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Lu J, Ju Z, Wang M, Sun X, Jia C, Li L, Bao W, Zhang H, Jiao F, Lin H, Yen TC, Cui R, Lan X, Zhao Q, Guan Y, Zuo C. Feasibility of 18F-florzolotau quantification in patients with Alzheimer's disease based on an MRI-free tau PET template. Eur Radiol 2023:10.1007/s00330-023-09571-7. [PMID: 37099173 DOI: 10.1007/s00330-023-09571-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/25/2023] [Accepted: 02/09/2023] [Indexed: 04/27/2023]
Abstract
OBJECTIVES Quantification of tau accumulation using positron emission tomography (PET) is critical for the diagnosis of Alzheimer's disease (AD). This study aimed to evaluate the feasibility of 18F-florzolotau quantification in patients with AD using a magnetic resonance imaging (MRI)-free tau PET template, since individual high-resolution MRI is costly and not always available in practice. METHODS 18F-florzolotau PET and MRI scans were obtained in a discovery cohort including (1) patients within the AD continuum (n = 87), (2) cognitively impaired patients with non-AD (n = 32), and (3) cognitively unimpaired subjects (n = 26). The validation cohort comprised 24 patients with AD. Following MRI-dependent spatial normalization (standard approach) in randomly selected subjects (n = 40) to cover the entire spectrum of cognitive function, selected PET images were averaged to create the 18F-florzolotau-specific template. Standardized uptake value ratios (SUVRs) were calculated in five predefined regions of interest (ROIs). MRI-free and MRI-dependent methods were compared in terms of continuous and dichotomous agreement, diagnostic performances, and associations with specific cognitive domains. RESULTS MRI-free SUVRs had a high continuous and dichotomous agreement with MRI-dependent measures for all ROIs (intraclass correlation coefficient ≥ 0.980; agreement ≥ 94.5%). Similar findings were observed for AD-related effect sizes, diagnostic performances with respect to categorization across the cognitive spectrum, and associations with cognitive domains. The robustness of the MRI-free approach was confirmed in the validation cohort. CONCLUSIONS The use of an 18F-florzolotau-specific template is a valid alternative to MRI-dependent spatial normalization, improving the clinical generalizability of this second-generation tau tracer. KEY POINTS • Regional 18F-florzolotau SUVRs reflecting tau accumulation in the living brains are reliable biomarkers for the diagnosis, differential diagnosis, and assessment of disease severity in patients with AD. • The 18F-florzolotau-specific template is a valid alternative to MRI-dependent spatial normalization, improving the clinical generalizability of this second-generation tau tracer.
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Affiliation(s)
- Jiaying Lu
- Department of Nuclear Medicine & PET Center & National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zizhao Ju
- Department of Nuclear Medicine & PET Center & National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China
| | - Xun Sun
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chenhao Jia
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Ling Li
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Weiqi Bao
- Department of Nuclear Medicine & PET Center & National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Huiwei Zhang
- Department of Nuclear Medicine & PET Center & National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Fangyang Jiao
- Department of Nuclear Medicine & PET Center & National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Huamei Lin
- Department of Nuclear Medicine & PET Center & National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | | | - Ruixue Cui
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
| | - Xiaoli Lan
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Qianhua Zhao
- Department of Neurology & National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
| | - Yihui Guan
- Department of Nuclear Medicine & PET Center & National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center & National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
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Spatial normalization and quantification approaches of PET imaging for neurological disorders. Eur J Nucl Med Mol Imaging 2022; 49:3809-3829. [PMID: 35624219 DOI: 10.1007/s00259-022-05809-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/19/2022] [Indexed: 12/17/2022]
Abstract
Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for 18F-FDG, 11C-PIB, 18F-Florbetapir, 18F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.
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Unified spatial normalization method of brain PET images using adaptive probabilistic brain atlas. Eur J Nucl Med Mol Imaging 2022; 49:3073-3085. [PMID: 35258689 DOI: 10.1007/s00259-022-05752-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/01/2022] [Indexed: 11/04/2022]
Abstract
PURPOSE A unique advantage of the brain positron emission tomography (PET) imaging is the ability to image different biological processes with different radiotracers. However, the diversity of the brain PET image patterns also makes their spatial normalization challenging. Since structural MR images are not always available in the clinical practice, this study proposed a PET-only spatial normalization method based on adaptive probabilistic brain atlas. METHODS The proposed method (atlas-based method) consists of two parts, an adaptive probabilistic brain atlas generation algorithm, and a probabilistic framework for registering PET image to the generated atlas. To validate this method, the results of MRI-based method and template-based method (a widely used PET-only method) were treated as the gold standard and control, respectively. A total of 286 brain PET images, including seven radiotracers (FDG, PIB, FBB, AV-45, AV-1451, AV-133, [18F]altanserin) and four groups of subjects (Alzheimer disease, Parkinson disease, frontotemporal dementia, and healthy control), were spatially normalized using the three methods. The results were then quantitatively compared by using correlation analysis, meta region of interest (meta-ROI) standardized uptake value ratio (SUVR) analysis, and statistical parametric mapping (SPM) analysis. RESULTS The Pearson correlation coefficient between the images computed by atlas-based method and the gold standard was 0.908 ± 0.005. The relative error of meta-ROI SUVR computed by atlas-based method was 2.12 ± 0.18%. Compared with template-based method, atlas-based method was also more consistent with the gold standard in SPM analysis. CONCLUSION The proposed method provides a unified approach to spatially normalize brain PET images of different radiotracers accurately without MR images. A free MATLAB toolbox for this method has been provided.
