GRELARD, Florent; TUCK, Michael; COSENZA, Elise; LEGLAND, David; DURAND, Marlene; MIRAUX, Sylvain; DESBENOIT, Nicolas

doi:10.1007/978-3-031-48593-0_13

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GRELARD, Florent
Chimie et Biologie des Membranes et des Nanoobjets [CBMN]

TUCK, Michael

Chimie et Biologie des Membranes et des Nanoobjets [CBMN]

COSENZA, Elise

Langue

Chapitre d'ouvrage

Ce document a été publié dans

Medical Image Understanding and Analysis. 2024-12-02, vol. 14122, p. 171-181

Résumé en anglais

Multimodal imaging is a prominent strategy for biomedical research. For instance, Mass Spectrometry Imaging (MSI) can reveal the chemical composition of tissues with high specificity, helping to elucidate their metabolic pathways. However, this technique is not necessarily informative about the structural organization of a tissue. Other modalities, such as Magnetic Resonance Imaging (MRI), reveal functional areas in tissue. Images are analyzed jointly using several computational methods. Registration is a pivotal step that estimates a transformation to spatially align two images. Automatic methods usually rely on similarity metrics. Similarity metrics are used as optimization functions to find the transformation parameters. MALDI–MS and MR images have different intensity distributions that cannot be accounted for by traditional similarity metrics. In this article, we propose a novel similarity metric for deformable registration, based on the update of distance transformation values. We show that our method limits the intensity distortions while providing precisely registered images, on both synthetic and mouse brain images.< Réduire

Mots clés en anglais

Image registration

Magnetic Resonance Imaging

Mass spectrometry imaging

Multimodal imaging

URI

https://oskar-bordeaux.fr/handle/20.500.12278/204409

DOI

http://dx.doi.org/10.1007/978-3-031-48593-0_13

Project ANR

Reactivité Ultrarapide des Biomolecules sous Irradiation - ANR-19-CE29-0011

Unités de recherche

CBMN : Chimie & de Biologie des Membranes & des Nano-objets - UMR 5248