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A Locally Linear Method for Enforcing Temporal Smoothness in Serial Image Registration

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Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data (STIA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8682))

Abstract

Deformation fields obtained from image registration are commonly used for deriving measurements of morphological changes between reference and follow-up images. As the underlying image matching problem is ill-posed, the exact shape of these deformation fields is often dependent on the regularization method. In longitudinal and cross-sectional studies this effect is amplified if time between acquisitions varies and smoothness between serial deformations is neglected. Existing solutions suffer from high computational costs, strong modeling assumptions and the bias towards a single reference image. In this paper, we propose a computationally efficient solution to this problem via a temporal smoothing formulation in the one-parameter subgroup of diffeomorphisms parametrized by stationary velocity fields. When applied to modeling fetal brain development, the proposed regularization results in smooth deformation fields over time and high data fidelity.

This research was supported by the Austian National Bank (14812, FETALMORPHO), the Austrian Science Fund (P 22578-B19, PULMARCH), and the European Union (FP7-ICT-2009-5/257528, KHRESMOI and FP7-ICT-2009-5/318068, VISCERAL)

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Author information

Authors and Affiliations

  1. CIR Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria

    Ernst Schwartz, Andras Jakab & Georg Langs

  2. Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria

    Ernst Schwartz, Andras Jakab, Gregor Kasprian & Georg Langs

  3. Laboratory for Computational Neuroimaging, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA

    Lilla Zöllei

Authors
  1. Ernst Schwartz
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  2. Andras Jakab
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  3. Gregor Kasprian
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  4. Lilla Zöllei
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  5. Georg Langs
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Corresponding author

Correspondence to Ernst Schwartz .

Editor information

Editors and Affiliations

  1. INRIA, Institut du Cerveau et de la Moelle Épinière, Paris, France

    Stanley Durrleman

  2. University of Utah, Salt Lake City, Utah, USA

    Tom Fletcher

  3. University of Utah, Salt Lake City, Utah, USA

    Guido Gerig

  4. University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Marc Niethammer

  5. INRIA Sophia, Asclepios Team, Sophia-Antipolis Cedex, France

    Xavier Pennec

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Schwartz, E., Jakab, A., Kasprian, G., Zöllei, L., Langs, G. (2015). A Locally Linear Method for Enforcing Temporal Smoothness in Serial Image Registration. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M., Pennec, X. (eds) Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. STIA 2014. Lecture Notes in Computer Science(), vol 8682. Springer, Cham. https://doi.org/10.1007/978-3-319-14905-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-14905-9_2

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