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Chang W, D'Ascenzo N, Antonecchia E, Li B, Yang J, Mu D, Li A, Xie Q. Deep denoiser prior driven relaxed iterated Tikhonov method for low-count PET image restoration. Phys Med Biol 2024; 69:165019. [PMID: 39053501 DOI: 10.1088/1361-6560/ad67a3] [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: 04/25/2024] [Accepted: 07/25/2024] [Indexed: 07/27/2024]
Abstract
Objective. Low-count positron emission tomography (PET) imaging is an efficient way to promote more widespread use of PET because of its short scan time and low injected activity. However, this often leads to low-quality PET images with clinical image reconstruction, due to high noise and blurring effects. Existing PET image restoration (IR) methods hinder their own restoration performance due to the semi-convergence property and the lack of suitable denoiser prior.Approach. To overcome these limitations, we propose a novel deep plug-and-play IR method called Deep denoiser Prior driven Relaxed Iterated Tikhonov method (DP-RI-Tikhonov). Specifically, we train a deep convolutional neural network denoiser to generate a flexible deep denoiser prior to handle high noise. Then, we plug the deep denoiser prior as a modular part into a novel iterative optimization algorithm to handle blurring effects and propose an adaptive parameter selection strategy for the iterative optimization algorithm.Main results. Simulation results show that the deep denoiser prior plays the role of reducing noise intensity, while the novel iterative optimization algorithm and adaptive parameter selection strategy can effectively eliminate the semi-convergence property. They enable DP-RI-Tikhonov to achieve an average quantitative result (normalized root mean square error, structural similarity) of (0.1364, 0.9574) at the stopping iteration, outperforming a conventional PET IR method with an average quantitative result of (0.1533, 0.9523) and a state-of-the-art deep plug-and-play IR method with an average quantitative result of (0.1404, 0.9554). Moreover, the advantage of DP-RI-Tikhonov becomes more obvious at the last iteration. Experiments on six clinical whole-body PET images further indicate that DP-RI-Tikhonov successfully reduces noise intensity and recovers fine details, recovering sharper and more uniform images than the comparison methods.Significance. DP-RI-Tikhonov's ability to reduce noise intensity and effectively eliminate the semi-convergence property overcomes the limitations of existing methods. This advancement may have substantial implications for other medical IR.
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Affiliation(s)
- Weike Chang
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Nicola D'Ascenzo
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, People's Republic of China
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, People's Republic of China
- Department of Innovation in Engineering and Physics, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S., Pozzilli, Italy
| | - Emanuele Antonecchia
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, People's Republic of China
- Department of Innovation in Engineering and Physics, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S., Pozzilli, Italy
| | - Bingxuan Li
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, People's Republic of China
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Dengyun Mu
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Ang Li
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Qingguo Xie
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, People's Republic of China
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, People's Republic of China
- Department of Innovation in Engineering and Physics, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S., Pozzilli, Italy
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Speranza G. Application of the Van Cittert Algorithm for Deconvolving Loss Features in X-ray Photoelectron Spectroscopy Spectra. MATERIALS (BASEL, SWITZERLAND) 2024; 17:763. [PMID: 38591627 PMCID: PMC10856702 DOI: 10.3390/ma17030763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/23/2024] [Accepted: 01/25/2024] [Indexed: 04/10/2024]
Abstract
The convolution of two physical entities, denoted as f and g, delineates the manner in which one entity undergoes modification in response to the other. This transformative process is mathematically represented by the expression f ⨂ g, symbolizing the convolution of the two entities in a resultant function h. Frequently, it becomes imperative to comprehend the magnitude of the induced modifications. From the derived function h, a crucial step involves the separation of the two original signals, a process commonly referred to as deconvolution. Various techniques have been proposed to facilitate the calculation of the deconvolution, with one notable approach originating in 1931 by van Cittert. The algorithm, based on an iterative method, has been scrutinized over time, notably by Bracewell and, more recently, by Jansson. This work represents the current state-of-the-art, focusing specifically on the analysis of Auger spectra obtained through XPS. Emphasis is placed on delineating the procedural aspects of the analysis, and the algorithm utilized in the open-source software RxpsG is comprehensively described.
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Affiliation(s)
- Giorgio Speranza
- Fondazione Bruno Kessler, v. Sommarive 18, 38123 Trento, Italy;
- Department of Industrial Engineering, University of Trento, v. Sommarive 9, 38123 Trento, Italy
- IFN-CNR, CSMFO Laboratory, v. alla Cascata 56/C, 38123 Trento, Italy
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Visser D, Tuncel H, Ossenkoppele R, Yaqub M, Wolters EE, Timmers T, Weltings E, Coomans EM, den Hollander ME, van der Flier WM, van Berckel BN, Golla SS. Longitudinal Tau PET Using 18F-Flortaucipir: The Effect of Relative Cerebral Blood Flow on Quantitative and Semiquantitative Parameters. J Nucl Med 2023; 64:281-286. [PMID: 36265910 PMCID: PMC9902853 DOI: 10.2967/jnumed.122.263926] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 08/11/2022] [Accepted: 08/11/2022] [Indexed: 02/04/2023] Open
Abstract
Semiquantitative PET measures such as SUV ratio (SUVr) have several advantages over quantitative measures, such as practical applicability and relative computational simplicity. However, SUVr may potentially be affected by changes in blood flow, whereas quantitative measures such as nondisplaceable binding potential (BPND) are not. For 18F-flortaucipir PET, the sensitivity of SUVr for changes in blood flow is currently unknown. Therefore, we compared semiquantitative (SUVr) and quantitative (BPND) parameters of longitudinal 18F-flortaucipir PET scans and assessed their vulnerability to changes in blood flow. Methods: Subjects with subjective cognitive decline (n = 38) and Alzheimer disease patients (n = 24) underwent baseline and 2-y follow-up dynamic 18F-flortaucipir PET scans. BPND and relative tracer delivery were estimated using receptor parametric mapping, and SUVr at 80-100 min was calculated. Regional SUVrs were compared with corresponding distribution volume ratio (BPND + 1) using paired t tests. Additionally, simulations were performed to model effects of larger flow changes in different binding categories. Results: Results in subjective cognitive decline and Alzheimer disease showed only minor differences between SUVr and BPND changes over time. Relative tracer delivery changes were small in all groups. Simulations illustrated a variable bias for SUVr depending on the amount of binding. Conclusion: SUVr provided an accurate estimate of changes in specific binding for 18F-flortaucipir over a 2-y follow-up during which changes in flow were small. Notwithstanding, simulations showed that large(r) flow changes may affect 18F-flortaucipir SUVr. Given that it is currently unknown to what order of magnitude pharmacotherapeutic interventions may induce changes in cerebral blood flow, caution may be warranted when changes in flow are potentially large(r), as in clinical trials.
