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http://dx.doi.org/10.9718/JBER.2018.39.6.237

Region of Interest Analysis for Standardized Uptake Value Ratio of 18F-fludeoxyglucose PET: Mild Cognitive Impairment and Alzheimer's Disease  

Kim, Seonjik (Department of Biomedical Engineering, Daegu Catholic University)
Yoon, Uicheul (Department of Biomedical Engineering, Daegu Catholic University)
Publication Information
Journal of Biomedical Engineering Research / v.39, no.6, 2018 , pp. 237-242 More about this Journal
Abstract
$^{18}F$-fludeoxyglucose PET (FDG-PET) can help finding an abnormal metabolic activity in brain. In this study, we evaluated an efficiency of volume- and cortical surface-based analysis which were used to determine whether standardized uptake value ratio (SUVR) of FDG-PET was different among Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy control (HC). Each PET image was rigidly co-registered to the corresponding magnetic resonance imaging (MRI) using mutual information. All voxels of the co-registered PET images were divided by the mean FDG uptake of the cerebellum cortex which was thresholded by partial volume effect (>0.9). Also, the SUVR value of each vertex was linearly interpolated from volumetric SUVR image which was thresholded by gray matter partial volume effect (>0.1). Lobar mean values were calculated from both volume- and cortical surface-based SUVRs. Statistical analysis was conducted to compare two measures for AD, MCI and HC groups. Even though the results of volume (SUVR_vol) and cortical surface-based SUVR (SUVR_surf) analysis were not significantly different from each other, the latter would be better for detecting group differences in SUVR of PET.
Keywords
Standardized uptake value ratio (SUVR); Volume-based SUVR (SUVR_vol); Cortical surface-based SUVR (SUVR_surf); $^{18}F$-fludeoxyglucose (FDG); Positron emission tomography (PET); Magnetic resonance imaging (MRI);
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1 C. Marcus, E. Mena, and R. M. Subramaniam, "Brain PET in the diagnosis of Alzheimer's disease," Clinical nuclear medicine, vol. 39, pp. e413, 2014.   DOI
2 V. J. Lowe, B. J. Kemp, C. R. Jack, M. Senjem, S. Weigand, M. Shiung, et al., "Comparison of 18F-FDG and PiB PET in cognitive impairment," Journal of Nuclear Medicine, vol. 50, pp. 878-886, 2009.   DOI
3 N. Smailagic, M. Vacante, C. Hyde, S. Martin, O. Ukoumunne, and C. Sachpekidis, "18F-FDG PET for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)," The Cochrane Library, 2015.
4 H. Barthel, H.-J. Gertz, S. Dresel, O. Peters, P. Bartenstein, K. Buerger, et al., "Cerebral amyloid-${\beta}$ PET with florbetaben (18 F) in patients with Alzheimer's disease and healthy controls: a multicentre phase 2 diagnostic study," The Lancet Neurology, vol. 10, pp. 424-435, 2011.   DOI
5 C. R. Jack, H. J. Wiste, T. G. Lesnick, S. D. Weigand, D. S. Knopman, P. Vemuri, et al., "Brain ${\beta}$-amyloid load approaches a plateau," Neurology, vol. 80, pp. 890-896, 2013.   DOI
6 H.-J. Park, J. D. Lee, J. W. Chun, J. H. Seok, M. Yun, M.-K. Oh, et al., "Cortical surface-based analysis of 18F-FDG PET: measured metabolic abnormalities in schizophrenia are affected by cortical structural abnormalities," Neuroimage, vol. 31, pp. 1434-1444, 2006.   DOI
7 B. S. Ye, S. W. Seo, C. H. Kim, S. Jeon, G. H. Kim, Y. Noh, et al., "Hippocampal and cortical atrophy in amyloid-negative mild cognitive impairments: comparison with amyloid-positive mild cognitive impairment," Neurobiology of aging, vol. 35, pp. 291-300, 2014.   DOI
8 A. D. Joshi, M. J. Pontecorvo, C. M. Clark, A. P. Carpenter, D. L. Jennings, C. H. Sadowsky, et al., "Performance characteristics of amyloid PET with florbetapir F 18 in patients with Alzheimer's disease and cognitively normal subjects," Journal of Nuclear Medicine, vol. 53, pp. 378-384, 2012.   DOI
