Probabilistic graphical model of SPECT/MRI

Stefano Pedemonte*, Alexandre Bousse, Brian F. Hutton, Simon Arridge, Sebastien Ourselin

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

    2 Citations (Scopus)

    Abstract

    The combination of PET and SPECT with MRI is an area of active research at present time and will enable new biological and pathological analysis tools for clinical applications and pre-clinical research. Image processing and reconstruction in multi-modal PET/MRI and SPECT/MRI poses new algorithmic and computational challenges. We investigate the use of Probabilistic Graphical Models (PGM) to construct a system model and to factorize the complex joint distribution that arises from the combination of the two imaging systems. A joint generative system model based on finite mixtures is proposed and the structural properties of the associated PGM are addressed in order to obtain an iterative algorithm for estimation of activity and multi-modal segmentation. In a SPECT/MRI digital phantom study, the proposed algorithm outperforms a well established method for multi-modal activity estimation in terms of bias/variance characteristics and identification of lesions.

    Original languageEnglish
    Title of host publicationMachine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
    Pages167-174
    Number of pages8
    Volume7009 LNCS
    DOIs
    Publication statusPublished - 2011
    Event2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
    Duration: 18 Sep 201118 Sep 2011

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7009 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
    CountryCanada
    CityToronto, ON
    Period18/09/1118/09/11

    Keywords

    • Bayesian Networks
    • Emission Tomography
    • Molecular Imaging
    • Multi-modality

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