DOI: 10.5281/zenodo.14559355
title: Histology-informed microstructural diffusion simulations for MRI cancer characterisation (Histo-µSim): ex vivo mouse data

description: The Histo-μSim diffusion MRI framework: data from fixed mouse tissue scanned ex vivo

This record contains diffusion MRI and hematoxylin-eosin (HE) histological data from fixed mouse tissue, scanned ex vivo on a 9.4T system. Data have been obtained from MMTV-PyMT transgenic mice and from the folic acid-induced kidney injury model.

- Samples are illustrated in sample_illustration.pdf and sample_illustration.pptx- Animal models and MRI/histology acquisitions are detailed in acquisition_information.txt- The numbering of the MRI ROIs used in the paper is visualised in ROILocations.pdf- Data are stored in the exvivo_mouse.zip folder.

The data has been used in the following paper:

"Histology-informed microstructural diffusion simulations for MRI cancer characterisation - the Histo-μSim framework"Athanasios Grigoriou, Carlos Macarro, Marco Palombo, Daniel Navarro-Garcia, Anna Voronova, Kinga Bernatowicz, Ignasi Barba, Alba Escriche, Emanuela Greco, Maria Abad, Sara Simonetti, Garazi Serna, Richard Mast, Xavier Merino, Nuria Roson, Manuel Escobar, Maria Vieito, Paolo Nuciforo, Rodrigo Toledo, Elena Garralda, Roser Sala-Llonch, Els Fieremans, Dmitry S. Novikov, Raquel Perez-Lopez and Francesco Grussu.Commun Biol 8, 1695 (2025), https://doi.org/10.1038/s42003-025-09096-3  

Content of the unzipped exvivo_mouse.zip folder 

- ./exvivo_mouse/acquisition_information.txt: details on the mouse models and on the MRI and histology acquisition

- ./exvivo_mouse/histology: HE histological images of 4 breast samples, 2 kidneys, and 2 spleens 

- ./exvivo_mouse/scans: diffusion MRI scans

Content of ./exvivo_mouse/histology

- ./exvivo_mouse/histology/histology_images: raw histological images in .ndpi format  - ./exvivo_mouse/histology/manual_segmentations: manual segmentations of cells across multiples regions-of-interest in SVG format (Commun Biol 8, 1695 (2025), https://doi.org/10.1038/s42003-025-09096-3)                                                    

Content of ./exvivo_mouse/histology/histology_images- ./exvivo_mouse/histology/histology_images/breast: breast histology. It contains two files:    * fslslice4_BC_week11_week14.ndpi: breast tumours at week 11 and 14      * fslslice4_NC_breast_BC_week9.ndpi: breast tumour at week 9 and non-cancerous breast- ./exvivo_mouse/histology/histology_images/kidney_spleen: histology of spleens and kidneys. It contains one file with all specimens (fslslice6_KID_SPLEEN_2_HE.ndpi)

 

Content of ./exvivo_mouse/scans- ./exvivo_mouse/scans/breast: diffusion MRI (dMRI) of the breast specimens (pulsed gradient spin echo).- ./exvivo_mouse/scans/kidney_spleens: dMRI of the kidney and spleen specimens.

Each of these two contains the following files:

