intensity values of a particular voxel to be underestimated or overestimated due to the effect of surrounding tracer uptake. We propose a novel partial volume correction (PVC) technique to overcome the adverse effects of PVE on PET images. METHODS Two hundred and twelve clinical brain PET scans, including 50 18F-Fluorodeoxyglucose (18F-FDG), 50 18F-Flortaucipir, 36 18F-Flutemetamol, and 76 18F-FluoroDOPA, and their corresponding T1-weighted MR images were enrolled in this study. The Iterative Yang technique was used for PVC as a reference or surrogate of the ground truth for evaluation. A cycle-consistent adversarial network (CycleGAN) was trained to directly map non-PVC PET images to PVC PET images. Quantitative analysis using various metrics, including structural similarity index (SSIM), root mean squared error (RMSE), and peak signal-to-noise ratio (PSNR), was performed. Furthermore, voxel-wise and region-wise-based correlations of activity concentration between the predicted and reference images were evaluated through joint histogram and Bland and Altman analysis. In addition, radiomic analysis was performed by calculating 20 radiomic features within 83 brain regions. Finally, a voxel-wise two-sample t-test was used to compare the predicted PVC PET images with reference PVC images for each radiotracer. RESULTS The Bland and Altman analysis showed the largest and smallest variance for 18F-FDG (95% CI: - 0.29, + 0.33 SUV, mean = 0.02 SUV) and 18F-Flutemetamol (95% CI: - 0.26, + 0.24 SUV, mean = - 0.01 SUV), respectively. The PSNR was lowest (29.64 ± 1.13 dB) for 18F-FDG and highest (36.01 ± 3.26 dB) for 18F-Flutemetamol. The smallest and largest SSIM were achieved for 18F-FDG (0.93 ± 0.01) and 18F-Flutemetamol (0.97 ± 0.01), respectively. The average relative error for the kurtosis radiomic feature was 3.32%, 9.39%, 4.17%, and 4.55%, while it was 4.74%, 8.80%, 7.27%, and 6.81% for NGLDM_contrast feature for 18F-Flutemetamol, 18F-FluoroDOPA, 18F-FDG, and 18F-Flortaucipir, respectively. CONCLUSION An end-to-end CycleGAN PVC method was developed and evaluated. Our model generates PVC images from the original non-PVC PET images without requiring additional anatomical information, such as MRI or CT. Our model eliminates the need for accurate registration or segmentation or PET scanner system response characterization. In addition, no assumptions regarding anatomical structure size, homogeneity, boundary, or background level are required.
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Affiliation(s)
- Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Hossein Shooli
- Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy (MIRT), Bushehr Medical University Hospital, Faculty of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Andrew Stephen Böhringer
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Maryam Sadeghi
- Department of Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, Schoepfstr. 41, Innsbruck, Austria
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Nathalie Ginovart
- Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- Department of Psychiatry, Geneva University, Geneva, Switzerland
- Department of Basic Neuroscience, Geneva University, Geneva, Switzerland
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
- Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Geneva University Neurocenter, University of Geneva, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Mannheim JG, Rausch I, Conti M, la Fougère C, Schmidt FP. Characterization of the partial volume effect along the axial field-of-view of the Biograph Vision Quadra total-body PET/CT system for multiple isotopes. EJNMMI Phys 2023; 10:33. [PMID: 37243869 DOI: 10.1186/s40658-023-00554-7] [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: 03/03/2023] [Accepted: 05/15/2023] [Indexed: 05/29/2023] Open
Abstract
BACKGROUND Total-body PET scanners with axial field of views (FOVs) longer than 1 m enable new applications to study multiple organs (e.g., the brain-gut-axis) simultaneously. As the spatial resolution and the associated partial volume effect (PVE) can vary significantly along the FOV, detailed knowledge of the contrast recovery coefficients (CRCs) is a prerequisite for image analysis and interpretation of quantitative results. The aim of this study was to determine the CRCs, as well as voxel noise, for multiple isotopes throughout the 1.06 m axial FOV of the Biograph Vision Quadra PET/CT system (Siemens Healthineers). MATERIALS AND METHODS Cylindrical phantoms equipped with three different sphere sizes (inner diameters 7.86 mm, 28 and 37 mm) were utilized for the PVE evaluation. The 7.86 mm sphere was filled with F-18 (8:1 and 4:1), Ga-68 (8:1) and Zr-89 (8:1). The 28 mm and 37 mm spheres were filled with F-18 (8:1). Background concentration in the respective phantoms was of ~ 3 kBq/ml. The phantoms were measured at multiple positions in the FOV (axial: 0, 10, 20, 30, 40 and 50 cm, transaxial: 0, 10, 20 cm). The data were reconstructed with the standard clinical protocol, including PSF correction and TOF information with up to 10 iterations for maximum ring differences (MRDs) of 85 and 322; CRCs, as well as voxel noise levels, were determined for each position. RESULTS F-18 CRCs (SBR 8:1 and 4:1) of the 7.86 mm sphere decreased up to 18% from the center FOV (cFOV) toward the transaxial edge and increased up to 17% toward the axial edge. Noise levels were below 15% for the default clinical reconstruction parameters. The larger spheres exhibited a similar pattern. Zr-89 revealed ~ 10% lower CRCs than F-18 but larger noise (9.1% (F-18), 19.1% (Zr-89); iteration 4, cFOV) for the default reconstruction. Zr-89 noise levels in the cFOV significantly decreased (~ 28%) when reconstructing the data with MRD322 compared with MRD85 along with a slight decrease in CRC values. Ga-68 exhibited the lowest CRCs for the three isotopes and noise characteristics comparable to those of F-18. CONCLUSIONS Distinct differences in the PVE within the FOV were detected for clinically relevant isotopes F-18, Ga-68 and Zr-89, as well as for different sphere sizes. Depending on the positions inside the FOV, the sphere-to-background ratios, count statistics and isotope used, this can result in an up to 50% difference between CRCs. Hence, these changes in PVE can significantly affect the quantitative analysis of patient data. MRD322 resulted in slightly lower CRC values, especially in the center FOV, whereas the voxel noise significantly decreased compared with MRD85.
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Affiliation(s)
- Julia G Mannheim
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard-Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany.
- Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, Tuebingen, Germany.
| | - Ivo Rausch
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Maurizio Conti
- Molecular Imaging, Siemens Medical Solutions USA, Inc., Knoxville, TN, USA
| | - Christian la Fougère
- Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, Tuebingen, Germany
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tuebingen, Germany
| | - Fabian P Schmidt
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard-Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tuebingen, Germany
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9
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Data-Driven Phenotyping of Alzheimer's Disease under Epigenetic Conditions Using Partial Volume Correction of PET Studies and Manifold Learning. Biomedicines 2023; 11:biomedicines11020273. [PMID: 36830810 PMCID: PMC9953610 DOI: 10.3390/biomedicines11020273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/10/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia. An increasing number of studies have confirmed epigenetic changes in AD. Consequently, a robust phenotyping mechanism must take into consideration the environmental effects on the patient in the generation of phenotypes. Positron Emission Tomography (PET) is employed for the quantification of pathological amyloid deposition in brain tissues. The objective is to develop a new methodology for the hyperparametric analysis of changes in cognitive scores and PET features to test for there being multiple AD phenotypes. We used a computational method to identify phenotypes in a retrospective cohort study (532 subjects), using PET and Magnetic Resonance Imaging (MRI) images and neuropsychological assessments, to develop a novel computational phenotyping method that uses Partial Volume Correction (PVC) and subsets of neuropsychological assessments in a non-biased fashion. Our pipeline is based on a Regional Spread Function (RSF) method for PVC and a t-distributed Stochastic Neighbor Embedding (t-SNE) manifold. The results presented demonstrate that (1) the approach to data-driven phenotyping is valid, (2) the different techniques involved in the pipelines produce different results, and (3) they permit us to identify the best phenotyping pipeline. The method identifies three phenotypes and permits us to analyze them under epigenetic conditions.
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10
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A new brain dedicated PET scanner with 4D detector information. BIO-ALGORITHMS AND MED-SYSTEMS 2022. [DOI: 10.2478/bioal-2022-0083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Abstract
In this article, we present the geometrical design and preliminary results of a high sensitivity organ-specific Positron Emission Tomography (PET) system dedicated to the study of the human brain. The system, called 4D-PET, will allow accurate imaging of brain studies due to its expected high sensitivity, high 3D spatial resolution and, by including precise photon time of flight (TOF) information, a boosted signal-to-noise ratio (SNR).
The 4D-PET system incorporates an innovative detector design based on crystal slabs (semi-monolithic) that enables accurate 3D photon impact positioning (including photon Depth of Interaction (DOI) measurement), while providing a precise determination of the photon arrival time to the detector. The detector includes a novel readout system that reduces the number of detector signals in a ratio of 4:1 thus, alleviating complexity and cost. The analog output signals are fed to the TOFPET2 ASIC (PETsys) for scalability purposes.
The present manuscript reports the evaluation of the 4D-PET detector, achieving best values 3D resolution values of <1.6 mm (pixelated axis), 2.7±0.5 mm (monolithic axis) and 3.4±1.1 (DOI axis) mm; 359 ± 7 ps coincidence time resolution (CTR); 10.2±1.5 % energy resolution; and sensitivity of 16.2% at the center of the scanner (simulated). Moreover, a comprehensive description of the 4D-PET architecture (that includes 320 detectors), some pictures of its mechanical assembly, and simulations on the expected image quality are provided.
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11
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Sur C, Adamczuk K, Scott D, Kost J, Sampat M, Buckley C, Farrar G, Newton B, Suhy J, Bennacef I, Egan MF. Evaluation of 18F-flutemetamol amyloid PET image analysis parameters on the effect of verubecestat on brain amlyoid load in Alzheimer's disease. Mol Imaging Biol 2022; 24:862-873. [PMID: 35794343 DOI: 10.1007/s11307-022-01735-z] [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: 12/13/2021] [Revised: 03/17/2022] [Accepted: 04/21/2022] [Indexed: 12/29/2022]
Abstract
PURPOSE The BACE inhibitor verubecestat was previously found to reduce amyloid load as assessed by 18F-flutemetamol positron emission tomography (PET) composite cortical standard uptake value ratio (SUVr) in patients with mild-to-moderate Alzheimer's disease (AD) in a substudy of the EPOCH trial. Here, we report on additional analyses relevant to the EPOCH PET data, to help inform on the use of PET for assessing amlyloid load in AD clinical trials. PROCEDURES The analyses addressed (1) identification of an optimal 18F-flutemetamol reference region, (2) determination of the threshold to characterize the magnitude of the longitudinal change, and (3) the impact of partial volume correction (PVC). Pons and subcortical white matter were evaluated as reference regions. The SUVr cutoffs and final reference region choice were determined using 162 18F-flutemetamol PET scans from the AIBL dataset. 18F-flutemetamol SUVrs were computed at baseline and at Week 78 in EPOCH participants who received verubecestat 12 mg (n = 14), 40 mg (n = 20), or placebo (n = 20). Drug effects on amyloid load were computed using either Meltzer (MZ), or symmetric geometric transfer matrix (SGTM) PVC and compared to uncorrected data. RESULTS The optimal subcortical white matter and pons SUVr cutoffs were determined to be 0.69 and 0.62, respectively. The effect size to detect longitudinal change was higher for subcortical white matter (1.20) than pons (0.45). Hence, subcortical white matter was used as the reference region for the EPOCH PET substudy. In EPOCH, uncorrected baseline SUVr values correlated strongly with MZ PVC (r2 = 0.94) and SGTM PVC (r2 = 0.92) baseline SUVr values, and PVC did not provide improvement for evaluating treatment effects on amyloid load at Week 78. No change from baseline was observed in the placebo group at Week 78, whereas a 0.02 and a 0.04 decrease in SUVr were observed in the 12 mg and 40 mg arms, with the latter representing a 22% reduction in the amyloid load above the detection threshold. CONCLUSIONS Treatment-related 18F-flutemetamol longitudinal changes in AD clinical trials can be quantified using a subcortical white matter reference region without PVC. CLINICAL TRIAL REGISTRATION clinicaltrials.gov NCT01739348.
