Subject-specific Models for the Analysis of Pathological FDG PET Data
Abnormalities in cerebral glucose metabolism detectable on fluorodeoxyglucose positron emission tomography (FDG PET) can be assessed on a regional or voxel-wise basis. In regional analysis, the average relative uptake over a region of interest is compared with the average relative uptake obtained for normal controls. Prior knowledge is required to determine the regions where abnormal uptake is expected, which can limit its usability. On the other hand, voxel-wise analysis consists of comparing the metabolic activity of the patient to the normal controls voxel-by-voxel, usually in a groupwise space. Voxel-based techniques are limited by the inter-subject morphological and metabolic variability in the normal population, which can limit their sensitivity.
In this paper, we combine the advantages of both regional and voxel-wise approaches through the use of subject-specific PET models for glucose metabolism. By accounting for inter-subject morphological differences, the proposed method aims to remove confounding variation and increase the sensitivity of group-wise approaches. The method was applied to a dataset of 22 individuals: 17 presenting four distinct neurodegenerative syndromes, and 5 controls. The proposed method more accurately distinguishes subgroups in this set, and improves the delineation of disease-specific metabolic patterns.
KeywordsValidation Dataset Semantic Dementia Cerebral Glucose Metabolism Posterior Cortical Atrophy Control Dataset
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- 1.Herholz, K.: PET studies in dementia. Annals of Nuclear Medicine 17(2) (2003)Google Scholar
- 3.Rabinovici, G.D., Jagust, W.J., Furst, A.J., Ogar, J.M., Racine, C.A., Mormino, E.C., O’Neil, J.P., Lal, R.A., Dronkers, N.F., Miller, B.L., Gorno-Tempini, M.L.: Ab Amyloid and Glucose Metabolism in Three Variants of Primary Progressive Aphasia. Annals of Neurology 64(4), 388–401 (2008), doi:10.1002/ana.21451CrossRefGoogle Scholar
- 5.Signorini, M., Paulesu, E., Friston, K., Perani, D., Colleluori, A., Lucignani, G., Grassi, F., Bettinardi, V., Frackowiak, R.S.J., Fazio, F.: Rapid Assessment of Regional Cerebral Metabolic Abnormalities in Single Subjects with Quantitative and Nonquantitative 18 FFDG PET: A Clinical Validation of Statistical Parametric Mapping. Neuroimage 9(1), 63–80 (1999)CrossRefGoogle Scholar
- 6.Drzezga, A., Grimmer, T., Riemenschneider, M., Lautenschlager, N., Siebner, H., Alexopoulus, P., Minoshima, S., Schwaiger, M., Kurz, A.: Prediction of Individual Clinical Outcome in MCI by Means of Genetic Assessment and 18 F-FDG PET. Journal of Nuclear Medicine 46(10), 1625–1632 (2005)Google Scholar
- 10.Rohlfing, T., Brandt, R., Maurer, Jr., C.R., Menzel, R.: Bee brains, B-splines and computational democracy: generating an average shape atlas. In: Proc. IEEE Workshop Mathematical Methods in Biomedical Image Analysis, pp. 187–194 (2001)Google Scholar
- 12.Burgos, N., Cardoso, M.J., Thielemans, K., Modat, M., Pedemonte, S., Dickson, J., Barnes, A., Ahmed, R., Mahoney, C.J., Schott, J.M., Duncan, J.S., Atkinson, D., Arridge, S.R., Hutton, B.F., Ourselin, S.: Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies. IEEE Transactions on Medical Imaging 33(12), 2332–2341 (2014)CrossRefGoogle Scholar