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Tagmazian AA, Schwarz C, Lange C, Pitkänen E, Vuoksimaa E. ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images. Eur J Neurosci 2024; 59:3030-3044. [PMID: 38576196 DOI: 10.1111/ejn.16332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Detection and measurement of amyloid-beta (Aβ) in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted Aβ CSF values correlated with measured Aβ CSF values (r = 0.92; q < 0.01), SUVR (rAV45 = -0.64, rFBB = -0.69; q < 0.01) and episodic memory measures (0.33 < r < 0.44; q < 0.01). For both classifications, cerebral white matter significantly contributed to CSF prediction (q < 0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, the brain stem, subcortical areas, cortical lobes, limbic lobe and basal forebrain made more significant contributions (q < 0.01). Considering cortical grey matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination.
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Affiliation(s)
- Arina A Tagmazian
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Claudia Schwarz
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Esa Pitkänen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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Chen K, Ghisays V, Luo J, Chen Y, Lee W, Wu T, Reiman EM, Su Y. Harmonizing florbetapir and PiB PET measurements of cortical Aβ plaque burden using multiple regions-of-interest and machine learning techniques: An alternative to the Centiloid approach. Alzheimers Dement 2024; 20:2165-2172. [PMID: 38276892 PMCID: PMC10984485 DOI: 10.1002/alz.13677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/30/2023] [Accepted: 12/11/2023] [Indexed: 01/27/2024]
Abstract
INTRODUCTION Machine learning (ML) can optimize amyloid (Aβ) comparability among positron emission tomography (PET) radiotracers. Using multi-regional florbetapir (FBP) measures and ML, we report better Pittsburgh compound-B (PiB)/FBP harmonization of mean-cortical Aβ (mcAβ) than Centiloid. METHODS PiB-FBP pairs from 92 subjects in www.oasis-brains.org and 46 in www.gaain.org/centiloid-project were used as the training/testing sets. FreeSurfer-extracted FBP multi-regional Aβ and actual PiB mcAβ in the training set were used to train ML models generating synthetic PiB mcAβ. The correlation coefficient (R) between the synthetic/actual PiB mcAβ in the testing set was assessed. RESULTS In the testing set, the synthetic/actual PiB mcAβ correlation R = 0.985 (R2 = 0.970) using artificial neural network was significantly higher (p ≤ 6.6e-4) than the FBP/PiB correlation R = 0.927 (R2 = 0.860), improving total variance percentage (R2 ) from 86% to 97%. Other ML models such as partial least square, ensemble, and relevance vector regressions also improved R (p = 9.677e-05 /0.045/0.0017). DISCUSSION ML improved mcAβ comparability. Additional studies are needed for the generalizability to other amyloid tracers, and to tau PET. Highlights Centiloid is a calibration of the amyloid scale, not harmonization. Centiloid unifies the amyloid scale without improving inter-tracer association (R2 ). Machine learning (ML) can harmonize the amyloid scale by improving R2 . ML harmonization maps multi-regional florbetapir SUVRs to PiB mean-cortical SUVR. Artificial neural network ML increases Centiloid R2 from 86% to 97%.
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Affiliation(s)
- Kewei Chen
- Banner Alzheimer's InstitutePhoenixArizonaUSA
- Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
- School of Mathematics and Statistical SciencesCollege of Health SolutionsArizona State UniversityTempeArizonaUSA
- Department of Neurology College of Medicine‐PhoenixUniversity of ArizonaPhoenixArizonaUSA
| | - Valentina Ghisays
- Banner Alzheimer's InstitutePhoenixArizonaUSA
- Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
| | - Ji Luo
- Banner Alzheimer's InstitutePhoenixArizonaUSA
- Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
| | - Yinghua Chen
- Banner Alzheimer's InstitutePhoenixArizonaUSA
- Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
| | - Wendy Lee
- Banner Alzheimer's InstitutePhoenixArizonaUSA
- Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
| | - Teresa Wu
- ASU‐Mayo Center for Innovative ImagingArizona State UniversityTempeArizonaUSA
- School of Computing and Augmented IntelligenceArizona State UniversityTempeArizonaUSA
| | - Eric M. Reiman
- Banner Alzheimer's InstitutePhoenixArizonaUSA
- Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
- ASU‐Banner Neurodegenerative Disease Research CenterArizona State UniversityTempeArizonaUSA
- Department of PsychiatryUniversity of ArizonaPhoenixArizonaUSA
| | - Yi Su
- Banner Alzheimer's InstitutePhoenixArizonaUSA
- Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
- Department of Neurology College of Medicine‐PhoenixUniversity of ArizonaPhoenixArizonaUSA
