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.
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Pedemonte, S., Bousse, A., Hutton, B.F., Arridge, S., Ourselin, S. (2011). Probabilistic Graphical Model of SPECT/MRI. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_21
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DOI: https://doi.org/10.1007/978-3-642-24319-6_21
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