- dwi.nii: raw dMRI scan- dwi.bval: b-values in s/mm2 of dwi.nii (FSL format)- dwi.bvec: gradient directions of dwi.nii (FSL format)- dwi.gdur: gradient duration of dwi.nii in ms (FSL format)- dwi.gsep: gradient separation of dwi.nii in ms (FSL format)- dwi_denoise_unring.nii: preprocessed dMRI scan (MP-PCA denoising and Gibbs unringing)- dwi_denoise_unring_sphmean.nii: spherical mean of dwi_denoise_unring.nii (average of images for x, y and z gradients)- dwi_denoise_unring_sphmean.bval: approximated b-value of each volume of dwi_denoise_unring_sphmean.nii, in s/mm2 (FSL format)- dwi_denoise_unring_sphmean.gdur: gradient duration of each volume of dwi_denoise_unring_sphmean.nii, in ms (FSL format)- dwi_denoise_unring_sphmean.gsep: gradient separation of each volume of dwi_denoise_unring_sphmean.nii, in ms (FSL format)- dwi_denoise_unring_sphmean.scheme: b-values, gradient duration and gradient separation as one scheme file- dwi_mask.nii: mask covering all samples of each scan- dwi_noise.nii: standard deviation of noise (from the MPPCA denoising)- ROI_masks.nii.gz: location of the regions-of-interest (ROIs) used in Commun Biol 8, 1695 (2025), https://doi.org/10.1038/s42003-025-09096-3. The ROIs have been drawn in the MRI slice from which the HE histological images where collected. Please also see ROILocations.pdf- maps_analyticalmodel: dMRI parametric maps from fitting an analytical signal model of intra-cellular, restricted diffusion within spherical cells, and hindered, extra-cellular Gaussian diffusion. See Commun Biol 8, 1695 (2025), https://doi.org/10.1038/s42003-025-09096-3.- maps_Histo_uSim: dMRI parametric maps from the proposed Histo-μSim technique. See Commun Biol 8, 1695 (2025), https://doi.org/10.1038/s42003-025-09096-3.                                                                            The folder maps_analyticalmodel contains maps from fitting a two-compartment analytical signal model, accounting for restrictedintra-cellular diffusion within spherical cells of equal size, and hindered, Gaussian extra-cellular diffusion. The model has beenfitted using the pgse2sphereinex.py script, released within the BodyMRITools python package (https://github.com/fragrussu/bodymritools).

The output parametric maps are:

- InExAnalysis_AIC.nii: Akaike information criterion (quality of fit)- InExAnalysis_BIC.nii: Bayesian information criterion (quality of fit)- InExAnalysis_cellsmm-2.nii: cell density per unit area, in cells/mm2- InExAnalysis_cellsmm-3.nii: cell density per unit volume, in cells/mm3- InExAnalysis_D0um2ms-1.nii: intrinsic intra-cellular diffusivity, in um2/ms- InExAnalysis_Dexinfum2ms-1.nii: extra-cellular apparent diffusion coefficient (ADCex), in um2/ms- InExAnalysis_exit.nii: fitting exit code (0 background, 1 success, -1 failure)- InExAnalysis_fin.nii: intra-cellular signal fraction- InExAnalysis_fobj.nii: fitting objective function- InExAnalysis_logL.nii: fitting log-likelihood- InExAnalysis_Lum.nii: cell diameter in um- InExAnalysis_S0.nii: b = 0 signal level estimate

The folder maps_Histo_uSim contains maps from fitting the proposed Histo-μSim signal model, which has been developed by learninga forward model from synthetic diffusion MRI signals form virtual cancer environments reconstructed directed from histology. Theenvironments used to build Histo-μSim are already available online at the following, permanent address:

Grigoriou, A., Macarro, C., Voronova, A., Perez-Lopez, R., & Grussu, F. (2024). Histology-informed microstructural diffusion simulations for MRI cancer characterisation (Histo-µSim): histology substrates. https://doi.org/10.5281/zenodo.14559103 

Histo-μSim has been fitted using the mri2micro_dictml.py script, released within the BodyMRITools python package (https://github.com/fragrussu/bodymritools).

The maps contained in the maps_Histo_uSim folder are:

- InExAnalysis_AIC.nii: Akaike information criterion (quality of fit)- InExAnalysis_BIC.nii: Bayesian information criterion (quality of fit)- InExAnalysis_exit.nii: fitting exit code (0 background, 1 success, -1 failure)- InExAnalysis_fobj.nii: fitting objective function- InExAnalysis_logL.nii: fitting log-likelihood- InExAnalysis_par1.nii: intra-cellular signal fraction- InExAnalysis_par2.nii: mean cell diameter in um- InExAnalysis_par3.nii: variance of cell diameters, in um2- InExAnalysis_par4.nii: skewness of the cell diameter distribution- InExAnalysis_par5.nii: intrinsic intra-cellular diffusivity, in um2/ms - InExAnalysis_par6.nii: intrinsic extra-cellular diffusivity, in um2/ms

Histo-μSim parameteric MRI maps have been generated using the dMRIMC GitHub repository (permanent address: https://github.com/radiomicsgroup/dMRIMC )