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Affiliation(s)
- Denise Visser
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands;
| | - Hayel Tuncel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands;,Clinical Memory Research Unit, Lund University, Lund, Sweden; and
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Emma E. Wolters
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands;,Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Tessa Timmers
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands;,Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Emma Weltings
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Emma M. Coomans
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marijke E. den Hollander
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands;,Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Bart N.M. van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sandeep S.V. Golla
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT. Cancers (Basel) 2023; 15:cancers15030932. [PMID: 36765889 PMCID: PMC9913076 DOI: 10.3390/cancers15030932] [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: 12/27/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on 18F-FDG PET/CT images. METHODS 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. RESULTS There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. CONCLUSION Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
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Huiskamp M, Yaqub M, van Lingen MR, Pouwels PJW, de Ruiter LRJ, Killestein J, Schwarte LA, Golla SSV, van Berckel BNM, Boellaard R, Geurts JJG, Hulst HE. Cognitive performance in multiple sclerosis: what is the role of the gamma-aminobutyric acid system? Brain Commun 2023; 5:fcad140. [PMID: 37180993 PMCID: PMC10174207 DOI: 10.1093/braincomms/fcad140] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 01/26/2023] [Accepted: 04/28/2023] [Indexed: 05/16/2023] Open
Abstract
Cognitive impairment occurs in 40-65% of persons with multiple sclerosis and may be related to alterations in glutamatergic and GABAergic neurotransmission. Therefore, the aim of this study was to determine how glutamatergic and GABAergic changes relate to cognitive functioning in multiple sclerosis in vivo. Sixty persons with multiple sclerosis (mean age 45.5 ± 9.6 years, 48 females, 51 relapsing-remitting multiple sclerosis) and 22 age-matched healthy controls (45.6 ± 22.0 years, 17 females) underwent neuropsychological testing and MRI. Persons with multiple sclerosis were classified as cognitively impaired when scoring at least 1.5 standard deviations below normative scores on ≥30% of tests. Glutamate and GABA concentrations were determined in the right hippocampus and bilateral thalamus using magnetic resonance spectroscopy. GABA-receptor density was assessed using quantitative [11C]flumazenil positron emission tomography in a subset of participants. Positron emission tomography outcome measures were the influx rate constant (a measure predominantly reflecting perfusion) and volume of distribution, which is a measure of GABA-receptor density. Twenty persons with multiple sclerosis (33%) fulfilled the criteria for cognitive impairment. No differences were observed in glutamate or GABA concentrations between persons with multiple sclerosis and healthy controls, or between cognitively preserved, impaired and healthy control groups. Twenty-two persons with multiple sclerosis (12 cognitively preserved and 10 impaired) and 10 healthy controls successfully underwent [11C]flumazenil positron emission tomography. Persons with multiple sclerosis showed a lower influx rate constant in the thalamus, indicating lower perfusion. For the volume of distribution, persons with multiple sclerosis showed higher values than controls in deep grey matter, reflecting increased GABA-receptor density. When comparing cognitively impaired and preserved patients to controls, the preserved group showed a significantly higher volume of distribution in cortical and deep grey matter and hippocampus. Positive correlations were observed between both positron emission tomography measures and information processing speed in the multiple sclerosis group only. Whereas concentrations of glutamate and GABA did not differ between multiple sclerosis and control nor between cognitively impaired, preserved and control groups, increased GABA-receptor density was observed in preserved persons with multiple sclerosis that was not seen in cognitively impaired patients. In addition, GABA-receptor density correlated to cognition, in particular with information processing speed. This could indicate that GABA-receptor density is upregulated in the cognitively preserved phase of multiple sclerosis as a means to regulate neurotransmission and potentially preserve cognitive functioning.
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Affiliation(s)
- Marijn Huiskamp
- Correspondence to: M. Huiskamp Department of Anatomy & Neurosciences Amsterdam UMC, Location Vrije Universiteit PO Box 7057, 1007 MB Amsterdam, The Netherlands E-mail:
| | - Maqsood Yaqub
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Marike R van Lingen
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Petra J W Pouwels
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Lodewijk R J de Ruiter
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Joep Killestein
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Lothar A Schwarte
- Department of Anesthesiology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Sandeep S V Golla
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Ronald Boellaard
- Department of Radiology and nuclear medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Jeroen J G Geurts
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
| | - Hanneke E Hulst
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, 1081 HZ, The Netherlands
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, 2333 AK, The Netherlands
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Ahrari S, Zaragori T, Bros M, Oster J, Imbert L, Verger A. Implementing the Point Spread Function Deconvolution for Better Molecular Characterization of Newly Diagnosed Gliomas: A Dynamic 18F-FDOPA PET Radiomics Study. Cancers (Basel) 2022; 14:cancers14235765. [PMID: 36497245 PMCID: PMC9738921 DOI: 10.3390/cancers14235765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/10/2022] [Accepted: 11/17/2022] [Indexed: 11/25/2022] Open
Abstract
Purpose: This study aims to investigate the effects of applying the point spread function deconvolution (PSFd) to the radiomics analysis of dynamic L-3,4-dihydroxy-6-[18F]-fluoro-phenyl-alanine (18F-FDOPA) positron emission tomography (PET) images, to non-invasively identify isocitrate dehydrogenase (IDH) mutated and/or 1p/19q codeleted gliomas. Methods: Fifty-seven newly diagnosed glioma patients underwent dynamic 18F-FDOPA imaging on the same digital PET system. All images were reconstructed with and without PSFd. An L1-penalized (Lasso) logistic regression model, with 5-fold cross-validation and 20 repetitions, was trained with radiomics features extracted from the static tumor-to-background-ratio (TBR) and dynamic time-to-peak (TTP) parametric images, as well as a combination of both. Feature importance was assessed using Shapley additive explanation values. Results: The PSFd significantly modified 95% of TBR, but only 79% of TTP radiomics features. Applying the PSFd significantly improved the ability to identify IDH-mutated and/or 1p/19q codeleted gliomas, compared to PET images not processed with PSFd, with respective areas under the curve of 0.83 versus 0.79 and 0.75 versus 0.68 for a combination of static and dynamic radiomics features (p < 0.001). Without the PSFd, four and eight radiomics features contributed to 50% of the model for detecting IDH-mutated and/or 1p/19q codeleted gliomas, respectively. Application of the PSFd reduced this to three and seven contributive radiomics features. Conclusion: Application of the PSFd to dynamic 18F-FDOPA PET imaging significantly improves the detection of molecular parameters in newly diagnosed gliomas, most notably by modifying TBR radiomics features.
