High-resolution T1-weighted image of brain1 acquired on a 3T GE Discovery MR 750 system at 0.3mm isotropic voxels.

NIfTI files to support the SIRF Exercises regarding Geometry.

Data from static phantoms in MRI and a PET/MR phantom - see readme file.

Data set of nearly 600 MR images from normal, healthy subjects, along with demographic characteristics, collected as part of the Information eXtraction from Images (IXI) project available for download. Tar files containing T1, T2, PD, MRA and DTI (15 directions) scans from these subjects are available. The data has been collected at three different hospitals in London: * Hammersmith Hospital using a Philips 3T system * Guy''s Hospital using a Philips 1.5T system * Institute of Psychiatry using a GE 1.5T system

Training Dataset for HNTSMRG 2024 Challenge

Overview

This repository houses the publicly available training dataset for the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTSMRG) 2024 Challenge.

Patient cohorts correspond to patients with histologically proven head and neck cancer who underwent radiotherapy (RT) at The University of Texas MD Anderson Cancer Center. The cancer types are predominately oropharyngeal cancer or cancer of unknown primary. Images include a pre-RT T2w MRI scan (1-3 weeks before start of RT) and a mid-RT T2w MRI scan (2-4 weeks intra-RT) for each patient. Segmentation masks of primary gross tumor volumes (abbreviated GTVp) and involved metastatic lymph nodes (abbreviated GTVn) are provided for each image (derived from multi-observer STAPLE consensus).

HNTSMRG 2024 is split into 2 tasks:

Task 1: Segmentation of tumor volumes (GTVp and GTVn) on pre-RT MRI.

Task 2: Segmentation of tumor volumes (GTVp and GTVn) on mid-RT MRI.

The same patient cases will be used for the training and test sets of both tasks of this challenge. Therefore, we are releasing a single training dataset that can be used to construct solutions for either segmentation task. The test data provided (via Docker containers), however, will be different for the two tasks. Please consult the challenge website for more details.

Data Details

DICOM files (images and structure files) have been converted to NIfTI format (.nii.gz) for ease of use by participants via DICOMRTTool v. 1.0.

Images are a mix of fat-suppressed and non-fat-suppressed MRI sequences. Pre-RT and mid-RT image pairs for a given patient are consistently either fat-suppressed or non-fat-suppressed.

Though some sequences may appear to be contrast enhancing, no exogenous contrast is used.

All images have been manually cropped from the top of the clavicles to the bottom of the nasal septum (~ oropharynx region to shoulders), allowing for more consistent image field of views and removal of identifiable facial structures.

The mask files have one of three possible values: background = 0, GTVp = 1, GTVn = 2 (in the case of multiple lymph nodes, they are concatenated into one single label). This labeling convention is similar to the 2022 HECKTOR Challenge.

150 unique patients are included in this dataset. Anonymized patient numeric identifiers are utilized.

The entire training dataset is ~15 GB.

Dataset Folder/File Structure

The dataset is uploaded as a ZIP archive. Please unzip before use. NIfTI files conform to the following standardized nomenclature: ID_timepoint_image/mask.nii.gz. For mid-RT files, a "registered" suffix (ID_timepoint_image/mask_registered.nii.gz) indicates the image or mask has been registered to the mid-RT image space (see more details in Additional Notes below).

The data is provided with the following folder hierarchy:

Top-level folder (named "HNTSMRG24_train")

Patient-level folder (anonymized patient ID, example: "2")

Pre-radiotherapy data folder ("preRT")

Original pre-RT T2w MRI volume (example: "2_preRT_T2.nii.gz").

Original pre-RT tumor segmentation mask (example: "2_preRT_mask.nii.gz").

Mid-radiotherapy data folder ("midRT")

Original mid-RT T2w MRI volume (example: "2_midRT_T2.nii.gz").

Original mid-RT tumor segmentation mask (example: "2_midRT_mask.nii.gz").

Registered pre-RT T2w MRI volume (example: "2_preRT_T2_registered.nii.gz").

Registered pre-RT tumor segmentation mask (example: "2_preRT_mask_registered.nii.gz").

Note: Cases will exhibit variable presentation of ground truth mask structures. For example, a case could have only a GTVp label present, only a GTVn label present, both GTVp and GTVn labels present, or a completely empty mask (i.e., complete tumor response at mid-RT). The following case IDs have empty masks at mid-RT (indicating a complete response): 21, 25, 29, 42. These empty masks are not errors. There will similarly be some cases in the test set for Task 2 that have empty masks.

