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?:abstract
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PURPOSE Biopsies are a diagnostic tool for the diagnosis of histopathological, molecular biological, proteomic, and imaging data, to narrow down disease patterns or identify diseases. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) provides an emerging state-of-the-art technique for molecular imaging of biological tissue. The aim of this study is the registration of MALDI MSI data sets and data acquired from different histological stainings to create a 3D model of biopsies and whole organs. EXPERIMENTAL DESIGN The registration of the image modalities is achieved by using a variant of our global, deformable Schatten-q-Norm registration approach. Utilizing a connected-component segmentation for background removal followed by a principal-axis based linear pre-registration, the images are adjusted into a homogeneous alignment. This registration approach is accompanied by the 3D reconstruction of histological and MALDI MSI data. RESULTS With this, a system of automatic registration for cross-process evaluation, as well as for creating 3D models, is developed and established. The registration of MALDI MSI data with different histological image data is evaluated by using the established global image registration system. CONCLUSIONS AND CLINICAL RELEVANCE In conclusion, this multimodal image approach offers the possibility of molecular analyses of tissue specimens in clinical research and diagnosis. STATEMENT OF CLINICAL RELEVANCE Biopsies are used for diagnostics of histopathological, molecular biological, proteomic and/or imaging data. Registration of multimodal imaging modalities described in this study enables both the histological and the molecular analysis of such tissue samples. The visualization of several tissue slides facilitates the analysis of the tissue samples for both diagnosis and research. With the algorithm developed and established in this study, the user is able to zoom into parts of the tissue sample as well as to select the specific view angle direction into the tissue resulting in a detailed analysis at specific regions of the tissue sample. The opportunity to select the superposition variants of the image enables to focus on tissue data of interest. This mass-spectrometric-based algorithm will contribute to the translation of histological analyses to a molecular level. This article is protected by copyright. All rights reserved.
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