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Affiliation(s)
- Shamimeh Ahrari
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, F-54000 Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, F-54000 Nancy, France
| | - Timothée Zaragori
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, F-54000 Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, F-54000 Nancy, France
| | - Marie Bros
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, F-54000 Nancy, France
| | - Julien Oster
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, F-54000 Nancy, France
| | - Laetitia Imbert
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, F-54000 Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, F-54000 Nancy, France
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, F-54000 Nancy, France
| | - Antoine Verger
- Imagerie Adaptative Diagnostique et Interventionnelle, Institut National de la Santé et de la Recherche Médicale U1254, Université de Lorraine, F-54000 Nancy, France
- Nancyclotep Imaging Platform, Université de Lorraine, F-54000 Nancy, France
- Department of Nuclear Medicine, Centre Hospitalier Régional Universitaire de Nancy, F-54000 Nancy, France
- Correspondence:
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Flaus A, Deddah T, Reilhac A, Leiris ND, Janier M, Merida I, Grenier T, McGinnity CJ, Hammers A, Lartizien C, Costes N. PET image enhancement using artificial intelligence for better characterization of epilepsy lesions. Front Med (Lausanne) 2022; 9:1042706. [PMID: 36465898 PMCID: PMC9708713 DOI: 10.3389/fmed.2022.1042706] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 10/21/2022] [Indexed: 11/16/2023] Open
Abstract
INTRODUCTION [18F]fluorodeoxyglucose ([18F]FDG) brain PET is used clinically to detect small areas of decreased uptake associated with epileptogenic lesions, e.g., Focal Cortical Dysplasias (FCD) but its performance is limited due to spatial resolution and low contrast. We aimed to develop a deep learning-based PET image enhancement method using simulated PET to improve lesion visualization. METHODS We created 210 numerical brain phantoms (MRI segmented into 9 regions) and assigned 10 different plausible activity values (e.g., GM/WM ratios) resulting in 2100 ground truth high quality (GT-HQ) PET phantoms. With a validated Monte-Carlo PET simulator, we then created 2100 simulated standard quality (S-SQ) [18F]FDG scans. We trained a ResNet on 80% of this dataset (10% used for validation) to learn the mapping between S-SQ and GT-HQ PET, outputting a predicted HQ (P-HQ) PET. For the remaining 10%, we assessed Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Root Mean Squared Error (RMSE) against GT-HQ PET. For GM and WM, we computed recovery coefficients (RC) and coefficient of variation (COV). We also created lesioned GT-HQ phantoms, S-SQ PET and P-HQ PET with simulated small hypometabolic lesions characteristic of FCDs. We evaluated lesion detectability on S-SQ and P-HQ PET both visually and measuring the Relative Lesion Activity (RLA, measured activity in the reduced-activity ROI over the standard-activity ROI). Lastly, we applied our previously trained ResNet on 10 clinical epilepsy PETs to predict the corresponding HQ-PET and assessed image quality and confidence metrics. RESULTS Compared to S-SQ PET, P-HQ PET improved PNSR, SSIM and RMSE; significatively improved GM RCs (from 0.29 ± 0.03 to 0.79 ± 0.04) and WM RCs (from 0.49 ± 0.03 to 1 ± 0.05); mean COVs were not statistically different. Visual lesion detection improved from 38 to 75%, with average RLA decreasing from 0.83 ± 0.08 to 0.67 ± 0.14. Visual quality of P-HQ clinical PET improved as well as reader confidence. CONCLUSION P-HQ PET showed improved image quality compared to S-SQ PET across several objective quantitative metrics and increased detectability of simulated lesions. In addition, the model generalized to clinical data. Further evaluation is required to study generalization of our method and to assess clinical performance in larger cohorts.
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Affiliation(s)
- Anthime Flaus
- Department of Nuclear Medicine, Hospices Civils de Lyon, Lyon, France
- Faculté de Médecine Lyon Est, Université Claude Bernard Lyon 1, Lyon, France
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
- Lyon Neuroscience Research Center, INSERM U1028/CNRS UMR5292, Lyon, France
- CERMEP-Life Imaging, Lyon, France
| | | | - Anthonin Reilhac
- Brain Health Imaging Centre, Center for Addiction and Mental Health (CAHMS), Toronto, ON, Canada
| | - Nicolas De Leiris
- Departement of Nuclear Medicine, CHU Grenoble Alpes, University Grenoble Alpes, Grenoble, France
- Laboratoire Radiopharmaceutiques Biocliniques, University Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France
| | - Marc Janier
- Department of Nuclear Medicine, Hospices Civils de Lyon, Lyon, France
- Faculté de Médecine Lyon Est, Université Claude Bernard Lyon 1, Lyon, France
| | | | - Thomas Grenier
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Colm J. McGinnity
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Alexander Hammers
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Carole Lartizien
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Nicolas Costes
- Lyon Neuroscience Research Center, INSERM U1028/CNRS UMR5292, Lyon, France
- CERMEP-Life Imaging, Lyon, France
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Bevington CWJ, Cheng JC, Sossi V. A 4-D Iterative HYPR Denoising Operator Improves PET Image Quality. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3123537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Connor W. J. Bevington
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Ju-Chieh Cheng
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
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Jamadar SD, Liang EX, Zhong S, Ward PGD, Carey A, McIntyre R, Chen Z, Egan GF. Monash DaCRA fPET-fMRI: A dataset for comparison of radiotracer administration for high temporal resolution functional FDG-PET. Gigascience 2022; 11:giac031. [PMID: 35488859 PMCID: PMC9055854 DOI: 10.1093/gigascience/giac031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/31/2022] [Accepted: 03/06/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND "Functional" [18F]-fluorodeoxyglucose positron emission tomography (FDG-fPET) is a new approach for measuring glucose uptake in the human brain. The goal of FDG-fPET is to maintain a constant plasma supply of radioactive FDG in order to track, with high temporal resolution, the dynamic uptake of glucose during neuronal activity that occurs in response to a task or at rest. FDG-fPET has most often been applied in simultaneous BOLD-fMRI/FDG-fPET (blood oxygenation level-dependent functional MRI fluorodeoxyglucose functional positron emission tomography) imaging. BOLD-fMRI/FDG-fPET provides the capability to image the 2 primary sources of energetic dynamics in the brain, the cerebrovascular haemodynamic response and cerebral glucose uptake. FINDINGS In this Data Note, we describe an open access dataset, Monash DaCRA fPET-fMRI, which contrasts 3 radiotracer administration protocols for FDG-fPET: bolus, constant infusion, and hybrid bolus/infusion. Participants (n = 5 in each group) were randomly assigned to each radiotracer administration protocol and underwent simultaneous BOLD-fMRI/FDG-fPET scanning while viewing a flickering checkerboard. The bolus group received the full FDG dose in a standard bolus administration, the infusion group received the full FDG dose as a slow infusion over the duration of the scan, and the bolus-infusion group received 50% of the FDG dose as bolus and 50% as constant infusion. We validate the dataset by contrasting plasma radioactivity, grey matter mean uptake, and task-related activity in the visual cortex. CONCLUSIONS The Monash DaCRA fPET-fMRI dataset provides significant reuse value for researchers interested in the comparison of signal dynamics in fPET, and its relationship with fMRI task-evoked activity.
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Affiliation(s)
- Sharna D Jamadar
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC 3800, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, 3800 Australia
| | - Emma X Liang
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia
| | - Shenjun Zhong
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia
- National Imaging Facility, 4072, Australia
| | - Phillip G D Ward
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, 3800 Australia
| | - Alexandra Carey
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia
- Department of Medical Imaging, Monash Health, VIC 3800, Australia
| | - Richard McIntyre
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia
- Department of Medical Imaging, Monash Health, VIC 3800, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University
, Melbourne, VIC 3800, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, VIC 3800, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC 3800, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, 3800 Australia
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10
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Pain CD, Egan GF, Chen Z. Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement. Eur J Nucl Med Mol Imaging 2022; 49:3098-3118. [PMID: 35312031 PMCID: PMC9250483 DOI: 10.1007/s00259-022-05746-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/25/2022] [Indexed: 12/21/2022]
Abstract
Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.
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Affiliation(s)
- Cameron Dennis Pain
- Monash Biomedical Imaging, Monash University, Melbourne, Australia.
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia.
