International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 pp 651-658 | Cite as

Subject-specific Models for the Analysis of Pathological FDG PET Data

  • Ninon Burgos
  • M. Jorge Cardoso
  • Alex F. Mendelson
  • Jonathan M. Schott
  • David Atkinson
  • Simon R. Arridge
  • Brian F. Hutton
  • Sébastien Ourselin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9350)

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Herholz, K.: PET studies in dementia. Annals of Nuclear Medicine 17(2) (2003)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 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
  4. 4.
    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)CrossRefGoogle Scholar
  5. 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. 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
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 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
  11. 11.
    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)CrossRefGoogle Scholar
  12. 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

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ninon Burgos
    • 1
  • M. Jorge Cardoso
    • 1
    • 2
  • Alex F. Mendelson
    • 1
  • Jonathan M. Schott
    • 2
  • David Atkinson
    • 3
  • Simon R. Arridge
    • 4
  • Brian F. Hutton
    • 5
    • 6
  • Sébastien Ourselin
    • 1
    • 2
  1. 1.Translational Imaging Group, CMICUniversity College LondonLondonUK
  2. 2.Dementia Research CentreUniversity College LondonLondonUK
  3. 3.Centre for Medical ImagingUniversity College LondonLondonUK
  4. 4.Centre for Medical Image ComputingUniversity College LondonLondonUK
  5. 5.Institute of Nuclear MedicineUniversity College LondonLondonUK
  6. 6.Centre for Medical Radiation PhysicsUniversity of WollongongWollongongAustralia

Personalised recommendations