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12
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. Lower brain glucose metabolism in normal ageing is predominantly frontal and temporal: A systematic review and pooled effect size and activation likelihood estimates meta-analyses. Hum Brain Mapp 2022; 44:1251-1277. [PMID: 36269148 PMCID: PMC9875940 DOI: 10.1002/hbm.26119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/29/2022] [Accepted: 10/05/2022] [Indexed: 01/31/2023] Open
Abstract
This review provides a qualitative and quantitative analysis of cerebral glucose metabolism in ageing. We undertook a systematic literature review followed by pooled effect size and activation likelihood estimates (ALE) meta-analyses. Studies were retrieved from PubMed following the PRISMA guidelines. After reviewing 635 records, 21 studies with 22 independent samples (n = 911 participants) were included in the pooled effect size analyses. Eight studies with eleven separate samples (n = 713 participants) were included in the ALE analyses. Pooled effect sizes showed significantly lower cerebral metabolic rates of glucose for older versus younger adults for the whole brain, as well as for the frontal, temporal, parietal, and occipital lobes. Among the sub-cortical structures, the caudate showed a lower metabolic rate among older adults. In sub-group analyses controlling for changes in brain volume or partial volume effects, the lower glucose metabolism among older adults in the frontal lobe remained significant, whereas confidence intervals crossed zero for the other lobes and structures. The ALE identified nine clusters of lower glucose metabolism among older adults, ranging from 200 to 2640 mm3 . The two largest clusters were in the left and right inferior frontal and superior temporal gyri and the insula. Clusters were also found in the inferior temporal junction, the anterior cingulate and caudate. Taken together, the results are consistent with research showing less efficient glucose metabolism in the ageing brain. The findings are discussed in the context of theories of cognitive ageing and are compared to those found in neurodegenerative disease.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneAustralia,Monash Biomedical ImagingMonash UniversityMelbourneAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneAustralia,Monash Biomedical ImagingMonash UniversityMelbourneAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia,Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneAustralia,Monash Biomedical ImagingMonash UniversityMelbourneAustralia,Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneAustralia,Monash Biomedical ImagingMonash UniversityMelbourneAustralia,Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneAustralia
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13
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Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease. J Med Syst 2022; 46:52. [PMID: 35713815 DOI: 10.1007/s10916-022-01836-w] [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: 02/28/2022] [Accepted: 05/16/2022] [Indexed: 10/18/2022]
Abstract
The purpose of this project is to develop and validate a Deep Learning (DL) FDG PET imaging algorithm able to identify patients with any neurodegenerative diseases (Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) or Dementia with Lewy Bodies (DLB)) among patients with Mild Cognitive Impairment (MCI). A 3D Convolutional neural network was trained using images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI dataset used for the model training and testing consisted of 822 subjects (472 AD and 350 MCI). The validation was performed on an independent dataset from La Fe University and Polytechnic Hospital. This dataset contained 90 subjects with MCI, 71 of them developed a neurodegenerative disease (64 AD, 4 FTD and 3 DLB) while 19 did not associate any neurodegenerative disease. The model had 79% accuracy, 88% sensitivity and 71% specificity in the identification of patients with neurodegenerative diseases tested on the 10% ADNI dataset, achieving an area under the receiver operating characteristic curve (AUC) of 0.90. On the external validation, the model preserved 80% balanced accuracy, 75% sensitivity, 84% specificity and 0.86 AUC. This binary classifier model based on FDG PET images allows the early prediction of neurodegenerative diseases in MCI patients in standard clinical settings with an overall 80% classification balanced accuracy.
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14
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Lee J, Ha S, Kim REY, Lee M, Kim D, Lim HK. Development of Amyloid PET Analysis Pipeline Using Deep Learning-Based Brain MRI Segmentation—A Comparative Validation Study. Diagnostics (Basel) 2022; 12:diagnostics12030623. [PMID: 35328176 PMCID: PMC8947654 DOI: 10.3390/diagnostics12030623] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 02/25/2022] [Accepted: 02/27/2022] [Indexed: 02/04/2023] Open
Abstract
Amyloid positron emission tomography (PET) scan is clinically essential for the non-invasive assessment of the presence and spatial distribution of amyloid-beta deposition in subjects with cognitive impairment suspected to have been a result of Alzheimer’s disease. Quantitative assessment can enhance the interpretation reliability of PET scan; however, its clinical application has been limited due to the complexity of preprocessing. This study introduces a novel deep-learning-based approach for SUVR quantification that simplifies the preprocessing step and significantly reduces the analysis time. Using two heterogeneous amyloid ligands, our proposed method successfully distinguished standardized uptake value ratio (SUVR) between amyloidosis-positive and negative groups. The proposed method’s intra-class correlation coefficients were 0.97 and 0.99 against PETSurfer and PMOD, respectively. The difference of global SUVRs between the proposed method and PETSurfer or PMOD were 0.04 and −0.02, which are clinically acceptable. The AUC-ROC exceeded 0.95 for three tools in the amyloid positive assessment. Moreover, the proposed method had the fastest processing time and had a low registration failure rate (1%). In conclusion, our proposed method calculates SUVR that is consistent with PETSurfer and PMOD, and has advantages of fast processing time and low registration failure rate. Therefore, PET quantification provided by our proposed method can be used in clinical practice.
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Affiliation(s)
- Jiyeon Lee
- Research Institute, Neurophet Inc., Seoul 06234, Korea; (J.L.); (R.E.Y.K.); (M.L.)
| | - Seunggyun Ha
- Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea;
| | - Regina E. Y. Kim
- Research Institute, Neurophet Inc., Seoul 06234, Korea; (J.L.); (R.E.Y.K.); (M.L.)
| | - Minho Lee
- Research Institute, Neurophet Inc., Seoul 06234, Korea; (J.L.); (R.E.Y.K.); (M.L.)
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul 06234, Korea; (J.L.); (R.E.Y.K.); (M.L.)
- Correspondence: (D.K.); (H.K.L.); Tel.: +82-10-9361-3781 (D.K.); +82-10-3797-6315 (H.K.L.)
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Korea
- Correspondence: (D.K.); (H.K.L.); Tel.: +82-10-9361-3781 (D.K.); +82-10-3797-6315 (H.K.L.)
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15
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Abstract
Abstract
In this partial review and partial attempt at vision of what may be the future of dedicated brain PET scanners, the key implementations of the PET technique, we postulate that we are still on a development path and there is still a lot to be done in order to develop optimal brain imagers. Optimized for particular imaging tasks and protocols, and also mobile, that can be used outside the PET center, in addition to the expected improvements in sensitivity and resolution. For this multi-application concept to be more practical, flexible, adaptable designs are preferred. This task is greatly facilitated by the improved TOF performance that allows for more open, adjustable, limited angular coverage geometries without creating image artifacts. As achieving uniform very high resolution in the whole body is not practical due to technological limits and high costs, hybrid systems using a moderate-resolution total body scanner (such as J-PET) combined with a very high performing brain imager could be a very attractive approach. As well, as using magnification inserts in the total body or long-axial length imagers to visualize selected targets with higher resolution. In addition, multigamma imagers combining PET with Compton imaging should be developed to enable multitracer imaging.
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16
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Guedj E, Varrone A, Boellaard R, Albert NL, Barthel H, van Berckel B, Brendel M, Cecchin D, Ekmekcioglu O, Garibotto V, Lammertsma AA, Law I, Peñuelas I, Semah F, Traub-Weidinger T, van de Giessen E, Van Weehaeghe D, Morbelli S. EANM procedure guidelines for brain PET imaging using [ 18F]FDG, version 3. Eur J Nucl Med Mol Imaging 2021; 49:632-651. [PMID: 34882261 PMCID: PMC8803744 DOI: 10.1007/s00259-021-05603-w] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/21/2021] [Indexed: 12/13/2022]
Abstract
The present procedural guidelines summarize the current views of the EANM Neuro-Imaging Committee (NIC). The purpose of these guidelines is to assist nuclear medicine practitioners in making recommendations, performing, interpreting, and reporting results of [18F]FDG-PET imaging of the brain. The aim is to help achieve a high-quality standard of [18F]FDG brain imaging and to further increase the diagnostic impact of this technique in neurological, neurosurgical, and psychiatric practice. The present document replaces a former version of the guidelines that have been published in 2009. These new guidelines include an update in the light of advances in PET technology such as the introduction of digital PET and hybrid PET/MR systems, advances in individual PET semiquantitative analysis, and current broadening clinical indications (e.g., for encephalitis and brain lymphoma). Further insight has also become available about hyperglycemia effects in patients who undergo brain [18F]FDG-PET. Accordingly, the patient preparation procedure has been updated. Finally, most typical brain patterns of metabolic changes are summarized for neurodegenerative diseases. The present guidelines are specifically intended to present information related to the European practice. The information provided should be taken in the context of local conditions and regulations.