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Department of Data Science and AI, Monash University, Melbourne, Australia
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11
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Coomans EM, Tomassen J, Ossenkoppele R, Golla SSV, den Hollander M, Collij LE, Weltings E, van der Landen S, Wolters EE, Windhorst AD, Barkhof F, de Geus EJ, Scheltens P, Visser PJ, van Berckel BNM, den Braber A. Genetically identical twins show comparable tau PET load and spatial distribution. Brain 2022; 145:3571-3581. [PMID: 35022652 PMCID: PMC9586544 DOI: 10.1093/brain/awac004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/05/2021] [Accepted: 11/26/2021] [Indexed: 11/13/2022] Open
Abstract
Tau accumulation starts during the preclinical phase of Alzheimer’s disease and is closely associated with cognitive decline. For preventive purposes, it is important to identify factors associated with tau accumulation and spread. Studying genetically identical twin-pairs may give insight into genetic and environmental contributions to tau pathology, as similarities in identical twin-pairs largely result from genetic factors, while differences in identical twin-pairs can largely be attributed to non-shared, environmental factors. This study aimed to examine similarities and dissimilarities in a cohort of genetically identical older twin-pairs in (i) tau load; and (ii) spatial distribution of tau, measured with 18F-flortaucipir PET. We selected 78 genetically identical twins (39 pairs; average age 73 ± 6 years), enriched for amyloid-β pathology and APOE ε4 carriership, who underwent dynamic 18F-flortaucipir PET. We extracted binding potentials (BPND) in entorhinal, temporal, widespread neocortical and global regions, and examined within-pair similarities in BPND using age and sex corrected intra-class correlations. Furthermore, we tested whether twin-pairs showed a more similar spatial 18F-flortaucipir distribution compared to non-twin pairs, and whether the participant’s co-twin could be identified solely based on the spatial 18F-flortaucipir distribution. Last, we explored whether environmental (e.g. physical activity, obesity) factors could explain observed differences in twins of a pair in 18F-flortaucipir BPND. On visual inspection, Alzheimer’s disease-like 18F-flortaucipir PET patterns were observed, and although we mainly identified similarities in twin-pairs, some pairs showed strong dissimilarities. 18F-flortaucipir BPND was correlated in twins in the entorhinal (r = 0.40; P = 0.01), neocortical (r = 0.59; P < 0.01) and global (r = 0.56; P < 0.01) regions, but not in the temporal region (r = 0.20; P = 0.10). The 18F-flortaucipir distribution pattern was significantly more similar between twins of the same pair [mean r = 0.27; standard deviation (SD) = 0.09] than between non-twin pairings of participants (mean r = 0.01; SD = 0.10) (P < 0.01), also after correcting for proxies of off-target binding. Based on the spatial 18F-flortaucipir distribution, we could identify with an accuracy of 86% which twins belonged to the same pair. Finally, within-pair differences in 18F-flortaucipir BPND were associated with within-pair differences in depressive symptoms (0.37 < β < 0.56), physical activity (−0.41 < β < −0.42) and social activity (−0.32 < β < −0.36) (all P < 0.05). Overall, identical twin-pairs were comparable in tau load and spatial distribution, highlighting the important role of genetic factors in the accumulation and spreading of tau pathology. Considering also the presence of dissimilarities in tau pathology in identical twin-pairs, our results additionally support a role for (potentially modifiable) environmental factors in the onset of Alzheimer’s disease pathological processes, which may be of interest for future prevention strategies.
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Affiliation(s)
- Emma M. Coomans
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jori Tomassen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Sandeep S. V. Golla
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marijke den Hollander
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Lyduine E. Collij
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Emma Weltings
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sophie van der Landen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Emma E. Wolters
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Albert D. Windhorst
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- UCL Institute of Neurology, London, UK
| | - Eco J.C. de Geus
- Department of Biological Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm Sweden
| | - Bart N. M. van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Biological Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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12
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Differential associations between neocortical tau pathology and blood flow with cognitive deficits in early-onset vs late-onset Alzheimer's disease. Eur J Nucl Med Mol Imaging 2022; 49:1951-1963. [PMID: 34997294 PMCID: PMC9016024 DOI: 10.1007/s00259-021-05669-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 12/20/2021] [Indexed: 12/23/2022]
Abstract
Purpose Early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD) differ in neuropathological burden and type of cognitive deficits. Assessing tau pathology and relative cerebral blood flow (rCBF) measured with [18F]flortaucipir PET in relation to cognition may help explain these differences between EOAD and LOAD. Methods Seventy-nine amyloid-positive individuals with a clinical diagnosis of AD (EOAD: n = 35, age-at-PET = 59 ± 5, MMSE = 23 ± 4; LOAD: n = 44, age-at-PET = 71 ± 5, MMSE = 23 ± 4) underwent a 130-min dynamic [18F]flortaucipir PET scan and extensive neuropsychological assessment. We extracted binding potentials (BPND) and R1 (proxy of rCBF) from parametric images using receptor parametric mapping, in medial and lateral temporal, parietal, occipital, and frontal regions-of-interest and used nine neuropsychological tests covering memory, attention, language, and executive functioning. We first examined differences between EOAD and LOAD in BPND or R1 using ANOVA (region-of-interest analysis) and voxel-wise contrasts. Next, we performed linear regression models to test for potential interaction effects between age-at-onset and BPND/R1 on cognition. Results Both region-of-interest and voxel-wise contrasts showed higher [18F]flortaucipir BPND values across all neocortical regions in EOAD. By contrast, LOAD patients had lower R1 values (indicative of more reduced rCBF) in medial temporal regions. For both tau and flow in lateral temporal, and occipitoparietal regions, associations with cognitive impairment were stronger in EOAD than in LOAD (EOAD BPND − 0.76 ≤ stβ ≤ − 0.48 vs LOAD − 0.18 ≤ stβ ≤ − 0.02; EOAD R1 0.37 ≤ stβ ≤ 0.84 vs LOAD − 0.25 ≤ stβ ≤ 0.16). Conclusions Compared to LOAD, the degree of lateral temporal and occipitoparietal tau pathology and relative cerebral blood-flow is more strongly associated with cognition in EOAD. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05669-6.
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13
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Gao Y, Zhu Y, Bilgel M, Ashrafinia S, Lu L, Rahmim A. Voxel-based partial volume correction of PET images via subtle MRI guided non-local means regularization. Phys Med 2021; 89:129-139. [PMID: 34365117 DOI: 10.1016/j.ejmp.2021.07.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 07/18/2021] [Accepted: 07/19/2021] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Positron emission tomography (PET) images tend to be significantly degraded by the partial volume effect (PVE) resulting from the limited spatial resolution of the reconstructed images. Our purpose is to propose a partial volume correction (PVC) method to tackle this issue. METHODS In the present work, we explore a voxel-based PVC method under the least squares framework (LS) employing anatomical non-local means (NLMA) regularization. The well-known non-local means (NLM) filter utilizes the high degree of information redundancy that typically exists in images, and is typically used to directly reduce image noise by replacing each voxel intensity with a weighted average of its non-local neighbors. Here we explore NLM as a regularization term within iterative-deconvolution model to perform PVC. Further, an anatomical-guided version of NLM was proposed that incorporates MRI information into NLM to improve resolution and suppress image noise. The proposed approach makes subtle usage of the accompanying MRI information to define a more appropriate search space within the prior model. To optimize the regularized LS objective function, we used the Gauss-Seidel (GS) algorithm with the one-step-late (OSL) technique. RESULTS After the import of NLMA, the visual and quality results are all improved. With a visual check, we notice that NLMA reduce the noise compared to other PVC methods. This is also validated in bias-noise curve compared to non-MRI-guided PVC framework. We can see NLMA gives better bias-noise trade-off compared to other PVC methods. CONCLUSIONS Our efforts were evaluated in the base of amyloid brain PET imaging using the BrainWeb phantom and in vivo human data. We also compared our method with other PVC methods. Overall, we demonstrated the value of introducing subtle MRI-guidance in the regularization process, the proposed NLMA method resulting in promising visual as well as quantitative performance improvements.
