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Abstract #3046

A fuzzy Markov random field approach for the unsupervised segmentation of hyperpolarized 13C MRI data

Charlie J Daniels1,2 and Ferdia A Gallagher1,3

1Department of Radiology, University of Cambridge, Cambridge, United Kingdom, 2Integrated Mathematical Oncology, Moffitt Cancer Centre, Tampa, FL, United States, 3Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom

MRI with hyperpolarized 13C-labelled compounds is an emerging clinical technique allowing in vivo metabolic processes to be characterized non-invasively. Accurate quantification of metabolism requires a region-of-interest to be defined, which is usually based on spatial information only. However, as the hyperpolarized data is 5-dimensional (spatial, temporal and spectral), it offers the possibility of applying novel segmentation methods to more accurately define this region-of-interest. A novel solution to the problem of 13C image segmentation is proposed here, using a hybrid Markov random field model with fuzzy logic. Performance of the algorithm is demonstrated using in silico and in vivo data.

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