Abstract
We introduce a pipeline for the individual analysis of positron emission tomography (PET) data on large cohorts of patients. This pipeline consists for each individual of generating a subject-specific model of healthy PET appearance and comparing the individual’s PET image to the model via a novel regularised Z-score. The resulting voxel-wise Z-score map can be interpreted as a subject-specific abnormality map that summarises the pathology’s topographical distribution in the brain. We then propose a strategy to validate the abnormality maps on several PET tracers and automatically detect the underlying pathology by using the abnormality maps as features to feed a linear support vector machine (SVM)-based classifier.
We applied the pipeline to a large dataset comprising 298 subjects selected from the ADNI2 database (103 cognitively normal, 105 late MCI and 90 Alzheimer’s disease subjects). The high classification accuracy obtained when using the abnormality maps as features demonstrates that the proposed pipeline is able to extract for each individual the signal characteristic of dementia from both FDG and Florbetapir PET data.
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Notes
- 1.
Imaging data were provided by the Alzheimer’s disease neuroimaging initiative (http://adni.loni.ucla.edu/).
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References
Jagust, W.: Positron emission tomography and magnetic resonance imaging in the diagnosis and prediction of dementia. Alzheimer’s Dement. 2(1), 36–42 (2006)
Klöppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack, C.R., Ashburner, J., Frackowiak, R.S.: Automatic classification of MR scans in Alzheimer’s disease. Brain 131(3), 681–689 (2008)
Illán, I., Górriz, J., Ramírez, J., Salas-Gonzalez, D., López, M., Segovia, F., Chaves, R., Gómez-Rio, M., Puntonet, C.G.: The Alzheimer’s disease neuroimaging initiative: \(^{18}\)F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Inf. Sci. 181(4), 903–916 (2011)
Burgos, N., Jorge Cardoso, M., Mendelson, A.F., Schott, J.M., Atkinson, D., Arridge, S.R., Hutton, B.F., Ourselin, S.: Subject-specific models for the analysis of pathological FDG PET data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 651–658. Springer, Cham (2015). doi:10.1007/978-3-319-24571-3_78
Landau, S.M., Mintun, M.A., Joshi, A.D., Koeppe, R.A., Petersen, R.C., Aisen, P.S., Weiner, M.W., Jagust, W.J.: Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann. Neurol. 72(4), 578–586 (2012)
Modat, M., Cash, D.M., Daga, P., Winston, G.P., Duncan, J.S., Ourselin, S.: A symmetric block-matching framework for global registration. In: Proceedings of SPIE, Medical Imaging, vol. 9034 (2014)
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. Comput. Methods Programs Biomed. 98(3), 278–84 (2010)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Forsberg, A., Engler, H., Almkvist, O., Blomquist, G., Hagman, G., Wall, A., Ringheim, A., Långström, B., Nordberg, A.: PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol. Aging 29(10), 1456–1465 (2008)
Acknowledgements
The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. PCOFUND-GA-2013-609102, through the PRESTIGE programme coordinated by Campus France, and from the programme “Investissements d’avenir” ANR-10-IAIHU-06.
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Burgos, N. et al. (2017). Individual Analysis of Molecular Brain Imaging Data Through Automatic Identification of Abnormality Patterns. In: Cardoso, M., et al. Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment. RAMBO CMMI SWITCH 2017 2017 2017. Lecture Notes in Computer Science(), vol 10555. Springer, Cham. https://doi.org/10.1007/978-3-319-67564-0_2
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