A New Similarity Metric for Deformable Registration of MALDI–MS and MRI Images

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.