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Affiliation(s)
- Eric Guedj
- APHM, CNRS, Centrale Marseille, Institut Fresnel, Timone Hospital, CERIMED, Nuclear Medicine Department, Aix Marseille Univ, Marseille, France. .,Service Central de Biophysique et Médecine Nucléaire, Hôpital de la Timone, 264 rue Saint Pierre, 13005, Marseille, France.
| | - Andrea Varrone
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Healthcare Services, Stockholm, Sweden
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Nathalie L Albert
- Department of Nuclear Medicine, Ludwig Maximilians-University of Munich, Munich, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University, Leipzig, Germany
| | - Bart van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Matthias Brendel
- Department of Nuclear Medicine, Ludwig Maximilians-University of Munich, Munich, Germany.,German Centre of Neurodegenerative Diseases (DZNE), Site Munich, Bonn, Germany
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine - DIMED, University of Padua, Padua, Italy
| | - Ozgul Ekmekcioglu
- Sisli Hamidiye Etfal Education and Research Hospital, Nuclear Medicine Dept., University of Health Sciences, Istanbul, Turkey
| | - Valentina Garibotto
- NIMTLab, Faculty of Medicine, Geneva University, Geneva, Switzerland.,Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Iván Peñuelas
- Department of Nuclear Medicine, Clinica Universidad de Navarra, IdiSNA, University of Navarra, Pamplona, Spain
| | - Franck Semah
- Nuclear Medicine Department, University Hospital, Lille, France
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Elsmarieke van de Giessen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC, Location AMC, Meibergdreef 9, Amsterdam, The Netherlands
| | | | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine Unit, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
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17
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Pijl JP, Nienhuis PH, Kwee TC, Glaudemans AWJM, Slart RHJA, Gormsen LC. Limitations and Pitfalls of FDG-PET/CT in Infection and Inflammation. Semin Nucl Med 2021; 51:633-645. [PMID: 34246448 DOI: 10.1053/j.semnuclmed.2021.06.008] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
White blood cells activated by either a pathogen or as part of a systemic inflammatory disease are characterized by high energy consumption and are therefore taking up the glucose analogue PET tracer FDG avidly. It is therefore not surprising that a steadily growing body of research and clinical reports now supports the use of FDG PET/CT to diagnose a wide range of patients with non-oncological diseases. However, using FDG PET/CT in patients with infectious or inflammatory diseases has some limitations and potential pitfalls that are not necessarily as pronounced in oncology FDG PET/CT. Some of these limitations are of a general nature and related to the laborious acquisition of PET images in patients that are often acutely ill, whereas others are more disease-specific and related to the particular metabolism in some of the organs most commonly affected by infections or inflammatory disease. Both inflammatory and infectious diseases are characterized by a more diffuse and less pathognomonic pattern of FDG uptake than oncology FDG PET/CT and the affected organs also typically have some physiological FDG uptake. In addition, patients referred to PET/CT with suspected infection or inflammation are rarely treatment naïve and may have received varying doses of antibiotics, corticosteroids or other immune-modulating drugs at the time of their examination. Combined, this results in a higher rate of false positive FDG findings and also in some cases a lower sensitivity to detect active disease. In this review, we therefore discuss the limitations and pitfalls of FDG PET/CT to diagnose infections and inflammation taking these issues into consideration. Our review encompasses the most commonly encountered inflammatory and infectious diseases in head and neck, in the cardiovascular system, in the abdominal organs and in the musculoskeletal system. Finally, new developments in the field of PET/CT that may help overcome some of these limitations are briefly highlighted.
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Affiliation(s)
- Jordy P Pijl
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen
| | - Pieter H Nienhuis
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen
| | - Thomas C Kwee
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen
| | - Andor W J M Glaudemans
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen
| | - Riemer H J A Slart
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen; Faculty of Science and Technology, Department of Biomedical Photonic Imaging, University of Twente, Enschede
| | - Lars C Gormsen
- Department of Nuclear Medicine & PET Center, Aarhus University Hospital, Aarhus N.
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18
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WANG Z, MANION MM, LAIDLAW E, RUPERT A, LAU CY, SMITH BR, NATH A, SERETI I, HAMMOUD DA. Redistribution of brain glucose metabolism in people with HIV after antiretroviral therapy initiation. AIDS 2021; 35:1209-1219. [PMID: 33710014 PMCID: PMC8556661 DOI: 10.1097/qad.0000000000002875] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
OBJECTIVE We evaluated brain glucose metabolism in people living with HIV (PWH) with [18F]-Fluoro-Deoxyglucose (FDG) PET/computed tomography (CT) before and after antiretroviral therapy (ART) initiation. DESIGN We conducted a longitudinal study wherein ART-naive late-presenting untreated PWH with CD4+ cell counts less than 100 cells/μl were prospectively assessed for FDG uptake at baseline and at 4-8 weeks (n = 22) and 19-26 months (n = 11) following ART initiation. METHODS Relative uptake in the subcortical regions (caudate, putamen and thalamus) and cortical regions (frontal, parietal, temporal and occipital cortices) were compared across time and correlated with biomarkers of disease activity and inflammation, in addition to being compared with a group of uninfected individuals (n = 10). RESULTS Before treatment initiation, putaminal and caudate relative FDG uptake values in PWH were significantly higher than in uninfected controls. Relative putaminal and thalamic uptake significantly decreased shortly following ART initiation, while frontal cortex values significantly increased. FDG uptake changes correlated with changes in CD4+ cell counts and viral load, and, in the thalamus, with IL-6R and sCD14. Approximately 2 years following ART initiation, there was further decrease in subcortical relative uptake values, reaching levels below those of uninfected controls. CONCLUSION Our findings support pretreatment basal ganglia and thalamic neuroinflammatory changes in PWH, which decrease after treatment with eventual unmasking of long-term irreversible neuronal damage. Meanwhile, increased frontal cortex metabolism following ART initiation suggests reversible cortical dysfunction which improves with virologic control and increased CD4+ cell counts. Early initiation of treatment after HIV diagnosis and secondary control of inflammation are thus necessary to halt neurological damage in PWH.