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Affiliation(s)
- Yuanyuan Gao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China; Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA.
| | - Yansong Zhu
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 20892, USA
| | - Saeed Ashrafinia
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Arman Rahmim
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V5Z 1M9, Canada.
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14
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Zhu Y, Bilgel M, Gao Y, Rousset OG, Resnick SM, Wong DF, Rahmim A. Deconvolution-based partial volume correction of PET images with parallel level set regularization. Phys Med Biol 2021; 66. [PMID: 34157707 DOI: 10.1088/1361-6560/ac0d8f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/22/2021] [Indexed: 11/11/2022]
Abstract
The partial volume effect (PVE), caused by the limited spatial resolution of positron emission tomography (PET), degrades images both qualitatively and quantitatively. Anatomical information provided by magnetic resonance (MR) images has the potential to play an important role in partial volume correction (PVC) methods. Post-reconstruction MR-guided PVC methods typically use segmented MR tissue maps, and further, assume that PET activity distribution is uniform in each region, imposing considerable constraints through anatomical guidance. In this work, we present a post-reconstruction PVC method based on deconvolution with parallel level set (PLS) regularization. We frame the problem as an iterative deconvolution task with PLS regularization that incorporates anatomical information without requiring MR segmentation or assuming uniformity of PET distributions within regions. An efficient algorithm for non-smooth optimization of the objective function (invoking split Bregman framework) is developed so that the proposed method can be feasibly applied to 3D images and produces sharper images compared to PLS method with smooth optimization. The proposed method was evaluated together with several other PVC methods using both realistic simulation experiments based on the BrainWeb phantom as well asin vivohuman data. Our proposed method showed enhanced quantitative performance when realistic MR guidance was provided. Further, the proposed method is able to reduce image noise while preserving structure details onin vivohuman data, and shows the potential to better differentiate amyloid positive and amyloid negative scans. Overall, our results demonstrate promise to provide superior performance in clinical imaging scenarios.
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Affiliation(s)
- Yansong Zhu
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States of America.,Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, United States of America.,Department of Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, United States of America
| | - Yuanyuan Gao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, People's Republic of China
| | - Olivier G Rousset
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, United States of America
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, United States of America
| | - Dean F Wong
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States of America
| | - Arman Rahmim
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, United States of America.,Department of Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada
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15
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Paredes-Pacheco J, López-González FJ, Silva-Rodríguez J, Efthimiou N, Niñerola-Baizán A, Ruibal Á, Roé-Vellvé N, Aguiar P. SimPET-An open online platform for the Monte Carlo simulation of realistic brain PET data. Validation for 18 F-FDG scans. Med Phys 2021; 48:2482-2493. [PMID: 33713354 PMCID: PMC8252452 DOI: 10.1002/mp.14838] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 03/03/2021] [Accepted: 03/04/2021] [Indexed: 12/11/2022] Open
Abstract
Purpose SimPET (www.sim‐pet.org) is a free cloud‐based platform for the generation of realistic brain positron emission tomography (PET) data. In this work, we introduce the key features of the platform. In addition, we validate the platform by performing a comparison between simulated healthy brain FDG‐PET images and real healthy subject data for three commercial scanners (GE Advance NXi, GE Discovery ST, and Siemens Biograph mCT). Methods The platform provides a graphical user interface to a set of automatic scripts taking care of the code execution for the phantom generation, simulation (SimSET), and tomographic image reconstruction (STIR). We characterize the performance using activity and attenuation maps derived from PET/CT and MRI data of 25 healthy subjects acquired with a GE Discovery ST. We then use the created maps to generate synthetic data for the GE Discovery ST, the GE Advance NXi, and the Siemens Biograph mCT. The validation was carried out by evaluating Bland‐Altman differences between real and simulated images for each scanner. In addition, SPM voxel‐wise comparison was performed to highlight regional differences. Examples for amyloid PET and for the generation of ground‐truth pathological patients are included. Results The platform can be efficiently used for generating realistic simulated FDG‐PET images in a reasonable amount of time. The validation showed small differences between SimPET and acquired FDG‐PET images, with errors below 10% for 98.09% (GE Discovery ST), 95.09% (GE Advance NXi), and 91.35% (Siemens Biograph mCT) of the voxels. Nevertheless, our SPM analysis showed significant regional differences between the simulated images and real healthy patients, and thus, the use of the platform for converting control subject databases between different scanners requires further investigation. Conclusions The presented platform can potentially allow scientists in clinical and research settings to perform MC simulation experiments without the need for high‐end hardware or advanced computing knowledge and in a reasonable amount of time.
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Affiliation(s)
- José Paredes-Pacheco
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain
| | - Francisco Javier López-González
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain
| | - Jesús Silva-Rodríguez
- Nuclear Medicine Department & Molecular Imaging Research Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Galicia, Spain.,R&D Department, Qubiotech Health Intelligence SL, A Coruña, Galicia, Spain
| | - Nikos Efthimiou
- Positron Emission Tomography Research Centre, University of Hull, Hull, HU6 7RX, UK
| | - Aida Niñerola-Baizán
- Nuclear Medicine Department, Hospital Clinic Barcelona, Universitat de Barcelona, Barcelona, Spain.,Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Álvaro Ruibal
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Nuclear Medicine Department & Molecular Imaging Research Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Galicia, Spain
| | - Núria Roé-Vellvé
- Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Pablo Aguiar
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Nuclear Medicine Department & Molecular Imaging Research Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Galicia, Spain
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16
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Coomans EM, Schoonhoven DN, Tuncel H, Verfaillie SCJ, Wolters EE, Boellaard R, Ossenkoppele R, den Braber A, Scheper W, Schober P, Sweeney SP, Ryan JM, Schuit RC, Windhorst AD, Barkhof F, Scheltens P, Golla SSV, Hillebrand A, Gouw AA, van Berckel BNM. In vivo tau pathology is associated with synaptic loss and altered synaptic function. Alzheimers Res Ther 2021; 13:35. [PMID: 33546722 PMCID: PMC7866464 DOI: 10.1186/s13195-021-00772-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/11/2021] [Indexed: 01/08/2023]
Abstract
BACKGROUND The mechanism of synaptic loss in Alzheimer's disease is poorly understood and may be associated with tau pathology. In this combined positron emission tomography (PET) and magnetoencephalography (MEG) study, we aimed to investigate spatial associations between regional tau pathology ([18F]flortaucipir PET), synaptic density (synaptic vesicle 2A [11C]UCB-J PET) and synaptic function (MEG) in Alzheimer's disease. METHODS Seven amyloid-positive Alzheimer's disease subjects from the Amsterdam Dementia Cohort underwent dynamic 130-min [18F]flortaucipir PET, dynamic 60-min [11C]UCB-J PET with arterial sampling and 2 × 5-min resting-state MEG measurement. [18F]flortaucipir- and [11C]UCB-J-specific binding (binding potential, BPND) and MEG spectral measures (relative delta, theta and alpha power; broadband power; and peak frequency) were assessed in cortical brain regions of interest. Associations between regional [18F]flortaucipir BPND, [11C]UCB-J BPND and MEG spectral measures were assessed using Spearman correlations and generalized estimating equation models. RESULTS Across subjects, higher regional [18F]flortaucipir uptake was associated with lower [11C]UCB-J uptake. Within subjects, the association between [11C]UCB-J and [18F]flortaucipir depended on within-subject neocortical tau load; negative associations were observed when neocortical tau load was high, gradually changing into opposite patterns with decreasing neocortical tau burden. Both higher [18F]flortaucipir and lower [11C]UCB-J uptake were associated with altered synaptic function, indicative of slowing of oscillatory activity, most pronounced in the occipital lobe. CONCLUSIONS These results indicate that in Alzheimer's disease, tau pathology is closely associated with reduced synaptic density and synaptic dysfunction.
