← Search

Data · dataset · 2023

Coregistered H&E- and VEGF-stained histology slides with diffusion tensor imaging data at 200 μm resolution in meningioma tumors

Listed in Researchdata.se

Description

Mean diffusivity (MD) and fractional anisotropy (FA) obtained with diffusion tensor imaging (DTI) have been associated with cell density and tissue anisotropy across tumors, but these associations have been challenged at the microscopic level and several additional histological features have been suggested as contributing to MD and FA. To facilitate investigation of the biological underpinnings of DTI parameters, we performed ex-vivo dMRI at 200 μm isotropic resolution on 16 excised meningioma tumor samples.

The samples together span a variety of microstructural features: six different meningioma types and two different grades. Diffusion tensor imaging (DTI) was used to produce maps such as MD, FA, in-plane FA (FAIP), axial diffusivity (AD) or radial diffusivity (RD). The maps were coregistered to H&E (hematoxylin & eosin) and VEGF-stained histological slides.

Read the rest (2 more)

In this repository, we provide raw and analysed DTI maps coregistered to H&E- and VEGF-stained histology slides, as well as an example analysis of the data that aims to quantify the degree to which cell density (CD), structure anisotropy (SA), as determined from histology, in comparison with convolutional neural network (CNN) account for the intra-tumor variability of MD and FAIP in meningioma tumors. The pipeline used to process the raw DTI data and the coregistration tools are hosted by GitHub and the code related to the our example analysis are available here.

Please refer and cite our two journal articles mentioned in the section References below for further information on the processing and if you find this data useful. We hope that data can be used in research and education concerning the link between the meningioma microstructure and parameters obtained by diffusion MRI.

Links

Where it is published

Catalogue records · 1

Topics

Inferred from text
Cancer 65% · Imaging 75% · Magnetic resonance imaging 65%
Provenance · 1 source records, 10 field assertions
SourceKeyLast seenRaw
Researchdata.seoai:researchdata.se:doi-10-23698-aida-micromen/013 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · researchdata seconnector:researchdata_se@1.0.0
concepts[disease].local:disease:cancerenrichment · researchdata sekeyword-concept-rules@1.0.0title+description (65%)
concepts[field].local:field:earth-environmentalmapping · researchdata seconnector:researchdata_se@1.0.0
concepts[field].local:field:medicine-healthmapping · researchdata seconnector:researchdata_se@1.0.0
concepts[modality].local:modality:imagingenrichment · researchdata sekeyword-concept-rules@1.0.0title+description (75%)
concepts[modality].local:modality:mrienrichment · researchdata sekeyword-concept-rules@1.0.0title+description (65%)
descriptionsource · researchdata seconnector:researchdata_se@1.0.0/metadata/dc/description
license_textsource · researchdata seconnector:researchdata_se@1.0.0
publication_datesource · researchdata seconnector:researchdata_se@1.0.0
titlesource · researchdata seconnector:researchdata_se@1.0.0/metadata/dc/title