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Guo XY, Chang Y, Kim Y, Rhee HY, Cho AR, Park S, Ryu CW, San Lee J, Lee KM, Shin W, Park KC, Kim EJ, Jahng GH. Development and evaluation of a T1 standard brain template for Alzheimer disease. Quant Imaging Med Surg 2021; 11:2224-2244. [PMID: 34079697 DOI: 10.21037/qims-20-710] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) have high variability in brain tissue loss, making it difficult to use a disease-specific standard brain template. The objective of this study was to develop an AD-specific three-dimensional (3D) T1 brain tissue template and to evaluate the characteristics of the populations used to form the template. Methods We obtained 3D T1-weighted images from 294 individuals, including 101 AD, 96 amnestic MCI, and 97 cognitively normal (CN) elderly individuals, and segmented them into different brain tissues to generate AD-specific brain tissue templates. Demographic data and clinical outcome scores were compared between the three groups. Voxel-based analyses and regions-of-interest-based analyses were performed to compare gray matter volume (GMV) and white matter volume (WMV) between the three participant groups and to evaluate the relationship of GMV and WMV loss with age, years of education, and Mini-Mental State Examination (MMSE) scores. Results We created high-resolution AD-specific tissue probability maps (TPMs). In the AD and MCI groups, losses of both GMV and WMV were found with respect to the CN group in the hippocampus (F >44.60, P<0.001). GMV was lower with increasing age in all individuals in the left (r=-0.621, P<0.001) and right (r=-0.632, P<0.001) hippocampi. In the left hippocampus, GMV was positively correlated with years of education in the CN groups (r=0.345, P<0.001) but not in the MCI (r=0.223, P=0.0293) or AD (r=-0.021, P=0.835) groups. WMV of the corpus callosum was not significantly correlated with years of education in any of the three subject groups (r=0.035 and P=0.549 for left, r=0.013 and P=0.821 for right). In all individuals, GMV of the hippocampus was significantly correlated with MMSE scores (left, r=0.710 and P<0.001; right, r=0.680 and P<0.001), while WMV of the corpus callosum showed a weak correlation (left, r=0.142 and P=0.015; right, r=0.123 and P=0.035). Conclusions A 3D, T1 brain tissue template was created using imaging data from CN, MCI, and AD participants considering the participants' age, sex, and years of education. Our disease-specific template can help evaluate brains to promote early diagnosis of MCI individuals and aid treatment of MCI and AD individuals.
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Affiliation(s)
- Xiao-Yi Guo
- Department of Medicine, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Yunjung Chang
- Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, Gyeonggi-do, Republic of Korea
| | - Yehee Kim
- Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, Gyeonggi-do, Republic of Korea
| | - Hak Young Rhee
- Department of Neurology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Ah Rang Cho
- Department of Psychiatry, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Soonchan Park
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Chang-Woo Ryu
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Kyung Mi Lee
- Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Wonchul Shin
- Department of Neurology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Key-Chung Park
- Department of Neurology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Eui Jong Kim
- Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
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Clinical validity of increased cortical binding of tau ligands of the THK family and PBB3 on PET as biomarkers for Alzheimer's disease in the context of a structured 5-phase development framework. Eur J Nucl Med Mol Imaging 2021; 48:2086-2096. [PMID: 33723628 PMCID: PMC8175292 DOI: 10.1007/s00259-021-05277-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/21/2021] [Indexed: 12/28/2022]
Abstract
PURPOSE The research community has focused on defining reliable biomarkers for the early detection of the pathological hallmarks of Alzheimer's disease (AD). In 2017, the Geneva AD Biomarker Roadmap initiative adapted the framework for the systematic validation of oncological biomarkers to AD, with the aim to accelerate their development and implementation in clinical practice. The aim of this work was to assess the validation status of tau PET ligands of the THK family and PBB3 as imaging biomarkers for AD, based on the Biomarker Roadmap methodology. METHODS A panel of experts in AD biomarkers convened in November 2019 at a 2-day workshop in Geneva. The level of clinical validity of tau PET ligands of the THK family and PBB3 was assessed based on the 5-phase development framework before the meeting and discussed during the workshop. RESULTS PET radioligands of the THK family discriminate well between healthy controls and patients with AD dementia (phase 2; partly achieved) and recent evidence suggests an accurate diagnostic accuracy at the mild cognitive impairment (MCI) stage of the disease (phase 3; partly achieved). The phases 2 and 3 were considered not achieved for PBB3 since no evidence exists about the ligand's diagnostic accuracy. Preliminary evidence exists about the secondary aims of each phase for all ligands. CONCLUSION Much work remains for completing the aims of phases 2 and 3 and replicating the available evidence. However, it is unlikely that the validation process for these tracers will be completed, given the presence of off-target binding and the development of second-generation tracers with improved binding and pharmacokinetic properties.
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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] [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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Fang C, Li C, Forouzannezhad P, Cabrerizo M, Curiel RE, Loewenstein D, Duara R, Adjouadi M. Gaussian discriminative component analysis for early detection of Alzheimer’s disease: A supervised dimensionality reduction algorithm. J Neurosci Methods 2020; 344:108856. [DOI: 10.1016/j.jneumeth.2020.108856] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 07/06/2020] [Accepted: 07/07/2020] [Indexed: 01/09/2023]
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