DOI: 10.5061/dryad.1vhhmgqv8
title: Multi-contrast MRI and histology datasets used to train and validate MRH networks to generate virtual mouse brain histology

description: H MRI maps brain structure and function non-invasively through versatile
 contrasts that exploit inhomogeneity in tissue micro-environments.
 Inferring histopathological information from MRI findings, however,
 remains challenging due to absence of direct links between MRI signals and
 cellular structures. Here, we provided deep convolutional neural
 networks, called MRH-Nets, developed using co-registered multi-contrast
 MRI and histological data of the mouse brain, can estimate histological
 staining intensity directly from MRI signals at each voxel. The results
 provide three-dimensional maps of axons and myelin with tissue contrasts
 that closely mimics target histology and enhanced sensitivity and
 specificity compared to conventional MRI markers. The dataset
 contains multi-contrast MRI and histology used for the
 training and testing and the acquisition parameters. The datasets have
 been carefully registered to mouse brain images from the Allen Mouse Brain
 Atlas (https://mouse.brain-map.org). The source codes for
 MRH-Nets can be found
 at https://github.com/liangzifei/MRH-Net.
description: <strong>Ex vivo Mouse brain MRI:</strong> Adult
 C57BL/6 mice (P60, n=10, Charles River, Wilmington, MA, USA), rag2-/-
 shiverer and littermate controls (n=5/5, P50) were perfusion fixed with 4%
 paraformaldehyde (PFA) in PBS. The samples were preserved in 4% PFA for 24
 hours before transferring to PBS. Ex vivo MRI of mouse brain specimens was
 performed on a horizontal 7 Tesla MR scanner (Bruker Biospin, Billerica,
 MA, USA) with a triple-axis gradient system. Images were acquired using a
 quadrature volume excitation coil (72 mm inner diameter) and a
 receive-only 4-channel phased array cryogenic coil. The specimens were
 imaged with the skull intact and placed in a syringe filled with Fomblin
 (perfluorinated polyether, Solvay Specialty Polymers USA, LLC, Alpharetta,
 GA, USA) to prevent tissue dehydration (33). Three-dimensional diffusion
 MRI data were acquired using a modified 3D diffusion-weighted gradient-
 and spin-echo (DW-GRASE) sequence (34) with the following parameters: echo
 time (TE)/repetition time (TR) = 30/400ms; two signal averages; field of
 view (FOV) = 12.8 mm x 10 mm x 18 mm, resolution = 0.1 mm x 0.1 mm x 0.1
 mm; two non-diffusion weighted images (b0s); 30 diffusion encoding
 directions; and b = 2,000 and 5,000 s/mm2, total 60 diffusion weighted
 images (DWIs). Co-registered T2-weighted and magnetization transfer (MT)
 MRI data were acquired using a rapid acquisition with relaxation
 enhancement (RARE) sequence with the same FOV, resolution, and signal
 averages as the diffusion MRI acquisition and the following parameters:
 T2: TE/TR=50/3000 ms; MT: TE/TR=8/800ms, one baseline image (M0) and one
 MT-weighted (Mt) images with offset frequency/power = -3 KHz/20 uT were
 acquired. The total imaging time was approximately 12 hours for each
 specimen. For the sas4-/-p53-/- and littermate controls (n=4/4, P28), PGSE
 and OGSE diffusion MRI data were acquired with the protocol described in
 (17) and a spatial resolution of 0.1 mm x 0.1 mm x 0.1 mm. All 3D       
 MRI data were interpolated to a numerical resolution of 0.06 mm x 0.06 mm
 x 0.06 mm to match the resolution of our MRI-based atlas. <br>
 Magnetization transfer ratio (MTR) images were generated as
 MTR=(M0-Mt)/M0. From the diffusion MRI data, diffusion tensors were
 calculated using the log-linear fitting method implemented in MRtrix
 (http://www.mrtrix.org) at each voxel, and maps of mean and radial
 diffusivities and fractional anisotropy were generated,  The mouse brain
 images were spatial normalized to an ex vivo MRI template (35) using the
 Large Deformation Diffeomorphic Metric Mapping (LDDMM) method implemented
 in the DiffeoMap software (www.mristudio.org). The template images had
 been normalized to the Allen reference atlas using landmark-based image
 mapping and LDDMM.<br> Histological data. From the Allen moue brain
 atlas, single subject 3D Nissl data and 3D AF data (n=100), which were
 already registered to the ARA space, were down sampled to 0.06 mm
 isotropic resolution.  <br>
 <strong>Registration of MRI and histological
 data:</strong> Group average 3D MRI data in our previously published
 mouse brain atlas(35) were first spatially normalized to the ARA space.