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Affiliation(s)
- Emma M Coomans
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Deborah N Schoonhoven
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Hayel Tuncel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sander C J Verfaillie
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Emma E Wolters
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Wiep Scheper
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands
- Center for Neurogenomics and Cognitive Research, Department of Functional Genomics, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick Schober
- Department of Anaesthesiology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | | | | | - Robert C Schuit
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Albert D Windhorst
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- UCL Institutes of Neurology and Healthcare Engineering, London, UK
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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17
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Yousefzadeh-Nowshahr E, Winter G, Bohn P, Kneer K, von Arnim CAF, Otto M, Solbach C, Anderl-Straub S, Polivka D, Fissler P, Prasad V, Kletting P, Riepe MW, Higuchi M, Ludolph A, Beer AJ, Glatting G. Comparison of MRI-based and PET-based image pre-processing for quantification of 11C-PBB3 uptake in human brain. Z Med Phys 2021; 31:37-47. [PMID: 33454153 DOI: 10.1016/j.zemedi.2020.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 11/11/2020] [Accepted: 12/03/2020] [Indexed: 11/30/2022]
Abstract
PURPOSE Quantification of tau load using 11C-PBB3-PET has the potential to improve diagnosis of neurodegenerative diseases. Although MRI-based pre-processing is used as a reference method, not all patients have MRI. The feasibility of a PET-based pre-processing for the quantification of 11C-PBB3 tracer was evaluated and compared with the MRI-based method. MATERIALS AND METHODS Fourteen patients with decreased recent memory were examined with 11C-PBB3-PET and MRI. The PET scans were visually assessed and rated as either PBB3(+) or PBB3(-). The image processing based on the PET-based method was validated against the MRI-based approach. The regional uptakes were quantified using the Mesial-temporal/Temporoparietal/Rest of neocortex (MeTeR) regions. SUVR values were calculated by normalizing to the cerebellar reference region to compare both methods within the patient groups. RESULTS Significant correlations were observed between the SUVRs of the MRI-based and the PET-based methods in the MeTeR regions (rMe=0.91; rTe=0.98; rR=0.96; p<0.0001). However, the Bland-Altman plot showed a significant bias between both methods in the subcortical Me region (bias: -0.041; 95% CI: -0.061 to -0.024; p=0.003). As in the MRI-based method, the 11C-PBB3 uptake obtained with the PET-based method was higher for the PBB3(+) group in each of the cortical regions and for the whole brain than for the PBB3(-) group (PET-basedGlobal: 1.11 vs. 0.96; Cliff's Delta (d)=0.68; p=0.04; MRI-basedGlobal: 1.11 vs. 0.97; d=0.70; p=0.03). To differentiate between positive and negative scans, Youden's index estimated the best cut-off of 0.99 from the ROC curve with good accuracy (AUC: 0.88±0.10; 95% CI: 0.67-1.00) and the same sensitivity (83%) and specificity (88%) for both methods. CONCLUSION The PET-based pre-processing method developed to quantify the tau burden with 11C-PBB3 provided comparable SUVR values and effect sizes as the MRI-based reference method. Furthermore, both methods have a comparable discrimination accuracy between PBB3(+) and PBB3(-) groups as assessed by visual rating. Therefore, the presented PET-based method can be used for clinical diagnosis if no MRI image is available.
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Affiliation(s)
- Elham Yousefzadeh-Nowshahr
- Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, Germany; Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Gordon Winter
- Department of Nuclear Medicine, Ulm University, Ulm, Germany
| | - Peter Bohn
- Department of Nuclear Medicine, Inselspital Bern - University of Bern, Bern, Switzerland
| | - Katharina Kneer
- Department of Nuclear Medicine, Ulm University, Ulm, Germany
| | - Christine A F von Arnim
- Department of Neurology, Ulm University, Ulm, Germany; Department of Geriatrics, University Medical Center Göttingen, Göttingen, Germany
| | - Markus Otto
- Department of Neurology, Ulm University, Ulm, Germany
| | | | | | - Dörte Polivka
- Department of Neurology, Ulm University, Ulm, Germany
| | - Patrick Fissler
- Department of Neurology, Ulm University, Ulm, Germany; Psychiatric Services of Thurgovia (Academic Teaching Hospital of Medical University Salzburg), Münsterlingen, Switzerland
| | - Vikas Prasad
- Department of Nuclear Medicine, Ulm University, Ulm, Germany
| | - Peter Kletting
- Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, Germany; Department of Nuclear Medicine, Ulm University, Ulm, Germany
| | - Matthias W Riepe
- Department of Psychiatry and Psychotherapy II, Ulm University, Ulm, Germany
| | - Makoto Higuchi
- National Institute of Radiological Sciences, Chiba, Japan
| | - Albert Ludolph
- Department of Neurology, Ulm University, Ulm, Germany; German Center for Neurodegerative Diseases (DZNE), Ulm, Germany
| | - Ambros J Beer
- Department of Nuclear Medicine, Ulm University, Ulm, Germany
| | - Gerhard Glatting
- Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, Germany; Department of Nuclear Medicine, Ulm University, Ulm, Germany
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18
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Visser D, Wolters EE, Verfaillie SCJ, Coomans EM, Timmers T, Tuncel H, Reimand J, Boellaard R, Windhorst AD, Scheltens P, van der Flier WM, Ossenkoppele R, van Berckel BNM. Tau pathology and relative cerebral blood flow are independently associated with cognition in Alzheimer's disease. Eur J Nucl Med Mol Imaging 2020; 47:3165-3175. [PMID: 32462397 PMCID: PMC7680306 DOI: 10.1007/s00259-020-04831-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 04/16/2020] [Indexed: 12/18/2022]
Abstract
