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Publication Detail
Markov random field and Gaussian mixture for segmented MRI-based partial volume correction in PET.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Bousse A, Pedemonte S, Thomas BA, Erlandsson K, Ourselin S, Arridge S, Hutton BF
  • Publication date:
  • Pagination:
    6681, 6705
  • Journal:
    Phys Med Biol
  • Volume:
  • Issue:
  • Status:
  • Country:
  • Language:
  • Keywords:
    Alzheimer Disease, Brain, Epilepsy, Fluorodeoxyglucose F18, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Markov Chains, Normal Distribution, Phantoms, Imaging, Positron-Emission Tomography, Reproducibility of Results
In this paper we propose a segmented magnetic resonance imaging (MRI) prior-based maximum penalized likelihood deconvolution technique for positron emission tomography (PET) images. The model assumes the existence of activity classes that behave like a hidden Markov random field (MRF) driven by the segmented MRI. We utilize a mean field approximation to compute the likelihood of the MRF. We tested our method on both simulated and clinical data (brain PET) and compared our results with PET images corrected with the re-blurred Van Cittert (VC) algorithm, the simplified Guven (SG) algorithm and the region-based voxel-wise (RBV) technique. We demonstrated our algorithm outperforms the VC algorithm and outperforms SG and RBV corrections when the segmented MRI is inconsistent (e.g. mis-segmentation, lesions, etc) with the PET image.
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