- ASU‐Mayo Center for Innovative ImagingArizona State UniversityTempeArizonaUSA
- School of Computing and Augmented IntelligenceArizona State UniversityTempeArizonaUSA
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Vega F, Addeh A, Ganesh A, Smith EE, MacDonald ME. Image Translation for Estimating Two-Dimensional Axial Amyloid-Beta PET From Structural MRI. J Magn Reson Imaging 2024; 59:1021-1031. [PMID: 37921361 DOI: 10.1002/jmri.29070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 09/29/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND Amyloid-beta and brain atrophy are hallmarks for Alzheimer's Disease that can be targeted with positron emission tomography (PET) and MRI, respectively. MRI is cheaper, less-invasive, and more available than PET. There is a known relationship between amyloid-beta and brain atrophy, meaning PET images could be inferred from MRI. PURPOSE To build an image translation model using a Conditional Generative Adversarial Network able to synthesize Amyloid-beta PET images from structural MRI. STUDY TYPE Retrospective. POPULATION Eight hundred eighty-two adults (348 males/534 females) with different stages of cognitive decline (control, mild cognitive impairment, moderate cognitive impairment, and severe cognitive impairment). Five hundred fifty-two subjects for model training and 331 for testing (80%:20%). FIELD STRENGTH/SEQUENCE 3 T, T1-weighted structural (T1w). ASSESSMENT The testing cohort was used to evaluate the performance of the model using the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR), comparing the likeness of the overall synthetic PET images created from structural MRI with the overall true PET images. SSIM was computed in the overall image to include the luminance, contrast, and structural similarity components. Experienced observers reviewed the images for quality, performance and tried to determine if they could tell the difference between real and synthetic images. STATISTICAL TESTS Pixel wise Pearson correlation was significant, and had an R2 greater than 0.96 in example images. From blinded readings, a Pearson Chi-squared test showed that there was no significant difference between the real and synthetic images by the observers (P = 0.68). RESULTS A high degree of likeness across the evaluation set, which had a mean SSIM = 0.905 and PSNR = 2.685. The two observers were not able to determine the difference between the real and synthetic images, with accuracies of 54% and 46%, respectively. CONCLUSION Amyloid-beta PET images can be synthesized from structural MRI with a high degree of similarity to the real PET images. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Fernando Vega
- Department of Biomedical, University of Calgary, Calgary, Alberta, Canada
- Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Abdoljalil Addeh
- Department of Biomedical, University of Calgary, Calgary, Alberta, Canada
- Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Aravind Ganesh
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Eric E Smith
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - M Ethan MacDonald
- Department of Biomedical, University of Calgary, Calgary, Alberta, Canada
- Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
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Tian M, Zuo C, Civelek AC, Carrio I, Watanabe Y, Kang KW, Murakami K, Garibotto V, Prior JO, Barthel H, Guan Y, Lu J, Zhou R, Jin C, Wu S, Zhang X, Zhong Y, Zhang H. International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer's Disease. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:375-389. [PMID: 37589025 PMCID: PMC10425321 DOI: 10.1007/s43657-022-00068-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 07/19/2022] [Accepted: 07/22/2022] [Indexed: 08/18/2023]
Abstract
Alzheimer's disease (AD) is the main cause of dementia, with its diagnosis and management remaining challenging. Amyloid positron emission tomography (PET) has become increasingly important in medical practice for patients with AD. To integrate and update previous guidelines in the field, a task group of experts of several disciplines from multiple countries was assembled, and they revised and approved the content related to the application of amyloid PET in the medical settings of cognitively impaired individuals, focusing on clinical scenarios, patient preparation, administered activities, as well as image acquisition, processing, interpretation and reporting. In addition, expert opinions, practices, and protocols of prominent research institutions performing research on amyloid PET of dementia are integrated. With the increasing availability of amyloid PET imaging, a complete and standard pipeline for the entire examination process is essential for clinical practice. This international consensus and practice guideline will help to promote proper clinical use of amyloid PET imaging in patients with AD.