Details Relevant for Algorithm Building

The goal of Task 1 is to generate a pre-RT tumor segmentation mask (e.g., "2_preRT_mask.nii.gz" is the relevant label). During blind testing for Task 1, only the pre-RT MRI (e.g., "2_preRT_T2.nii.gz") will be provided to the participants algorithms.

The goal of Task 2 is to generate a mid-RT segmentation mask (e.g., "2_midRT_mask.nii.gz" is the relevant label). During blind testing for Task 2, the mid-RT MRI (e.g., "2_midRT_T2.nii.gz"), original pre-RT MRI (e.g., "2_preRT_T2.nii.gz"), original pre-RT tumor segmentation mask (e.g., "2_preRT_mask.nii.gz"), registered pre-RT MRI (e.g., "2_preRT_T2_registered.nii.gz"), and registered pre-RT tumor segmentation mask (e.g., "2_preRT_mask_registered.nii.gz") will be provided to the participants algorithms.

When building models, the resolution of the generated prediction masks should be the same as the corresponding MRI for the given task. In other words, the generated masks should be in the correct pixel spacing and origin with respect to the original reference frame (i.e., pre-RT image for Task 1, mid-RT image for Task 2). More details on the submission of models will be located on the challenge website.

Additional Notes

General notes.

NIfTI format images and segmentations may be easily visualized in any NIfTI viewing software such as 3D Slicer.

Test data will not be made public until the completion of the challenge. The complete training and test data will be published together (along with all original multi-observer annotations and relevant clinical data) at a later date via The Cancer Imaging Archive. Expected date ~ Spring 2025.

Task 1 related notes.

When training their algorithms for Task 1, participants can choose to use only pre-RT data or add in mid-RT data as well. Initially, our plan was to limit participants to utilizing only pre-RT data for training their algorithms in Task 1. However, upon reflection, we recognized that in a practical setting, individuals aiming to develop auto-segmentation algorithms could theoretically train models using any accessible data at their disposal. Based on current literature, we actually don't know what the best solution would be! Would the incorporation of mid-RT data for training a pre-RT segmentation model actually be helpful, or would it merely introduce harmful noise? The answer remains unclear. Therefore, we leave this choice to the participants.

Remember, though, during testing, you will ONLY have the pre-RT image as an input to your model (naturally, since Task 1 is a pre-RT segmentation task and you won't know what mid-RT data for a patient will look like).

Task 2 related notes.

In addition to the mid-RT MRI and segmentation mask, we have also provided a registered pre-RT MRI and the corresponding registered pre-RT segmentation mask for each patient. We offer this data for participants who opt not to integrate any image registration techniques into their algorithms for Task 2 but still wish to use the two images as a joint input to their model. Moreover, in a real-world adaptive RT context, such registered scans are typically readily accessible. Naturally, participants are also free to incorporate their own image registration processes into their pipelines if they wish (or ignore the pre-RT images/masks altogether).

Registrations were generated using SimpleITK, where the mid-RT image serves as the fixed image and the pre-RT image serves as the moving image. Specifically, we utilized the following steps: 1. Apply a centered transformation, 2. Apply a rigid transformation, 3. Apply a deformable transformation with Elastix using a preset parameter map (Parameter map 23 in the Elastix Zoo). This particular deformable transformation was selected as it is open-source and was benchmarked in a previous similar application (https://doi.org/10.1002/mp.16128). For cases where excessive warping was noted during deformable registration (a small minority of cases), only the rigid transformation was applied.

Contact

We have set up a general email address that you can message to notify all organizers at: hntsmrg2024@gmail.com. Additional specific organizer contacts:

Kareem A. Wahid, PhD (kawahid@mdanderson.org)

Cem Dede, MD (cdede@mdanderson.org)

Mohamed A. Naser, PhD (manaser@mdanderson.org)

The Menelik head voxel model in NIFTI format.Dimension: 396x514x442 (xyz)Voxel size: 0.5 0.5 0.5 (mm)Byte type: 1-Byte unsigned integerCompression: GZIPData Orientaion: Column(X+), Rows(Y-), Slices(Z+)File Format: NIFTI

