Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D.

DOI

The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons.              In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP).           The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix.  

 

We provide the data in raw format and as aligned data sets, as well as their spatial transformations.

Note: There are ongoing corrections taking place with the B6 mouse strain data (P2A_B6_M1, P2A_B6_M2, P2A_B6_M6, P2A_B6_M10). If you are interested in working with these data, please wait for the new version to be uploaded or contact the authors of https://doi.org/10.1016/j.radonc.2023.109591

Chunked zip: The histological data are stored as chunked .zip files (.zip.001 - .zip.0XX). In order to unpack the data, download all chunks into the same directory, then unpack.

{"references": ["Suckert, T. et al. (2019): High-precision image-guided proton irradiation of mouse brain sub-volumes", "Bogovic, J. A. et al. (2016): Robust registration of calcium images by learned contrast synthesis", "Klein, S. et al. (2010), elastix: a toolbox for intensity based medical image registration", "Dorr, A. E. et al. (2008). High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice", "Steadman, P. E. et al. (2014). Genetic effects on cerebellar structure across mouse models of autism using a magnetic resonance imaging atlas", "Ullmann, J. F. P. et al. (2013). A segmentation protocol and MRI atlas of the C57BL/6J mouse neocortex", "Richards, K. et al. (2011). Segmentation of the mouse hippocampal formation in magnetic resonance images", "Qiu, L. R. et al. (2018). Mouse MRI shows brain areas relatively larger in males emerge before those larger in females"]}

Identifier
DOI https://doi.org/10.14278/rodare.1849
Related Identifier https://doi.org/10.3389/fonc.2020.598360
Related Identifier https://www.hzdr.de/publications/Publ-31469
Related Identifier https://www.hzdr.de/publications/Publ-32124
Related Identifier https://www.hzdr.de/publications/Publ-32394
Related Identifier https://doi.org/10.1016/j.radonc.2023.109591
Related Identifier https://doi.org/10.14278/rodare.557
Related Identifier https://rodare.hzdr.de/communities/ecfunded
Related Identifier https://rodare.hzdr.de/communities/health
Related Identifier https://rodare.hzdr.de/communities/rodare
Metadata Access https://rodare.hzdr.de/oai2d?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:rodare.hzdr.de:1849
Provenance
Creator Müller, Johannes ORCID logo; Suckert, Theresa ORCID logo; Beyreuther, Elke ORCID logo; Schneider, Moritz; Boucsein, Marc ORCID logo; Bodenstein, Elisabeth ORCID logo; Stolz-Kieslich, Liane; Krause, Mechthild ORCID logo; Neubeck, Cläre Von ORCID logo; Haase, Robert ORCID logo; Lühr, Armin ORCID logo; Dietrich, Antje ORCID logo; Nexhipi, Sindi ORCID logo
Publisher Rodare
Contributor European Commission
Publication Year 2022
Funding Reference European Commission info:eu-repo/grantAgreement/EC/H2020/730983/
Rights Creative Commons Attribution 4.0 International; Open Access; https://creativecommons.org/licenses/by/4.0/legalcode; info:eu-repo/semantics/openAccess
OpenAccess true
Contact https://rodare.hzdr.de/support
Representation
Language English
Resource Type Dataset
Version 0.3.1
Discipline Life Sciences; Natural Sciences; Engineering Sciences