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Open Access Green as soon as Postprint is submitted to ZB.
An MRF-based discrete optimization framework for combined DCE-MRI motion correction and pharmacokinetic parameter estimation.
In: (Bayesian and grAphical Models for Biomedical Imaging). Berlin [u.a.]: Springer, 2014. 73-84 (Lect. Notes Comput. Sc. ; 8677)
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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Publication type
Article: Conference contribution
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
Bayesian and grAphical Models for Biomedical Imaging
Quellenangaben
Volume: 8677,
Pages: 73-84
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)