9 K. E. Pike, G. Savage, V. L. Villemagne, S. Ng, S. A. Moss, P. Maruff, et al., "${\beta}$-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer's disease," Brain, vol. 130, pp. 2837-2844, 2007.   DOI
10 J. Tohka, A. Zijdenbos, and A. Evans, "Fast and robust parameter estimation for statistical partial volume models in brain MRI," Neuroimage, vol. 23, pp. 84-97, 2004.   DOI
11 J. G. Sled, A. P. Zijdenbos, and A. C. Evans, "A nonparametric method for automatic correction of intensity nonuniformity in MRI data," IEEE transactions on medical imaging, vol. 17, pp. 87-97, 1998.   DOI
12 A. P. Zijdenbos, R. Forghani, and A. C. Evans, "Automatic" pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis," IEEE transactions on medical imaging, vol. 21, pp. 1280-1291, 2002.   DOI
13 S. M. Smith, "Fast robust automated brain extraction," Human brain mapping, vol. 17, pp. 143-155, 2002.   DOI
14 R. Ossenkoppele, N. Tolboom, J. C. Foster-Dingley, S. F. Adriaanse, R. Boellaard, M. Yaqub, et al., "Longitudinal imaging of Alzheimer pathology using [11C] PIB,[18F] FDDNP and [18F] FDG PET," European journal of nuclear medicine and molecular imaging, vol. 39, pp. 990-1000, 2012.   DOI
15 D. L. Collins, P. Neelin, T. M. Peters, and A. C. Evans, "Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space," Journal of computer assisted tomography, vol. 18, pp. 192-205, 1994.   DOI
16 J. S. Kim, V. Singh, J. K. Lee, J. Lerch, Y. Ad-Dab'bagh, D. MacDonald, et al., "Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification," Neuroimage, vol. 27, pp. 210-221, 2005.   DOI
17 O. Lyttelton, M. Boucher, S. Robbins, and A. Evans, "An unbiased iterative group registration template for cortical surface analysis," Neuroimage, vol. 34, pp. 1535-1544, 2007.   DOI
18 K. Im, J.-M. Lee, O. Lyttelton, S. H. Kim, A. C. Evans, and S. I. Kim, "Brain size and cortical structure in the adult human brain," Cerebral Cortex, vol. 18, pp. 2181-2191, 2008.   DOI
19 U. Yoon, J.-M. Lee, K. Im, Y.-W. Shin, B. H. Cho, I. Y. Kim, et al., "Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia," Neuroimage, vol. 34, pp. 1405-1415, 2007.   DOI
20 S. Minoshima, N. L. Foster, A. A. Sima, K. A. Frey, R. L. Albin, and D. E. Kuhl, "Alzheimer's disease versus dementia with Lewy bodies: cerebral metabolic distinction with autopsy confirmation," Annals of neurology, vol. 50, pp. 358-365, 2001.   DOI
21 L. Mosconi and D. H. Silverman, "FDG PET in the evaluation of mild cognitive impairment and early dementia," in PET in the Evaluation of Alzheimer's Disease and Related Disorders, ed: Springer, 2009, pp. 49-65.
22 K. Chen, J. B. Langbaum, A. S. Fleisher, N. Ayutyanont, C. Reschke, W. Lee, et al., "Twelve-month metabolic declines in probable Alzheimer's disease and amnestic mild cognitive impairment assessed using an empirically pre-defined statistical region-of-interest: findings from the Alzheimer's Disease Neuroimaging Initiative," Neuroimage, vol. 51, pp. 654-664, 2010.   DOI
23 S. Morbelli, A. Piccardo, G. Villavecchia, B. Dessi, A. Brugnolo, A. Piccini, et al., "Mapping brain morphological and functional conversion patterns in amnestic MCI: a voxel-based MRI and FDG-PET study," European journal of nuclear medicine and molecular imaging, vol. 37, pp. 36, 2010.   DOI
24 D. Riviere, J.-F. Mangin, D. Papadopoulos-Orfanos, J.-M. Martinez, V. Frouin, and J. Regis, "Automatic recognition of cortical sulci of the human brain using a congregation of neural networks," Medical image analysis, vol. 6, pp. 77-92, 2002.   DOI
25 J. C. Patterson, D. L. Lilien, A. Takalkar, and J. B. Pinkston, "Early detection of brain pathology suggestive of early AD using objective evaluation of FDG-PET scans," International Journal of Alzheimer's Disease, vol. 2011, 2011.
26 A. Tucholka, V. Fritsch, J.-B. Poline, and B. Thirion, "An empirical comparison of surface-based and volume-based group studies in neuroimaging," Neuroimage, vol. 63, pp. 1443-1453, 2012.   DOI