Abstract
Quantification of myocardial T1 and extra cellular volume (ECV) in cardiac MRI provides relevant diagnostic information about myocardial structure. However, since these maps are pixel-wise calculated from two sequences of T1-weighted images, they are frequently disturbed by motion artifacts originating from e.g. patient motion, failure in breath-hold or cardiac motion. We propose a new non-rigid registration framework combining a robust data-driven initialization with a model-based refinement. The data-driven algorithm finds an optimal registration sequence of images for calculation of an initial T1 map. The registration is subsequently refined by exploiting the exponential relaxation model of T1. Validation using 20 in-vivo data sets showed a decrease in mean boundary error and an increase in global Dice coefficient.
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Tilborghs, S. et al. (2017). Robust Model-Based Registration of Cardiac MR Images for T1 and ECV Mapping. In: Pop, M., Wright, G. (eds) Functional Imaging and Modelling of the Heart. FIMH 2017. Lecture Notes in Computer Science(), vol 10263. Springer, Cham. https://doi.org/10.1007/978-3-319-59448-4_5
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DOI: https://doi.org/10.1007/978-3-319-59448-4_5
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