Abstract
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.
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Keywords
- Validation Dataset
- Semantic Dementia
- Cerebral Glucose Metabolism
- Posterior Cortical Atrophy
- Control Dataset
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Herholz, K.: PET studies in dementia. Annals of Nuclear Medicine 17(2) (2003)
Nestor, P.J., Graham, N.L., Fryer, T.D., Williams, G.B., Patterson, K., Hodges, J.R.: Progressive non-fluent aphasia is associated with hypometabolism centred on the left anterior insula. Brain 126(11), 2406–2418 (2003)
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.21451
Crutch, S.J., Lehmann, M., Schott, J.M., Rabinovici, G.D., Rossor, M.N., Fox, N.C.: Posterior cortical atrophy. The Lancet Neurology 11(2), 170 (2012)
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)
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)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data.. IEEE Transactions on Medical Imaging 17(1), 87–97 (1998)
Cardoso, M., Wolz, R., Modat, M., Fox, N.C., Rueckert, D., Ourselin, S.: Geodesic Information Flows. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 262–270. Springer, Heidelberg (2012)
Minoshima, S., Frey, K.A., Foster, N.L., Kuhl, D.E.: Preserved Pontine Glucose Metabolism in Alzheimer Disease A Reference Region for Functional Brain Image (PET) Analysis. Journal of Computer Assisted Tomography 19(4), 541–547 (1995)
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)
Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Computer Methods and Programs in Biomedicine 98(3), 278–284 (2010)
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)
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Burgos, N. et al. (2015). Subject-specific Models for the Analysis of Pathological FDG PET Data. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_78
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DOI: https://doi.org/10.1007/978-3-319-24571-3_78
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