Content

This work is a derivative from the Atlanta sample (Liu et al., 2009) found in the 1000 functional connectome project (Biswal et al., 2010), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for 19 healthy subjects. Time series are packaged in a series of .mat matlab/octave (HDF5) files, one per subject. For each subject, an array featuring about 200 time points for 116 brain regions from the AAL template is available. The Atlanta AAL preprocessed time series release more specifically contains the following files: * README.md: a markdown (text) description of the release. * brain_rois.nii.gz: a 3D nifti volume of the AAL template at 3 mm isotropic resolution, in the MNI non-linear 2009a symmetric space (http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Region number I is filled with Is (background is filled with 0s). * labels_aal.mat: a .mat file with two variables: rois_aal(i) is the numrical ID of the i-th region in the AAL template (e.g. 2001, 2002, 2101, etc). labels_all{i} is a string label for the i-th region (e.g. 'Precentral_L', 'Precentral_R', etc). * tseries_rois_SUBJECT_session1_run1.mat: a matlab/octave file for each subject. Each tseries file contains the following variables: * confounds: a TxK array. Each row corresponds to a time sample, and each column to one confound that was regressed out from the time series during preprocessing. * labels_confounds: cell of strings. Each entry is the label of a confound that was regressed out from the time series. * mask_suppressed: a T2x1 vector. T2 is the number of time samples in the raw time series (before preprocessing), T2=205. Each entry corresponds to a time sample, and is 1 if the corresponding sample was removed due to excessive motion (or to wait for magnetic equilibrium at the beginning of the series). Samples that were kept are tagged with 0s. * time_frames: a Tx1 vector. Each entry is the time of acquisition (in s) of the corresponding volume. * tseries: a TxN array, where each row is a time sample and each column a region (N=483, numbered as in brain_rois.nii.gz). Note that the number of time samples may vary, as some samples have been removed if tagged with excessive motion.

Preprocessing

The datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.6.5c. The parameters of a rigid body motion were first estimated at each time frame of the fMRI dataset (no correction of inter-slice difference in acquisition time was applied). The median volume of the fMRI time series was coregistered with a T1 individual scan using Minctracc9 (Collins et al., 1994), which was itself transformed to the Montreal Neurological Institute (MNI) non-linear template (Fonov et al., 2011) using the CIVET10 pipeline (Zijdenbos et al., 2002). The rigid-body transform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 3 mm isotropic resolution. The “scrubbing” method of Power et al. (2012) was used to remove the volumes with excessive motion (frame displacement greater than 0.5). The following nuisance parameters were regressed out from the time series at each voxel: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of the six rigid-body motion parameters and their squares (Giove et al., 2009). The fMRI volumes were then spatially smoothed with a 6 mm isotropic Gaussian blurring kernel. The fMRI time series were spatially averaged on each of the areas of the AAL brain template (Tzourio-Mazoyer et al., 2002). To further reduce the spatial dimension, only the 81 cortical AAL areas were included in the analysis (excluding the cerebellum, the basal ganglia and the thalamus). The clustering methods were applied to these regional time series. Note that 8 subjects were excluded because there was not enough time points left after scrubbing (a minimum number of 190 volumes was selected as acceptable), and one additional subject had to be excluded because the quality of the T1-fMRI coregistration was substandard (by visual inspection). A total of 19 subjects was thus actually released.

References

Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010.Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neu-roImage 51 (3), 1126–1139.URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Biswal, B. B. et al., 2010. Toward discoveryscience of human brain function. Proceedings of the National Academy ofSciences of the U.S.A. 107 (10), 4734–4739. Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlin-ear registration-based segmentation. International Journal of Pattern Recog-nition and Artificial Intelligence 11, 1271–1294. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins,D. L., Jan. 2011. Unbiased average age-appropriate atlases for pediatric stud-ies. NeuroImage 54 (1), 313–327.URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033 Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009.Images-based suppression of unwanted global signals in resting-state func-tional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064.URL http://dx.doi.org/10.1016/j.mri.2009.06.004 Liu, H., Stufflebeam, S. M., Sepulcre, J., Hedden, T., Buckner, R. L., 2009.Evidence from intrinsic activity that asymmetry of the human brain iscontrolled by multiple factors. Proceedings of the National Academy ofSciences of the U.S.A. 106 (48), 20499–20503. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E.,Feb. 2012. Spurious but systematic correlations in functional connectivityMRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154.URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018 Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard,O., Delcroix, N., Mazoyer, B., Joliot, M., 2002. Automated anatomicallabeling of activations in SPM using a macroscopic anatomical parcellationof the MNI MRI single-subject brain. NeuroImage 15, 273–289. Zijdenbos, A. P., Forghani, R., Evans, A. C., 2002. Automatic ”pipeline”analysis of 3-D MRI data for clinical trials: application to multiple sclerosis.IEEE Transactions on Medical Imaging 21 (10), 1280–1291.