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Affiliation(s)
- Mei Tian
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
- Human Phenome Institute, Fudan University, Shanghai, 201203 China
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
- National Center for Neurological Disorders and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Ali Cahid Civelek
- Department of Radiology and Radiological Science, Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins Medicine, Baltimore, 21287 USA
| | - Ignasi Carrio
- Department of Nuclear Medicine, Hospital Sant Pau, Autonomous University of Barcelona, Barcelona, 08025 Spain
| | - Yasuyoshi Watanabe
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047 Japan
| | - Keon Wook Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080 Korea
| | - Koji Murakami
- Department of Radiology, Juntendo University Hospital, Tokyo, 113-8431 Japan
| | - Valentina Garibotto
- Diagnostic Department, University Hospitals of Geneva and NIMTlab, University of Geneva, Geneva, 1205 Switzerland
| | - John O. Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, 1011 Switzerland
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University Medical Center, Leipzig, 04103 Germany
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
| | - Jiaying Lu
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Chentao Jin
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Shuang Wu
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Xiaohui Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Yan Zhong
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009 China
- The College of Biomedical Engineering and Instrument Science of Zhejiang University, Hangzhou, 310007 China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310007 China
| | - Molecular Imaging-Based Precision Medicine Task Group of A3 (China-Japan-Korea) Foresight Program
- PET Center, Huashan Hospital, Fudan University, Shanghai, 200235 China
- Human Phenome Institute, Fudan University, Shanghai, 201203 China
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 China
- National Center for Neurological Disorders and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Department of Radiology and Radiological Science, Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins Medicine, Baltimore, 21287 USA
- Department of Nuclear Medicine, Hospital Sant Pau, Autonomous University of Barcelona, Barcelona, 08025 Spain
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047 Japan
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080 Korea
- Department of Radiology, Juntendo University Hospital, Tokyo, 113-8431 Japan
- Diagnostic Department, University Hospitals of Geneva and NIMTlab, University of Geneva, Geneva, 1205 Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, 1011 Switzerland
- Department of Nuclear Medicine, Leipzig University Medical Center, Leipzig, 04103 Germany
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009 China
- The College of Biomedical Engineering and Instrument Science of Zhejiang University, Hangzhou, 310007 China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310007 China
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Bilgel M. Probabilistic estimation for across-batch compatibility enhancement for amyloid PET. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12436. [PMID: 37424963 PMCID: PMC10323321 DOI: 10.1002/dad2.12436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/20/2023] [Accepted: 04/10/2023] [Indexed: 07/11/2023]
Abstract
INTRODUCTION It is necessary to accurately account for systematic differences due to variability in scanners, radiotracers, and acquisition protocols in multisite studies combining amyloid imaging data. METHODS We propose Probabilistic Estimation for Across-batch Compatibility Enhancement (PEACE), a fully Bayesian multimodal extension of the widely used ComBat harmonization model, and we apply it to harmonize regional amyloid positron emission tomography data from two scanners. RESULTS Simulations show that PEACE recovers true harmonized values better than ComBat, even for unimodal data. PEACE harmonization of multiscanner regional amyloid imaging data yields results that agree better with longitudinal data compared to ComBat, without removing the known biological effects of age or apolipoprotein E genotype. DISCUSSION PEACE outperforms ComBat in both unimodal and bimodal contexts, is applicable to multisite amyloid imaging data, and holds promise for the harmonization of other neuroimaging data over ComBat. HIGHLIGHTS We introduce PEACE, a fully Bayesian multimodal extension of ComBat harmonization.Simulations show that PEACE recovers true harmonized values better than ComBat.PEACE accurately harmonizes multiscanner regional amyloid imaging data.
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Affiliation(s)
- Murat Bilgel
- Laboratory of Behavioral NeuroscienceNational Institute on AgingBaltimoreMarylandUSA
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Shah J, Siddiquee MMR, Krell-Roesch J, Syrjanen JA, Kremers WK, Vassilaki M, Forzani E, Wu T, Geda YE. Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective. J Alzheimers Dis 2023; 92:1131-1146. [PMID: 36872783 PMCID: PMC11102734 DOI: 10.3233/jad-221261] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
There is a growing interest in the application of machine learning (ML) in Alzheimer's disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.
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Affiliation(s)
- Jay Shah
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- ASU-Mayo Center for Innovative Imaging, Tempe, AZ, USA
| | - Md Mahfuzur Rahman Siddiquee
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- ASU-Mayo Center for Innovative Imaging, Tempe, AZ, USA
| | - Janina Krell-Roesch
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jeremy A. Syrjanen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Walter K. Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Erica Forzani
- Biodesign Institute, Arizona State University, Tempe, AZ, USA
| | - Teresa Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- ASU-Mayo Center for Innovative Imaging, Tempe, AZ, USA
| | - Yonas E. Geda
- Department of Neurology and the Franke Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA
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Bourgeat P, Doré V, Burnham SC, Benzinger T, Tosun D, Li S, Goyal M, LaMontagne P, Jin L, Rowe CC, Weiner MW, Morris JC, Masters CL, Fripp J, Villemagne VL. β-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3. Neuroimage 2022; 262:119527. [PMID: 35917917 PMCID: PMC9550562 DOI: 10.1016/j.neuroimage.2022.119527] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/11/2022] [Accepted: 07/28/2022] [Indexed: 10/31/2022] Open
Abstract
INTRODUCTION The Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies. METHODS All Aβ PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in 11C-PiB-18F-Florbetapir pairs and longitudinal consistency were evaluated. RESULTS The smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for 18F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used. CONCLUSIONS FWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.
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Affiliation(s)
| | - Vincent Doré
- CSIRO Health and Biosecurity, Brisbane, Australia; Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia
| | | | | | - Duygu Tosun
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA,; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Shenpeng Li
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - Manu Goyal
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA
| | - Liang Jin
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
| | - Michael W Weiner
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA,; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - John C Morris
- Washington University in St. Louis, St. Louis, MO, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; Department of Psychiatry, The University of Pittsburgh, Pittsburgh, PA, USA
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