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Functional and dynamic magnetic resonance imaging using vector adaptive weights smoothing

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  • Jörg Polzehl
  • Vladimir G. Spokoiny

Abstract

We consider the problem of statistical inference for functional and dynamic magnetic resonance imaging (MRI). A new approach is proposed which extends the adaptive weights smoothing procedure of Polzehl and Spokoiny that was originally designed for image denoising. We demonstrate how the adaptive weights smoothing method can be applied to time series of images, which typically occur in functional and dynamic MRI. It is shown how signal detection in functional MRI and the analysis of dynamic MRI can benefit from spatially adaptive smoothing. The performance of the procedure is illustrated by using real and simulated data.

Suggested Citation

  • Jörg Polzehl & Vladimir G. Spokoiny, 2001. "Functional and dynamic magnetic resonance imaging using vector adaptive weights smoothing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 485-501.
    • Handle: RePEc:bla:jorssc:v:50:y:2001:i:4:p:485-501
    DOI: 10.1111/1467-9876.00249
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    1. repec:jss:jstsof:19:i01 is not listed on IDEAS
    2. Polzehl, Jörg & Tabelow, Karsten, 2007. "Adaptive Smoothing of Digital Images: The R Package adimpro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i01).
    3. Lei Xu & Timothy D. Johnson & Thomas E. Nichols & Derek E. Nee, 2009. "Modeling Inter-Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model," Biometrics, The International Biometric Society, vol. 65(4), pages 1041-1051, December.

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