Other derivatives

This dataset was used in a publication:http://arxiv.org/abs/1501.05194

Dataset

This dataset was created by Tejo Manasa

Contents

High-resolution MR datasets of a cohort of 15 healthy adult subjects acquired on a 3T scanner at the Neuroradiology Unit and CERMAC (Center of Excellence for High Field Magnetic Resonance), Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milano, Italy. The data includes:

T1_3D_PROSET_Sag: T1-weighted volumetric sequence acquired on the sagittal plane for morphological characterization. This sequence demonstrates difference in the T1 relaxation time of tissues and provide excellent contrast between GM and WM.

3D_FLAIR_Tra: Fluid‑Attenuated Inversion Recovery volumetric sequence acquired on the axial planefor morphological characterization. This is an inversion recovery sequence with a long inversion time (TI), which results in removing signal from the cerebrospinal fluid from the images.

SWIp_axial: Susceptibility‑Weighted Imaging sequence acquired on the axial plane.This is a three-dimensional high-spatial resolution Gradient Echo MRI sequence providing excellent contrast for venous vascular modeling.

s3DI_MC_HR: three‑dimensional high‑resolution time‑of‑flight (TOF) MR angiography acquisition to visualize flow within the arterial vessel. It is based on the phenomenon of flow-related enhancement of spins entering into an imaging slice. As a result of being unsaturated, these spins give more signal that surrounding stationary spins.

MIP_s3DI_MC_HR: angiographic 3D visualization of TOF images using the maximum intensity projection (MIP) technique of reconstruction.

raw_data_DTI_32: Diffusion Tensor Imaging raw data. This is a diffusion-weighted Spin Echo EPI single-shot pulse sequence acquired on the axial planealong 32 gradient directions at a b-value of 1000 s/mm2 and one volume without diffusion-weighting (b0 image).

raw_data_NODDI: multi-compartmental dMRI sequence for advanced tractography and NODDI analyses, including an axial high angular resolution diffusion-weighted imaging (HARDI) acquisition along 60 gradient directions at a b-value of 3000 s/mm2,a DTI acquisition along 35 directions at a b-value of 711 s/mm2 and 11 volumes without diffusion-weighting (b0 images). The phase-encoding direction was anterior-to-posterior for all these acquisitions.

B0_reverse: a sequence without diffusion-weighting having the same geometrical parameters of the ‘raw_data_NODDI’ images, but acquired using a reversed phase-encoding direction (posterior-to-anterior). This volume allowed estimation and correction for susceptibility-induced distortions.

‘DTI’ Folder’: This folder contains the DTI-derived parametric maps calculated off-linefrom the ‘raw_data_DTI_32’ acquisition (32 gradient directions, b-value = 1000 s/mm2) and saved in the NIfTI-1 Data Format.

‘HARDI’ Folder: This folder contains the parametric maps calculated off-linefrom the HARDI acquisition (60 gradient directions, b-value = 3000 s/mm2) of the ‘raw_data_NODDI’ sequence. Maps are saved in the NIfTI-1 Data Format.

‘Tractography’ Folder: This folder contains the probabilistic tractography reconstructions of the main white matter fiber tracts, calculated from the HARDI acquisition (60 gradient directions, b-value = 3000 s/mm2) of the ‘raw_data_NODDI’ sequence. Dipy has been used for q-ball residual-bootstrap fiber tracking. The folder contains a minimum number of two pair of tracts for each subjects.

‘NODDI’ Folder: This folder contains the Neurite orientation dispersion and density imaging (NODDI) parametric maps calculated off-line from the ‘raw_data_NODDI’ acquisition (60 gradient directions at b=3000 s/mm2, 35 gradient directions at b=711 s/mm2 and 11 b0 volumes) and saved in the NIfTI-1 Data Format. Maps have been generated using the NODDI Matlab Toolbox (https://www.nitrc.org/projects/noddi_toolbox).

Note that all MRI data files were converted from DICOM series using Chris Rorden's dcm2niiX version v1.0.20200331.

