@inproceedings{1649800f322e4eca80d495b3bafcc93e,
title = "Probabilistic graphical model of SPECT/MRI",
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.",
keywords = "Bayesian Networks, Emission Tomography, Molecular Imaging, Multi-modality",
author = "Stefano Pedemonte and Alexandre Bousse and Hutton, {Brian F.} and Simon Arridge and Sebastien Ourselin",
year = "2011",
doi = "10.1007/978-3-642-24319-6_21",
language = "English",
isbn = "9783642243189",
volume = "7009 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "167--174",
booktitle = "Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings",
note = "2nd 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 ; Conference date: 18-09-2011 Through 18-09-2011",
}