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Affiliation(s)
- Zeping WANG
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Maura M. MANION
- Laboratory of Immunoregulation, National Institute for Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Elizabeth LAIDLAW
- Laboratory of Immunoregulation, National Institute for Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Adam RUPERT
- Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
| | - Chuen-Yen LAU
- National Institute for Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Bryan R. SMITH
- Section of Infections of the Nervous System, National Institute of Neurological Diseases and Stroke, Bethesda, Maryland, USA
| | - Avindra NATH
- Section of Infections of the Nervous System, National Institute of Neurological Diseases and Stroke, Bethesda, Maryland, USA
| | - Irini SERETI
- Laboratory of Immunoregulation, National Institute for Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Dima A HAMMOUD
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
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19
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Liu H, Nai YH, Saridin F, Tanaka T, O' Doherty J, Hilal S, Gyanwali B, Chen CP, Robins EG, Reilhac A. Improved amyloid burden quantification with nonspecific estimates using deep learning. Eur J Nucl Med Mol Imaging 2021; 48:1842-1853. [PMID: 33415430 PMCID: PMC8113180 DOI: 10.1007/s00259-020-05131-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/18/2020] [Indexed: 11/28/2022]
Abstract
PURPOSE Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we propose a novel amyloid-PET quantification approach that harnesses the intermodal image translation capability of convolutional networks to remove this undesirable source of variability. METHODS Paired MR and PET images exhibiting very low specific uptake were selected from a Singaporean amyloid-PET study involving 172 participants with different severities of CeVD. Two convolutional neural networks (CNN), ScaleNet and HighRes3DNet, and one conditional generative adversarial network (cGAN) were trained to map structural MR to NS PET images. NS estimates generated for all subjects using the most promising network were then subtracted from SUVr images to determine specific amyloid load only (SAβL). Associations of SAβL with various cognitive and functional test scores were then computed and compared to results using conventional SUVr. RESULTS Multimodal ScaleNet outperformed other networks in predicting the NS content in cortical gray matter with a mean relative error below 2%. Compared to SUVr, SAβL showed increased association with cognitive and functional test scores by up to 67%. CONCLUSION Removing the undesirable NS uptake from the amyloid load measurement is possible using deep learning and substantially improves its accuracy. This novel analysis approach opens a new window of opportunity for improved data modeling in Alzheimer's disease and for other neurodegenerative diseases that utilize PET imaging.
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Affiliation(s)
- Haohui Liu
- Raffles Institution, Singapore, Singapore
| | - Ying-Hwey Nai
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Translational Medicine (MD6), 14 Medical Drive, #B1-01, Singapore, 117599, Singapore.
| | - Francis Saridin
- Memory Aging and Cognition Centre, National University Health System, Singapore, Singapore
| | - Tomotaka Tanaka
- Department of Neurology, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Jim O' Doherty
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Translational Medicine (MD6), 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
| | - Saima Hilal
- Memory Aging and Cognition Centre, National University Health System, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Bibek Gyanwali
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Christopher P Chen
- Memory Aging and Cognition Centre, National University Health System, Singapore, Singapore
| | - Edward G Robins
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Translational Medicine (MD6), 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
- Singapore BioImaging Consortium (SBIC), Agency for Science, Technology and Research (A*Star), Singapore, Singapore
| | - Anthonin Reilhac
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Translational Medicine (MD6), 14 Medical Drive, #B1-01, Singapore, 117599, Singapore
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20
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Gonzalez-Escamilla G, Miederer I, Grothe MJ, Schreckenberger M, Muthuraman M, Groppa S. Metabolic and amyloid PET network reorganization in Alzheimer's disease: differential patterns and partial volume effects. Brain Imaging Behav 2021; 15:190-204. [PMID: 32125613 PMCID: PMC7835313 DOI: 10.1007/s11682-019-00247-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder, considered a disconnection syndrome with regional molecular pattern abnormalities quantifiable by the aid of PET imaging. Solutions for accurate quantification of network dysfunction are scarce. We evaluate the extent to which PET molecular markers reflect quantifiable network metrics derived through the graph theory framework and how partial volume effects (PVE)-correction (PVEc) affects these PET-derived metrics 75 AD patients and 126 cognitively normal older subjects (CN). Therefore our goal is twofold: 1) to evaluate the differential patterns of [18F]FDG- and [18F]AV45-PET data to depict AD pathology; and ii) to analyse the effects of PVEc on global uptake measures of [18F]FDG- and [18F]AV45-PET data and their derived covariance network reconstructions for differentiating between patients and normal older subjects. Network organization patterns were assessed using graph theory in terms of “degree”, “modularity”, and “efficiency”. PVEc evidenced effects on global uptake measures that are specific to either [18F]FDG- or [18F]AV45-PET, leading to increased statistical differences between the groups. PVEc was further shown to influence the topological characterization of PET-derived covariance brain networks, leading to an optimised characterization of network efficiency and modularisation. Partial-volume effects correction improves the interpretability of PET data in AD and leads to optimised characterization of network properties for organisation or disconnection.
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Affiliation(s)
- Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.
| | - Isabelle Miederer
- Department of Nuclear Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE) - Rostock/Greifswald, Rostock, Germany
| | - Mathias Schreckenberger
- Department of Nuclear Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
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21
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Zhang YD, Dong Z, Wang SH, Yu X, Yao X, Zhou Q, Hu H, Li M, Jiménez-Mesa C, Ramirez J, Martinez FJ, Gorriz JM. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2020; 64:149-187. [PMID: 32834795 PMCID: PMC7366126 DOI: 10.1016/j.inffus.2020.07.006] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/06/2020] [Accepted: 07/14/2020] [Indexed: 05/13/2023]
Abstract
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
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Affiliation(s)
- Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Zhengchao Dong
- Department of Psychiatry, Columbia University, USA
- New York State Psychiatric Institute, New York, NY 10032, USA
| | - Shui-Hua Wang
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- School of Architecture Building and Civil engineering, Loughborough University, Loughborough, LE11 3TU, UK
- School of Mathematics and Actuarial Science, University of Leicester, LE1 7RH, UK
| | - Xiang Yu
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Xujing Yao
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Qinghua Zhou
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Hua Hu
- Department of Psychiatry, Columbia University, USA
- Department of Neurology, The Second Affiliated Hospital of Soochow University, China
| | - Min Li
- Department of Psychiatry, Columbia University, USA
- School of Internet of Things, Hohai University, Changzhou, China
| | - Carmen Jiménez-Mesa
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Francisco J Martinez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK
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22
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Raman F, Grandhi S, Murchison CF, Kennedy RE, Landau S, Roberson ED, McConathy J. Biomarker Localization, Analysis, Visualization, Extraction, and Registration (BLAzER) Methodology for Research and Clinical Brain PET Applications. J Alzheimers Dis 2020; 70:1241-1257. [PMID: 31322571 DOI: 10.3233/jad-190329] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Tools for efficient evaluation of amyloid- and tau-PET images are needed in both clinical and research settings. OBJECTIVE This study was designed to validate a semi-automated image analysis methodology, called Biomarker Localization, Analysis, Visualization, Extraction, and Registration (BLAzER). We tested BLAzER using two different segmentation platforms, FreeSurfer (FS) and Neuroreader (NR), for regional brain PET quantification in participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. METHODS 127 amyloid-PET and 55 tau-PET studies with volumetric MRIs were obtained from ADNI. The BLAzER methodology utilizes segmentation of MR images by FS or NR, then visualizes and quantifies regional brain PET data using FDA-cleared software (MIM), enabling quality control to ensure optimal registration and to detect segmentation errors. RESULTS BLAzER analysis required ∼5 min plus segmentation time. BLAzER using FS segmentation showed strong agreement with ADNI for global amyloid-PET standardized uptake value ratios (SUVRs) (r = 0.9922, p < 0.001) and regional tau-PET SUVRs across all Braak staging regions (r > 0.97, p < 0.001) with high inter-operator reproducibility (ICC > 0.97) and nearly identical dichotomization as amyloid-positive or -negative (2 discrepant cases out of 127). Comparing FS versus NR segmentation with BLAzER, global SUVRs were strongly correlated for amyloid-PET (r = 0.9841, p < 0.001), but were systematically higher (4% on average) with NR, likely due to more inclusion of white matter with NR-defined regions. CONCLUSIONS BLAzER provides an efficient methodology for regional brain PET quantification. FDA-cleared components and visualization of registration reduce barriers between research and clinical applications.
