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iLogDemons: A Demons-Based Registration Algorithm for Tracking Incompressible Elastic Biological Tissues

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Abstract

Tracking soft tissues in medical images using non-linear image registration algorithms requires methods that are fast and provide spatial transformations consistent with the biological characteristics of the tissues. LogDemons algorithm is a fast non-linear registration method that computes diffeomorphic transformations parameterised by stationary velocity fields. Although computationally efficient, its use for tissue tracking has been limited because of its ad-hoc Gaussian regularisation, which hampers the implementation of more biologically motivated regularisations. In this work, we improve the logDemons by integrating elasticity and incompressibility for soft-tissue tracking. To that end, a mathematical justification of demons Gaussian regularisation is proposed. Building on this result, we replace the Gaussian smoothing by an efficient elastic-like regulariser based on isotropic differential quadratic forms of vector fields. The registration energy functional is finally minimised under the divergence-free constraint to get incompressible deformations. As the elastic regulariser and the constraint are linear, the method remains computationally tractable and easy to implement. Tests on synthetic incompressible deformations showed that our approach outperforms the original logDemons in terms of elastic incompressible deformation recovery without reducing the image matching accuracy. As an application, we applied the proposed algorithm to estimate 3D myocardium strain on clinical cine MRI of two adult patients. Results showed that incompressibility constraint improves the cardiac motion recovery when compared to the ground truth provided by 3D tagged MRI.

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Authors and Affiliations

  1. Asclepios Research Project, INRIA Sophia Antipolis Méditerranée, 2004 Route des Lucioles, BP 93, 06902, Sophia Antipolis, France

    Tommaso Mansi, Xavier Pennec, Maxime Sermesant, Hervé Delingette & Nicholas Ayache

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  1. Tommaso Mansi
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  2. Xavier Pennec
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  3. Maxime Sermesant
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  4. Hervé Delingette
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  5. Nicholas Ayache
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Correspondence to Tommaso Mansi.

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Mansi, T., Pennec, X., Sermesant, M. et al. iLogDemons: A Demons-Based Registration Algorithm for Tracking Incompressible Elastic Biological Tissues. Int J Comput Vis 92, 92–111 (2011). https://doi.org/10.1007/s11263-010-0405-z

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