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An MRF-Based Discrete Optimization Framework for Combined DCE-MRI Motion Correction and Pharmacokinetic Parameter Estimation

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Bayesian and grAphical Models for Biomedical Imaging

Abstract

Dynamic contrast-enhanced MRI (DCE-MRI) images are increasingly used for assessing cancer treatment outcome. These time sequences are typically affected by motion, which causes significant errors in tracer kinetic model analysis. Current intra-sequence registration methods for contrast enhanced data either assume restricted transformations (e.g. translation) or employ continuous optimization, which is prone to local optima. In this work, we propose a new approach to DCE-MRI intra-sequence registration and pharmacokinetic modelling, which is formulated in an MRF optimization framework. The complete 4D graph corresponding to a DCE-MRI sequence is reduced to a concatenation of minimum spanning trees, which can be optimized more efficiently. To address the changes due to contrast, a data cost function which incorporates pharmacokinetic modelling information is formulated. The advantages of this method are demonstrated on 8 DCE-MRI image sequences of patients with advanced rectal tumours, presenting mild to severe motion.

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Enescu, M., Heinrich, M.P., Hill, E., Sharma, R., Chappell, M.A., Schnabel, J.A. (2014). An MRF-Based Discrete Optimization Framework for Combined DCE-MRI Motion Correction and Pharmacokinetic Parameter Estimation. In: Cardoso, M.J., Simpson, I., Arbel, T., Precup, D., Ribbens, A. (eds) Bayesian and grAphical Models for Biomedical Imaging. Lecture Notes in Computer Science, vol 8677. Springer, Cham. https://doi.org/10.1007/978-3-319-12289-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-12289-2_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12288-5

  • Online ISBN: 978-3-319-12289-2

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