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Affiliation(s)
- Fabio Raman
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA.,Alzheimer's Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA.,Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA.,Center for Neurodegeneration and Experimental Therapeutics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sameera Grandhi
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA.,Alzheimer's Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Charles F Murchison
- Alzheimer's Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA.,Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Richard E Kennedy
- Alzheimer's Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA.,Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Susan Landau
- Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA, USA
| | - Erik D Roberson
- Alzheimer's Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA.,Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA.,Center for Neurodegeneration and Experimental Therapeutics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jonathan McConathy
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA.,Alzheimer's Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA
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23
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Oyama S, Hosoi A, Ibaraki M, McGinnity CJ, Matsubara K, Watanuki S, Watabe H, Tashiro M, Shidahara M. Error propagation analysis of seven partial volume correction algorithms for [ 18F]THK-5351 brain PET imaging. EJNMMI Phys 2020; 7:57. [PMID: 32926222 PMCID: PMC7490288 DOI: 10.1186/s40658-020-00324-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 08/24/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Novel partial volume correction (PVC) algorithms have been validated by assuming ideal conditions of image processing; however, in real clinical PET studies, the input datasets include error sources which cause error propagation to the corrected outcome. METHODS We aimed to evaluate error propagations of seven PVCs algorithms for brain PET imaging with [18F]THK-5351 and to discuss the reliability of those algorithms for clinical applications. In order to mimic brain PET imaging of [18F]THK-5351, pseudo-observed SUVR images for one healthy adult and one adult with Alzheimer's disease were simulated from individual PET and MR images. The partial volume effect of pseudo-observed PET images were corrected by using Müller-Gärtner (MG), the geometric transfer matrix (GTM), Labbé (LABBE), regional voxel-based (RBV), iterative Yang (IY), structural functional synergy for resolution recovery (SFS-RR), and modified SFS-RR algorithms with incorporation of error sources in the datasets for PVC processing. Assumed error sources were mismatched FWHM, inaccurate image-registration, and incorrectly segmented anatomical volume. The degree of error propagations in ROI values was evaluated by percent differences (%diff) of PV-corrected SUVR against true SUVR. RESULTS Uncorrected SUVRs were underestimated against true SUVRs (- 15.7 and - 53.7% in hippocampus for HC and AD conditions), and application of each PVC algorithm reduced the %diff. Larger FWHM mismatch led to larger %diff of PVC-SUVRs against true SUVRs for all algorithms. Inaccurate image registration showed systematic propagation for most algorithms except for SFS-RR and modified SFS-RR. Incorrect segmentation of the anatomical volume only resulted in error propagations in limited local regions. CONCLUSIONS We demonstrated error propagation by numerical simulation of THK-PET imaging. Error propagations of 7 PVC algorithms for brain PET imaging with [18F]THK-5351 were significant. Robust algorithms for clinical applications must be carefully selected according to the study design of clinical PET data.
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Affiliation(s)
- Senri Oyama
- Division of Cyclotron Nuclear Medicine, Cyclotron and Radioisotope Center, Sendai, Japan
| | - Ayumu Hosoi
- Division of Applied Quantum Medical Engineering, Department of Quantum Science and Energy Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Masanobu Ibaraki
- Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita Cerebrospinal and Cardiovascular Center, Akita, Japan
| | - Colm J McGinnity
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,King's College London and Guy's and St Thomas' PET Centre, St Thomas Hospital, London, UK
| | - Keisuke Matsubara
- Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita Cerebrospinal and Cardiovascular Center, Akita, Japan
| | - Shoichi Watanuki
- Division of Cyclotron Nuclear Medicine, Cyclotron and Radioisotope Center, Sendai, Japan
| | - Hiroshi Watabe
- Division of Radiation Protection and Safety Control, Cyclotron and Radioisotope Center, Tohoku University, Sendai, Japan
| | - Manabu Tashiro
- Division of Cyclotron Nuclear Medicine, Cyclotron and Radioisotope Center, Sendai, Japan
| | - Miho Shidahara
- Division of Cyclotron Nuclear Medicine, Cyclotron and Radioisotope Center, Sendai, Japan. .,Division of Applied Quantum Medical Engineering, Department of Quantum Science and Energy Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan.
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24
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Rullmann M, McLeod A, Grothe MJ, Sabri O, Barthel H. Reshaping the Amyloid Buildup Curve in Alzheimer Disease? Partial-Volume Effect Correction of Longitudinal Amyloid PET Data. J Nucl Med 2020; 61:1820-1824. [PMID: 32358089 DOI: 10.2967/jnumed.119.238477] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 03/28/2020] [Indexed: 01/27/2023] Open
Abstract
It was hypothesized that the brain β-amyloid buildup curve plateaus at an early symptomatic stage of Alzheimer disease (AD). Atrophy-related partial-volume effects (PVEs) degrade signal in hot-spot imaging techniques such as amyloid PET. The current study, a longitudinal analysis of amyloid-sensitive PET data, investigated the effect on the shape of the β-amyloid curve in AD when PVE correction (PVEC) is applied. Methods: We analyzed baseline and 2-y follow-up data for 216 symptomatic individuals on the AD continuum (positive amyloid status) enrolled in the Alzheimer's Disease Neuroimaging Initiative (17 with AD dementia and 199 with mild cognitive impairment), including 18F-florbetapir PET, MRI, and Mini Mental State Examination scores. For PVEC, the modified Müller-Gärtner method was performed. Results: Compared with non-PVE-corrected data, PVE-corrected data yielded significantly higher changes in regional and composite SUV ratio (SUVR) over time (P = 0.0002 for composite SUVRs). Longitudinal SUVR changes in relation to Mini Mental State Examination decreases showed a significantly higher slope for the regression line in the PVE-corrected than in the non-PVE-corrected PET data (F 1 = 7.1, P = 0.008). Conclusion: These PVEC results indicate that the β-amyloid buildup curve does not plateau at an early symptomatic disease stage. A further evaluation of the impact of PVEC on the in vivo characterization of time-dependent AD pathology, including the reliable assessment and comparison of other amyloid tracers, is warranted.
