L2 Similarity Metrics for Diffusion Multi-Compartment Model Images Registration

Olivier Commowick; Renaud Hedouin; Emmanuel Caruyer; Christian Barillot

doi:10.1007/978-3-319-66182-7_30

L2 Similarity Metrics for Diffusion Multi-Compartment Model Images Registration

Olivier Commowick ^{1, *} Renaud Hedouin ¹ Emmanuel Caruyer ¹ Christian Barillot ¹

* Corresponding author

1 VisAGeS - Vision, Action et Gestion d'informations en Santé

INSERM - Institut National de la Santé et de la Recherche Médicale : U1228, Inria Rennes – Bretagne Atlantique , IRISA_D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE

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.

Document type :

Conference papers

Domain :

Life Sciences [q-bio] / Bioengineering

Life Sciences [q-bio] / Neurons and Cognition [q-bio.NC]

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https://www.hal.inserm.fr/inserm-01556476
Contributor : Olivier Commowick <>
Submitted on : Wednesday, July 5, 2017 - 11:00:06 AM
Last modification on : Monday, March 4, 2019 - 2:07:43 PM

MICCAI17-317.pdf

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