Purpose We aimed to investigate associations between tau pathology and relative cerebral blood flow (rCBF), and their relationship with cognition in Alzheimer’s disease (AD), by using a single dynamic [18F]flortaucipir positron emission tomography (PET) scan. Methods Seventy-one subjects with AD (66 ± 8 years, mini-mental state examination (MMSE) 23 ± 4) underwent a dynamic 130-min [18F]flortaucipir PET scan. Cognitive assessment consisted of composite scores of four cognitive domains. For tau pathology and rCBF, receptor parametric mapping (cerebellar gray matter reference region) was used to create uncorrected and partial volume-corrected parametric images of non-displaceable binding potential (BPND) and R1, respectively. (Voxel-wise) linear regressions were used to investigate associations between BPND and/or R1 and cognition. Results Higher [18F]flortaucipir BPND was associated with lower R1 in the lateral temporal, parietal and occipital regions. Higher medial temporal BPND was associated with worse memory, and higher lateral temporal BPND with worse executive functioning and language. Higher parietal BPND was associated with worse executive functioning, language and attention, and higher occipital BPND with lower cognitive scores across all domains. Higher frontal BPND was associated with worse executive function and attention. For [18F]flortaucipir R1, lower values in the lateral temporal and parietal ROIs were associated with worse executive functioning, language and attention, and lower occipital R1 with lower language and attention scores. When [18F]flortaucipir BPND and R1 were modelled simultaneously, associations between lower R1 in the lateral temporal ROI and worse attention remained, as well as for lower parietal R1 and worse executive functioning and attention. Conclusion Tau pathology was associated with locally reduced rCBF. Tau pathology and low rCBF were both independently associated with worse cognitive performance. For tau pathology, these associations spanned widespread neocortex, while for rCBF, independent associations were restricted to lateral temporal and parietal regions and the executive functioning and attention domains. These findings indicate that each biomarker may independently contribute to cognitive impairment in AD. Electronic supplementary material The online version of this article (10.1007/s00259-020-04831-w) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Denise Visser
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Emma E Wolters
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sander C J Verfaillie
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Emma M Coomans
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Tessa Timmers
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Hayel Tuncel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Juhan Reimand
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Albert D Windhorst
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Bart N M van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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19
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Wolters EE, Ossenkoppele R, Golla SS, Verfaillie SC, Timmers T, Visser D, Tuncel H, Coomans EM, Windhorst AD, Scheltens P, van der Flier WM, Boellaard R, van Berckel BN. Hippocampal [ 18F]flortaucipir BP ND corrected for possible spill-in of the choroid plexus retains strong clinico-pathological relationships. NEUROIMAGE-CLINICAL 2019; 25:102113. [PMID: 31835238 PMCID: PMC6920114 DOI: 10.1016/j.nicl.2019.102113] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 11/22/2019] [Accepted: 12/01/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Off-target [18F]flortaucipir (tau) PET binding in the choroid plexus causes spill-in into the nearby hippocampus, which may influence the correlation between [18F]flortaucipir binding and measures of cognition. Previously, we showed that partial volume correction (combination of Van Cittert iterative deconvolution and HYPR denoising; PVC HDH) and manually eroding the hippocampus resulted in a significant decrease of the choroid plexus spill-in. In this study, we compared three different approaches for the quantification of hippocampal [18F]flortaucipir signal using a semi-automated technique, and assessed correlations with cognitive performance across methods. METHODS Dynamic 130 min [18F]flortaucipir PET scans were performed in 109 subjects (45 cognitively normal subjects (CN) and 64 mild cognitive impairment/Alzheimer's disease (AD) dementia patients. We extracted hippocampal binding potential (BPND) using receptor parametric mapping with cerebellar grey matter as reference region. PVC HDH was performed. Based on our previous study in which we manually eroded 40% ± 10% of voxels of the hippocampus, three hippocampal volumes-of-interest (VOIs) were generated: a non-optimized 100% hippocampal VOI [100%], and combining HDH with eroding a percentage of the highest hippocampus BPND voxels (i.e. lowering spill-in) resulting in optimized 50%[50%HDH] and 40%[40%HDH] hippocampal VOIs. Cognitive performance was assessed with the Mini-Mental State Examination (MMSE) and Rey auditory verbal learning delayed recall. We performed receiver operating characteristic analyses to investigate which method could best discriminate MCI/AD from controls. Subsequently, we performed linear regressions to investigate associations between the hippocampal [18F]flortaucipir BPND VOIs and MMSE/delayed recall adjusted for age, sex and education. RESULTS We found higher hippocampal [18F]flortaucipir BPND in MCI/AD patients (BPND100%=0.27±0.15) compared to CN (BPND100%= 0.07±0.13) and all methods showed comparable discriminative effects (AUC100%=0.85[CI=0.78-0.93]; AUC50%HDH=0.84[CI=0.74-0.92]; AUC40%HDH=0.83[CI=0.74-0.92]). Across groups, higher [18F]flortaucipir BPND was related to lower scores on MMSE (standardized β100%=-0.38[CI=-0.57-0.20]; β50%HDH= -0.37[CI=-0.54-0.19]; β40%HDH=-0.35[CI=-0.53-0.17], all p<0.001) and delayed recall (standardized β100%=-0.64[CI=-0.79-0.49]; β50%HDH= -0.61[CI=-0.76-0.46]; β40%HDH=-0.59[CI=-0.75-0.44]; all p<0.001), with comparable effect sizes for all hippocampal VOIs. CONCLUSIONS Hippocampal tau load measured with [18F]flortaucipir PET is strongly associated with cognitive function. Both discrimination between diagnostic groups and associations between hippocampal [18F]flortaucipir BPND and memory were comparable for all methods. The non-optimized 100% hippocampal VOI may be sufficient for clinical interpretation. However, proper correction for choroid plexus spillover and may be required in case of smaller effect sizes between subject groups or for longitudinal studies.