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Affiliation(s)
- Michael Rullmann
- Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany; and
| | - Anke McLeod
- Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany; and
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases-Rostock/Greifswald, Rostock, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany; and
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25
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Harmah DJ, Li C, Li F, Liao Y, Wang J, Ayedh WMA, Bore JC, Yao D, Dong W, Xu P. Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy. Front Comput Neurosci 2020; 13:85. [PMID: 31998105 PMCID: PMC6966771 DOI: 10.3389/fncom.2019.00085] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 12/04/2019] [Indexed: 12/31/2022] Open
Abstract
People living with schizophrenia (SCZ) experience severe brain network deterioration. The brain is constantly fizzling with non-linear causal activities measured by electroencephalogram (EEG) and despite the variety of effective connectivity methods, only few approaches can quantify the direct non-linear causal interactions. To circumvent this problem, we are motivated to quantitatively measure the effective connectivity by multivariate transfer entropy (MTE) which has been demonstrated to be able to capture both linear and non-linear causal relationships effectively. In this work, we propose to construct the EEG effective network by MTE and further compare its performance with the Granger causal analysis (GCA) and Bivariate transfer entropy (BVTE). The simulation results quantitatively show that MTE outperformed GCA and BVTE under varied signal-to-noise conditions, edges recovered, sensitivity, and specificity. Moreover, its applications to the P300 task EEG of healthy controls (HC) and SCZ patients further clearly show the deteriorated network interactions of SCZ, compared to that of the HC. The MTE provides a novel tool to potentially deepen our knowledge of the brain network deterioration of the SCZ.
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Affiliation(s)
- Dennis Joe Harmah
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Cunbo Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiuju Wang
- Institute of Mental Health, Peking University Sixth Hospital, National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Walid M. A. Ayedh
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Joyce Chelangat Bore
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Wentian Dong
- Institute of Mental Health, Peking University Sixth Hospital, National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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26
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Song TA, Yang F, Chowdhury SR, Kim K, Johnson KA, El Fakhri G, Li Q, Dutta J. PET Image Deblurring and Super-Resolution with an MR-Based Joint Entropy Prior. IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2019; 5:530-539. [PMID: 31723575 PMCID: PMC6853071 DOI: 10.1109/tci.2019.2913287] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The intrinsically limited spatial resolution of PET confounds image quantitation. This paper presents an image deblurring and super-resolution framework for PET using anatomical guidance provided by high-resolution MR images. The framework relies on image-domain post-processing of already-reconstructed PET images by means of spatially-variant deconvolution stabilized by an MR-based joint entropy penalty function. The method is validated through simulation studies based on the BrainWeb digital phantom, experimental studies based on the Hoffman phantom, and clinical neuroimaging studies pertaining to aging and Alzheimer's disease. The developed technique was compared with direct deconvolution and deconvolution stabilized by a quadratic difference penalty, a total variation penalty, and a Bowsher penalty. The BrainWeb simulation study showed improved image quality and quantitative accuracy measured by contrast-to-noise ratio, structural similarity index, root-mean-square error, and peak signal-to-noise ratio generated by this technique. The Hoffman phantom study indicated noticeable improvement in the structural similarity index (relative to the MR image) and gray-to-white contrast-to-noise ratio. Finally, clinical amyloid and tau imaging studies for Alzheimer's disease showed lowering of the coefficient of variation in several key brain regions associated with two target pathologies.
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Affiliation(s)
- Tzu-An Song
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA; Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Fan Yang
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA; Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Samadrita Roy Chowdhury
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA; Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Kyungsang Kim
- Massachusetts General Hospital, Boston, MA, 02114, USA
| | | | | | - Quanzheng Li
- Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Joyita Dutta
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA; Massachusetts General Hospital, Boston, MA, 02114, USA
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27
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Song TA, Roy Chowdhury S, Yang F, Jacobs H, El Fakhri G, Li Q, Johnson K, Dutta J. GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR ALZHEIMER'S DISEASE CLASSIFICATION. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2019; 2019:414-417. [PMID: 31327984 DOI: 10.1109/isbi.2019.8759531] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Graph convolutional neural networks (GCNNs) aim to extend the data representation and classification capabilities of convolutional neural networks, which are highly effective for signals defined on regular Euclidean domains, e.g. image and audio signals, to irregular, graph-structured data defined on non-Euclidean domains. Graph-theoretic tools that enable us to study the brain as a complex system are of great significance in brain connectivity studies. Particularly, in the context of Alzheimer's disease (AD), a neurodegenerative disorder associated with network dysfunction, graph-based tools are vital for disease classification and staging. Here, we implement and test a multi-class GCNN classifier for network-based classification of subjects on the AD spectrum into four categories: cognitively normal, early mild cognitive impairment, late mild cognitive impairment, and AD. We train and validate the network using structural connectivity graphs obtained from diffusion tensor imaging data. Using receiver operating characteristic curves, we show that the GCNN classifier outperforms a support vector machine classifier by margins that are reliant on disease category. Our findings indicate that the performance gap between the two methods increases with disease progression from CN to AD. We thus demonstrate that GCNN is a competitive tool for staging and classification of subjects on the AD spectrum.
