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\(\ell _2\) Similarity Metrics for Diffusion Multi-Compartment Model Images Registration

  • Olivier Commowick
  • Renaud Hédouin
  • Emmanuel Caruyer
  • Christian Barillot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

Diffusion multi-compartment models (MCM) allow for a fine and comprehensive study of the white matter microstructure. Non linear registration of MCM images may provide valuable information on the brain e.g. through population comparison. State-of-the-art MCM registration however relies on pairing-based similarity measures where the one-to-one mapping of MCM compartments is required. This approach leads to non differentiabilties or discontinuities, which may turn into poorer registration. Moreover, these measures are often specific to one MCM compartment model. We propose two new MCM similarity measures based on the space of square integrable functions, applied to MCM characteristic functions. These measures are pairing-free and agnostic to compartment types. We derive their analytic expressions for multi-tensor models and propose a spherical approximation for more complex models. Evaluation is performed on synthetic deformations and inter-subject registration, demonstrating the robustness of the proposed measures.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Olivier Commowick
    • 1
  • Renaud Hédouin
    • 1
  • Emmanuel Caruyer
    • 1
  • Christian Barillot
    • 1
  1. 1.VisAGeS U1228 INSERM/Inria, IRISA UMR CNRS 6074RennesFrance

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