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Affiliation(s)
- Emma E Wolters
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands; Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Sandeep Sv Golla
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands
| | - Sander Cj Verfaillie
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands
| | - Tessa Timmers
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands; Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Denise Visser
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands
| | - Hayel Tuncel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands
| | - Emma M Coomans
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands
| | - Albert D Windhorst
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands
| | - Bart Nm van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands
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20
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Timmers T, Ossenkoppele R, Wolters EE, Verfaillie SCJ, Visser D, Golla SSV, Barkhof F, Scheltens P, Boellaard R, van der Flier WM, van Berckel BNM. Associations between quantitative [ 18F]flortaucipir tau PET and atrophy across the Alzheimer's disease spectrum. ALZHEIMERS RESEARCH & THERAPY 2019; 11:60. [PMID: 31272512 PMCID: PMC6610969 DOI: 10.1186/s13195-019-0510-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 06/06/2019] [Indexed: 01/09/2023]
Abstract
Background Neuropathological studies have linked tau aggregates to neuronal loss. To describe the spatial distribution of neurofibrillary tangle pathology in post-mortem tissue, Braak staging has been used. The aim of this study was to examine in vivo associations between tau pathology, quantified with [18F]flortaucipir PET in regions corresponding to Braak stages, and atrophy across the Alzheimer’s disease (AD) spectrum. Methods We included 100 subjects, including 58 amyloid-β positive patients with mild cognitive impairment (MCI, n = 6) or AD dementia (n = 52) and 42 controls with subjective cognitive decline (36% amyloid-β positive). All subjects underwent a dynamic [18F]flortaucipir PET to generate non-displaceable binding potential (BPND) maps. We extracted average [18F]flortaucipir BPND entorhinal, Braak III–IV (limbic) and Braak V–VI (neocortical) regions of interest (ROIs). T1-weighted MRI was used to assess gray matter (GM) volumes. We performed linear regression analyses using [18F]flortaucipir BPND ROIs as independent and GM density (ROI or voxelwise) as dependent variable. Results In MCI/AD subjects (age [mean ± SD] 65 ± 8 years, MMSE 23 ± 4), [18F]flortaucipir BPND was higher than in controls (age 65 ± 8, MMSE 29 ± 1) across all ROIs (entorhinal 0.06 ± 0.21 vs 0.46 ± 0.25 p < 0.001, Braak III–IV 0.11 ± 0.10 vs 0.46 ± 0.26, p < 0.001, Braak V–VI 0.07 ± 0.07 vs 0.38 ± 0.29, p < 0.001). In MCI/AD, greater [18F]flortaucipir BPND in entorhinal cortex was associated with lower GM density in medial temporal lobe (β − 0.40, p < 0.001). Greater [18F]flortaucipir BPND in ROI Braak III–IV and Braak V–VI was associated with smaller GM density in lateral and inferior temporal, parietal, occipital, and frontal lobes (range standardized βs − 0.30 to − 0.55, p < 0.01), but not in medial temporal lobe (β − 0.22, p 0.07). [18F]Flortaucipir BPND in ROI Braak I–II was not associated with GM density loss anywhere. When quantifying [18F]flortaucipir BPND across brain lobes, we observed both local and distant associations with GM atrophy. In controls, there were no significant associations between [18F]flortaucipir BPND and GM density (standardized βs ranging from − 0.24 to 0.02, all p > 0.05). Conclusions In MCI/AD patients, [18F]flortaucipir binding in entorhinal, limbic, and neocortical regions was associated with cortical atrophy. Electronic supplementary material The online version of this article (10.1186/s13195-019-0510-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tessa Timmers
- Department of Radiology, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands. .,Amsterdam Alzheimer Center, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands.
| | - Rik Ossenkoppele
- Amsterdam Alzheimer Center, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands.,Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Emma E Wolters
- Department of Radiology, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands.,Amsterdam Alzheimer Center, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
| | - Sander C J Verfaillie
- Department of Radiology, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands.,Amsterdam Alzheimer Center, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
| | - Denise Visser
- Department of Radiology, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Department of Radiology, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands.,Institutes of Neurology and Healthcare Engineering, UCLK, London, UK
| | - Philip Scheltens
- Amsterdam Alzheimer Center, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Amsterdam Alzheimer Center, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands.,Department of Epidemiology and Biostatistics, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology, Amsterdam UMC, location VUmc, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
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21
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Cysouw MCF, Golla SVS, Frings V, Smit EF, Hoekstra OS, Kramer GM, Boellaard R. Partial-volume correction in dynamic PET-CT: effect on tumor kinetic parameter estimation and validation of simplified metrics. EJNMMI Res 2019; 9:12. [PMID: 30715647 PMCID: PMC6362178 DOI: 10.1186/s13550-019-0483-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 01/25/2019] [Indexed: 12/27/2022] Open
Abstract
Background Partial-volume effects generally result in an underestimation of tumor tracer uptake on PET-CT for small lesions, necessitating partial-volume correction (PVC) for accurate quantification. However, investigation of PVC in dynamic oncological PET studies to date is scarce. The aim of this study was to investigate PVC’s impact on tumor kinetic parameter estimation from dynamic PET-CT acquisitions and subsequent validation of simplified semi-quantitative metrics. Ten patients with EGFR-mutated non-small cell lung cancer underwent dynamic 18F-fluorothymidine PET-CT before, 7 days after, and 28 days after commencing treatment with a tyrosine kinase inhibitor. Parametric PVC was applied using iterative deconvolution without and with highly constrained backprojection (HYPR) denoising, respectively. Using an image-derived input function with venous parent plasma calibration, we estimated full kinetic parameters VT, K1, and k3/k4 (BPND) using a reversible two-tissue compartment model, and simplified metrics (SUV and tumor-to-blood ratio) at 50–60 min post-injection. Results PVC had a non-linear effect on measured activity concentrations per timeframe. PVC significantly changed each kinetic parameter, with a median increase in VT of 11.8% (up to 25.1%) and 10.8% (up to 21.7%) without and with HYPR, respectively. Relative changes in kinetic parameter estimates vs. simplified metrics after applying PVC were poorly correlated (correlations 0.36–0.62; p < 0.01). PVC increased correlations between simplified metrics and VT from 0.82 and 0.81 (p < 0.01) to 0.90 and 0.88 (p < 0.01) for SUV and TBR, respectively, albeit non-significantly. PVC also increased correlations between treatment-induced changes in simplified metrics vs. VT at 7 (SUV) and 28 (SUV and TBR) days after treatment start non-significantly. Delineation on partial-volume corrected PET images resulted in a median decrease in metabolic tumor volume of 14.3% (IQR − 22.1 to − 7.5%), and increased the effect of PVC on kinetic parameter estimates. Conclusion PVC has a significant impact on tumor kinetic parameter estimation from dynamic PET-CT data, which differs from its effect on simplified metrics. However, it affected validation of these simplified metrics both as single measurements and as biomarkers of treatment response only to a small extent. Future dynamic PET studies should preferably incorporate PVC. Trial registration Dutch Trial Register, NTR3557. Electronic supplementary material The online version of this article (10.1186/s13550-019-0483-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- M C F Cysouw
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands.
| | - S V S Golla
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - V Frings
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - E F Smit
- Department of Thoracic Oncology, Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam, the Netherlands
| | - O S Hoekstra
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - G M Kramer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - R Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
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22
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A novel partial volume correction method for accurate quantification of [ 18F] flortaucipir in the hippocampus. EJNMMI Res 2018; 8:79. [PMID: 30112620 PMCID: PMC6093830 DOI: 10.1186/s13550-018-0432-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 08/06/2018] [Indexed: 12/21/2022] Open
Abstract
Background Off-target binding in the choroid plexus (CP) may cause spill-in of the tau PET tracer [18F] flortaucipir into the adjacent hippocampus region. The impact of this spill-in on hippocampal uptake was assessed using a novel partial volume correction method (PVC). Methods PVC was performed on 20 [18F] flortaucipir dynamic PET scans (10 probable AD and 10 controls). Volumes of interest (VOIs) were defined for both hippocampus and CP. The correlation between hippocampal and CP distribution volume (VT), with and without PVC, was determined. Both anatomically defined and eroded VOIs were used. Results For controls, the correlation between hippocampal and CP VT was significantly reduced after using PVC along with an eroded VOI (r2 = 0.59, slope = 0.80 versus r2 = 0.15, slope = 0.15; difference: p < 0.05). The same was true for AD patients (p < 0.05). Conclusion PVC together with an optimized hippocampal VOI resulted in effective reduction of CP spill-in and improved accuracy of hippocampal VT. Electronic supplementary material The online version of this article (10.1186/s13550-018-0432-2) contains supplementary material, which is available to authorized users.
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