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Ex vivo MRI atlas of the human medial temporal lobe: characterizing neurodegeneration due to tau pathology

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OpenNeuro2020-08-03 更新2026-03-14 收录
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# Penn Medial Temporal Lobe Atlas 2021 Data Manifest This dataset contains imaging data from paper "Ex vivo MRI atlas of the human medial temporal lobe: characterizing neurodegeneration due to tau pathology" by Ravikumar et al. published in Ravikumar, Sadhana, et al. "Ex vivo MRI atlas of the human medial temporal lobe: characterizing neurodegeneration due to tau pathology." Acta neuropathologica communications 9.1 (2021): 1-14. ## Contents of each `subject` directory Each specimen directory contains images corresponding to a single specimen. The final template is included as an additional specimen. * `sub-CNDR/NDRI/HNL` * `XXX_T2w.nii`: raw MRI image, as reconstructed from the scanner * `XXX_clip.nii.gz`: MRI image with intensity clipped to [0.1,99.9] percentile range (Sec. S1.1) * `sub-exvivoMTLatlas`: Data associated with the final template (corresponds to `ncc` below) * `sub-exvivoMTLatlas_T2w.nii.gz`: Average intensity image (identical to `template_build/ncc`) * `sub-exvivoMTLatlas_phg.nii.gz`: Average MTL segmentation (identical to `template_build/sub-exvivoMTLatlas/ncc`) * `sub-exvivoMTLatlas_phgfinal`: Final binary MTL segmentation used for morphometric analysis ## Contents of the 'derivatives' directory This directory contains five subdirectories: * `distortion_corrected`: MRI scans corrected for scanner gradient miscalibration (Sec S1.1). These scans have been resliced using an affine transform that was derived by analyzing a scan of a 3D printed phantom. * `XXX_distcorr_YYY.nii.gz`: corrected versions of the image and MTL segmentation (for `YYY` in `{phg,img}`) * `XXX_raw_to_distcorr_affine.mat`: affine transform that maps from raw space to the distortion-corrected space. * `manual_segmentation`: Image space in which semi-manual segmentation of the MTL was performed. For some specimens, this differs from the raw image space by a crop, or axis flip * `XXX_manseg_phg.nii.gz`: Segmentation of the MTL * `XXX_manseg_img.nii.gz`: MRI in the space of the segmentation * `XXX_manseg_to_raw_affine.mat`: Affine transform between image space in which manual segmentation was performed and raw image space. * `template_build`: Stores templates constructed during different stages of template-building. For each subject, including the atlas, this directory contains five subdirectories: * `mst`: Specimen mapped to the root of the minimum spanning tree, based on registration of the MTL segmentations * `gshoot`: Specimen mapped to the template created using geodesic shooting, based on registration of MTL segmentations * `ncc`: Specimen mapped to the template created using group-wise intensity registration, with initialization provided by geodesic shooting * `foil_affine_only`: Specimen aligned to other specimens using affine registration only. * `foil_affine_ncc`: Specimen mapped to the template created using group-wise intensity registration initialized by affine registration. * Each `sub-CNDR/HNL/NDRI` subject directory contains: * `sub-XXX_distor_to_ZZZ_template_warp.nii.gz`: Diffeomorphic warp between the distortion corrected image space and template space. * `sub-XXX_reslice_to_ZZZ_img.nii.gz`: Specimen's intensity image mapped into the template space * `sub-XXX_reslice_to_ZZZ_phg.nii.gz`: Specimen's MTL segmentation mapped into the template space * `sub-exvivoMTLatlas` contains: * `sub-exvivoMTLatlas_ZZZ_img.nii.gz`: Average intensity image * `sub-exvivoMTLatlas_ZZZ_phg.nii.gz`: Average MTL segmentation * `surface_thickness`: Data used to generate thickness analysis. Thickness maps are computed in Sec. S1.4.6 and presented in Fig. 2. * `sub-exvivoMTLatlas`: smoothed MTL surface in template space. This mapped to subject space for the thickness computation * `sub-exvivoMTLatlas_phg_surface_smooted.vtk` * `subj-CNDR/HNL/NDRI/`: Subject-specific thickness maps (in distcorr space) * `XXX_YYY_skeleton.vtk`: MTL Skeleton mesh * `XXX_YYY_thickness.vtk`: MTL Thickness. The variable Thickness_md takes into account surface vertices with missing data.
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2020-08-03
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