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Multilevel Non-parametric Groupwise Registration in Cardiac MRI: Application to Explanted Porcine Hearts

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Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges (STACOM 2017)


Statistical atlases of myocardial fiber directions have great utility in modelling applications. The first step in building atlases requires a registration of the hearts to a template. In this paper, we performed groupwise registration on a small database of explanted pig hearts (\(N=4\)) and coupled it with a multilevel pairwise registration framework in order to generate an average cardiac geometry. The scheme implemented in our experiments effectively registers and normalizes the hearts despite a high variability in cardiac measurements. In addition, we adopted an intuitive averaging technique on the transformed versions of each heart to obtain a new reference geometry at every iteration. This reduces biases that may be introduced by the selection of an initial reference geometry in the construction of an average cardiac geometry. The next step will focus on improving current results by using a larger database of heart samples.

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Correspondence to Mehran Ebrahimi .

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Mojica, M., Pop, M., Sermesant, M., Ebrahimi, M. (2018). Multilevel Non-parametric Groupwise Registration in Cardiac MRI: Application to Explanted Porcine Hearts. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham.

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  • Print ISBN: 978-3-319-75540-3

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