Abstract
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, hel** 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.
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Acknowledgments
The authors would like to thank Bastien Arnaud, Mathieu Fanuel, Loïc Foucat and Héléne Rogniaux (UR BIA, BIBS facility, INRAE Nantes, France) for preliminary data acquisition and project supervision which allowed this work to be published.
Funding
This work was financially supported by the Agence National de la Recherche (France, Grants ANR-19-CE29-0010601 “MultiRaMaS"), the CNRS (Groupement de Recherche “GdR-MSI", GDR2125), and the STS Department of the University of Bordeaux.
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Grélard, F. et al. (2024). A New Similarity Metric for Deformable Registration of MALDI–MS and MRI Images. In: Waiter, G., Lambrou, T., Leontidis, G., Oren, N., Morris, T., Gordon, S. (eds) Medical Image Understanding and Analysis. MIUA 2023. Lecture Notes in Computer Science, vol 14122. Springer, Cham. https://doi.org/10.1007/978-3-031-48593-0_13
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