 Briefly, fourteen major brain structures (e.g., cortex, hippocampus,
 striatum) in the atlas MRI data were manually segmented following the
 structural delineations in the ARA. Voxels that belong to these structures
 in the MRI and average 3D AF data in the ARA (down sampled to 0.06 mm
 isotropic resolution) were assigned distinct intensity values, and a
 diffeomorphic mapping between the discretized atlas MRI and ARA AF data
 was computed using LDDMM. The mapping was then applied to the original
 atlas MRI data to generate an MRI template registered to the ARA space.
 Using dual-channel LDDMM based on tissue contrasts in the average DWI and
 FA images and the MRI template, the 3D MRI data acquired in this study
 were accurately normalized to the ARA space. <br>     NF and MBP
 stained images of the C57BL/6 mouse brain were downloaded from the ARA
 reference dataset. Images with major artifacts or tissue loss were
 excluded. Small tissue tearing and staining artifacts were removed using
 the Inpainting feature implemented in the photoshop heading brush tool
 (www.adobe.com), and dark voxels in the ventricles were replaced by the
 average intensity values of the cortex to match MRI data. The repaired
 images were down-sampled to an in-plane resolution of 0.06 mm/voxel. For
 each 2D histological image, the best-matching MRI section in the MRI
 template was identified, and a coarse-to-fine alignment from histology to
 MRI using affine transform and landmark-based image warping tool in ImageJ
 (https://imageJ.nete/BUnwarpJ). The aligned 2D sections were then
 assembled into a 3D volume and mapped to the MRI template using LDDMM
 (between NF/MBP and FA) to further improve the quality of
 registration. 
description: The dataset contains multi-contrast MRI data acquired from ex
 vivo mouse brain specimen as well as co-registered histology data. For
 each subject, MRI data contains T2-weighted, magnetization weighted and
 non-weighted (MT/M0), and diffusion MRI data (30 directions, 2 b-shell).
 Histology data were downloaded from the Allen Mouse Brain Atlas
 (http://connectivity.brain-map.org/static/referencedata), containing
 auto-fluorescence (AF), neurofilament (NF), and myelin basic protein
 (MBP).  Below is a list of all the data files. In the
 following description, the Allen space refers to the standard space
 defined by the Allen mouse brain reference atlas v3.
 <strong>a) AF data (in the
 AF_data.zip)</strong> a.1) Allen AF referebce data are
 stored in the Allen_AF_REF folder (Allen_REF_P60.hdr and
 Allen_REF_P60.img). The data are in the Allen space and stored in Analyze
 format and can be openned using MRIcro and other software. a.2) Allen AF individual subject
 data are stored in the Allen_AF_subjects folder. The data are in the Allen
 space stored in Analyze format and can be openned using MRIcro and other
 software. <strong>b) MRI data from 10 normal
 C57BL/6 P60 mouse brains (in MRI_C57_data.zip)</strong>
 MRI data from individual subject are stored in separate folders.
 Within each subject, we have provided the following data. masked_outline.hdr/img:       
  binary mask for the brain. rigidaffine_Lddmm_dwi2000.hdr/img:
     diffusion MRI images (b=2,000 s/mm^2), total 32 images (two reference
 images and 30 diffusion weighted images), aligned to the Allen
 space. rididaffine_Lddmm_dwi5000.hdr/img:
     diffusion MRI images (b=5,000 s/mm^2), total 32 images (two reference
 images and 30 diffusion weighted images), aligned to the Allen
 space. rigidaffine_Lddmm_fa.hdr/img:      
  fractional anisotropy images, aligned to the Allen space. rigidaffine_Lddmm_t2MTONOFF.hdr/img:    T2-weighted, MT/M0 images, aligned to the Allen space.  md_mk_ByteIntenadj.hdr/img:        mean diffusivity (MD) and mean kurtosis (MK) images, aligned to the Allen space. <strong>c) MRI data from shiverer and control mouse brains (n=5/5) (in MRI_shiverer_data.zip)</strong> MRI data from individual subject are stored in separate folders. Within each subject, we have provided the following data. masked_outline.hdr/img:         binary mask for the brain. rigidaffine_b2000.hdr/img:     diffusion MRI images (b=2,000 s/mm^2), total 32 images (two reference images and 30 diffusion weighted images), aligned to the Allen space. rididaffine_b5000.hdr/img:     diffusion MRI images (b=5,000 s/mm^2), total 32 images (two reference images and 30 diffusion weighted images), aligned to the Allen space. rigidaffine_FA.hdr/img:        fractional anisotropy images, aligned to the Allen space. rigidaffine_t2MTONOFF.hdr/img:    T2-weighted, MT/M0 images, aligned to the Allen space.  <strong>d) NF data registered to MRI data (NF_to_MRI.zip)</strong> <strong>e) MBP data registered to MRI data (MBP_to_MRI.zip)</strong> <strong>f) Nissl data and co-registered OGSE diffusion MRI data (Nissl_to_MRI.zip)</strong>