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Affiliation(s)
- Tzu-An Song
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA
| | - Samadrita Roy Chowdhury
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA
| | - Fan Yang
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA
| | - Heidi Jacobs
- Gordon Center for Medical Imaging, Massachusetts General Hospital & Harvard Medical School, Boston, MA
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Massachusetts General Hospital & Harvard Medical School, Boston, MA
| | - Quanzheng Li
- Gordon Center for Medical Imaging, Massachusetts General Hospital & Harvard Medical School, Boston, MA
| | - Keith Johnson
- Gordon Center for Medical Imaging, Massachusetts General Hospital & Harvard Medical School, Boston, MA
| | - Joyita Dutta
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA.,Gordon Center for Medical Imaging, Massachusetts General Hospital & Harvard Medical School, Boston, MA
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28
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Nørgaard M, Ganz M, Svarer C, Frokjaer VG, Greve DN, Strother SC, Knudsen GM. Optimization of preprocessing strategies in Positron Emission Tomography (PET) neuroimaging: A [ 11C]DASB PET study. Neuroimage 2019; 199:466-479. [PMID: 31158479 DOI: 10.1016/j.neuroimage.2019.05.055] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 03/21/2019] [Accepted: 05/21/2019] [Indexed: 11/26/2022] Open
Abstract
Positron Emission Tomography (PET) is an important neuroimaging tool to quantify the distribution of specific molecules in the brain. The quantification is based on a series of individually designed data preprocessing steps (pipeline) and an optimal preprocessing strategy is per definition associated with less noise and improved statistical power, potentially allowing for more valid neurobiological interpretations. In spite of this, it is currently unclear how to design the best preprocessing pipeline and to what extent the choice of each preprocessing step in the pipeline minimizes subject-specific errors. To evaluate the impact of various preprocessing strategies, we systematically examined 384 different pipeline strategies in data from 30 healthy participants scanned twice with the serotonin transporter (5-HTT) radioligand [11C]DASB. Five commonly used preprocessing steps with two to four options were investigated: (1) motion correction (MC) (2) co-registration (3) delineation of volumes of interest (VOI's) (4) partial volume correction (PVC), and (5) kinetic modeling. To quantitatively compare and evaluate the impact of various preprocessing strategies, we used the performance metrics: test-retest bias, within- and between-subject variability, the intraclass-correlation coefficient, and global signal-to-noise ratio. We also performed a power analysis to estimate the required sample size to detect either a 5% or 10% difference in 5-HTT binding as a function of preprocessing pipeline. The results showed a complex downstream dependency between the various preprocessing steps on the performance metrics. The choice of MC had the most profound effect on 5-HTT binding, prior to the effects caused by PVC and kinetic modeling, and the effects differed across VOI's. Notably, we observed a negative bias in 5-HTT binding across test and retest in 98% of pipelines, ranging from 0 to 6% depending on the pipeline. Optimization of the performance metrics revealed a trade-off in within- and between-subject variability at the group-level with opposite effects (i.e. minimization of within-subject variability increased between-subject variability and vice versa). The sample size required to detect a given effect size was also compromised by the preprocessing strategy, resulting in up to 80% increases in sample size needed to detect a 5% difference in 5-HTT binding. This is the first study to systematically investigate and demonstrate the effect of choosing different preprocessing strategies on the outcome of dynamic PET studies. We provide a framework to show how optimal and maximally powered neuroimaging results can be obtained by choosing appropriate preprocessing strategies and we provide recommendations depending on the study design. In addition, the results contribute to a better understanding of methodological uncertainty and variability in preprocessing decisions for future group- and/or longitudinal PET studies.
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Affiliation(s)
- Martin Nørgaard
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Claus Svarer
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Gitte M Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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Kim J, Ghadery C, Cho SS, Mihaescu A, Christopher L, Valli M, Houle S, Strafella AP. Network Patterns of Beta-Amyloid Deposition in Parkinson's Disease. Mol Neurobiol 2019; 56:7731-7740. [PMID: 31111400 DOI: 10.1007/s12035-019-1625-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 04/24/2019] [Indexed: 01/07/2023]
Abstract
Beta-amyloid (Aβ) in the brain is a key pathological feature of certain neurodegenerative diseases. Recent studies using graph theory have shown that Aβ brain networks are of pathological significance in Alzheimer's disease (AD). However, the characteristics of Aβ brain networks in Parkinson's disease (PD) are unknown. In the present study using positron emission tomography (PET) with [11C]-Pittsburgh compound B (PiB), we applied a graph theory-based analysis to assess the topological properties of Aβ brain network in PD patients with and without Aβ burden (PiB-positive and PiB-negative, respectively) and healthy controls with Aβ burden. We found that the PD PiB-positive group demonstrated significantly lower value in global efficiency and modularity compared with PD PiB-negative group. The less robust modular structure indicates the tendency of having increased inter-modular connections than intra-modular connectivity (i.e., reduced segregation). Results of hub organization showed that relative to PD PiB-negative group, different hubs were identified in the PiB-positive group, which were located mainly within the default mode network. Overall, our findings suggest disturbances in Aβ topological organization characterized by abnormal network integration and segregation in PD patients with Aβ burden. The stronger inter-modular connectivity observed in the PD PiB-positive group may suggest the spreading pattern of Aβ between modules in those PD patients with elevated PiB burden, thus providing insight into the beta-amyloidopathy of PD.
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Affiliation(s)
- Jinhee Kim
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada. .,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada.
| | - Christine Ghadery
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Sang Soo Cho
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Alexander Mihaescu
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Leigh Christopher
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Mikaeel Valli
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Sylvain Houle
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
| | - Antonio P Strafella
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.,Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
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Abstract
Recent advances in disease understanding, instrumentation technology, and computationally demanding image analysis approaches are opening new frontiers in the investigation of movement disorders and brain disease in general. A key aspect is the recognition of the need to determine molecular correlates to early functional and metabolic connectivity alterations, which are increasingly recognized as useful signatures of specific clinical disease phenotypes. Such multi-modal approaches are highly likely to provide new information on pathogenic mechanisms and to help the identification of novel therapeutic targets. This chapter describes recent methodological developments in PET starting with a very brief overview of radiotracers relevant to movement disorders while emphasizing the development of instrumentation, algorithms and imaging analysis methods relevant to multi-modal investigation of movement disorders.
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Affiliation(s)
- Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.
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31
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Kim H, Han H. Computer-Aided Multi-Target Management of Emergent Alzheimer's Disease. Bioinformation 2018; 14:167-180. [PMID: 29983487 PMCID: PMC6016757 DOI: 10.6026/97320630014167] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 04/29/2018] [Accepted: 04/30/2018] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) represents an enormous global health burden in terms of human suffering and economic cost. AD management requires a shift from the prevailing paradigm targeting pathogenesis to design and develop effective drugs with adequate success in clinical trials. Therefore, it is of interest to report a review on amyloid beta (Aβ) effects and other multi-targets including cholinesterase, NFTs, tau protein and TNF associated with brain cell death to be neuro-protective from AD. It should be noted that these molecules have been generated either by target-based or phenotypic methods. Hence, the use of recent advancements in nanomedicine and other natural compounds screening tools as a feasible alternative for circumventing specific liabilities is realized. We review recent developments in the design and identification of neuro-degenerative compounds against AD generated using current advancements in computational multi-target modeling algorithms reflected by theragnosis (combination of diagnostic tests and therapy) concern.
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Affiliation(s)
- Hyunjo Kim
- Department of Medical Informatics, Ajou Medical University Hospital, Suwon, Kyeounggido province, South Korea
| | - Hyunwook Han
- Department of Informatics, School of Medicine, CHA University, Seongnam, South Korea
- Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam, South Korea
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