Dataset

This dataset was created by Tejo Manasa

Contents

Collection description

None

Subject species

homo sapiens

Modality

fMRI-BOLD

Map type

U

Group-wise average of brains 1-5

ContentThis work is a derivative from the COBRE sample found in the International Neuroimaging Data-sharing Initiative (INDI), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for 72 patients diagnosed with schizophrenia (58 males, age range = 18-65 yrs) and 74 healthy controls (51 males, age range = 18-65 yrs). The fMRI dataset for each subject are single nifti files (.nii.gz), featuring 150 EPI blood-oxygenation level dependent (BOLD) volumes were obtained in 5 mns (TR = 2 s, TE = 29 ms, FA = 75°, 32 slices, voxel size = 3x3x4 mm3 , matrix size = 64x64, FOV = mm2 ). The data processing as well as packaging was implemented by Pierre Bellec, CRIUGM, Department of Computer Science and Operations Research, University of Montreal, 2016.The COBRE preprocessed fMRI release more specifically contains the following files:README.md: a markdown (text) description of the release.phenotypic_data.tsv.gz: A gzipped tabular-separated value file, with each column representing a phenotypic variable as well as measures of data quality (related to motions). Each row corresponds to one participant, except the first row which contains the names of the variables (see file below for a description).keys_phenotypic_data.json: a json file describing each variable found in phenotypic_data.tsv.gz.fmri_XXXXXXX.tsv.gz: A gzipped tabular-separated value file, with each column representing a confounding variable for the time series of participant XXXXXXX (which is the same participant ID found in phenotypic_data.tsv.gz). Each row corresponds to a time frame, except for the first row, which contains the names of the variables (see file below for a definition).keys_confounds.json: a json file describing each variable found in the files fmri_XXXXXXX.tsv.gz.fmri_XXXXXXX.nii.gz: a 3D+t nifti volume at 6 mm isotropic resolution, stored as short (16 bits) integers, in the MNI non-linear 2009a symmetric space (http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Each fMRI data features 150 volumes.Usage recommendationsIndividual analyses: You may want to remove some time frames with excessive motion for each subject, see the confounding variable called scrub in fmri_XXXXXXX.tsv.gz. Also, after removing these time frames there may not be enough usable data. We recommend a minimum number of 60 time frames. A fairly large number of confounds have been made available as part of the release (slow time drifts, motion paramaters, frame displacement, scrubbing, average WM/Vent signal, COMPCOR, global signal). We strongly recommend regression of slow time drifts. Everything else is optional.Group analyses: There will also be some residuals effect of motion, which you may want to regress out from connectivity measures at the group level. The number of acceptable time frames as well as a measure of residual motion (called frame displacement, as described by Power et al., Neuroimage 2012), can be found in the variables Frames OK and FD scrubbed in phenotypic_data.tsv.gz. Finally, the simplest use case with these data is to predict the overall presence of a diagnosis of schizophrenia (values Control or Patient in the phenotypic variable Subject Type). You may want to try to match the control and patient samples in terms of amounts of motion, as well as age and sex. Note that more detailed diagnostic categories are available in the variable Diagnosis.PreprocessingThe datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.17, under CentOS version 6.3 with Octave (http://gnu.octave.org) version 4.0.2 and the Minc toolkit (http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit) version 0.3.18. Each fMRI dataset was corrected for inter-slice difference in acquisition time and the parameters of a rigid-body motion were estimated for each time frame. Rigid-body motion was estimated within as well as between runs, using the median volume of the first run as a target. The median volume of one selected fMRI run for each subject was coregistered with a T1 individual scan using Minctracc (Collins and Evans, 1998), which was itself non-linearly transformed to the Montreal Neurological Institute (MNI) template (Fonov et al., 2011) using the CIVET pipeline (Ad-Dabbagh et al., 2006). The MNI symmetric template was generated from the ICBM152 sample of 152 young adults, after 40 iterations of non-linear coregistration. The rigid-body transform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 6 mm isotropic resolution.Note that a number of confounding variables were estimated and are made available as part of the release. WARNING: no confounds were actually regressed from the data, so it can be done interactively by the user who will be able to explore different analytical paths easily. The “scrubbing” method of (Power et al., 2012), was used to identify the volumes with excessive motion (frame displacement greater than 0.5 mm). A minimum number of 60 unscrubbed volumes per run, corresponding to ~120 s of acquisition, is recommended for further analysis. The following nuisance parameters were estimated: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the six rigid-body motion parameters (Giove et al., 2009), anatomical COMPCOR signal in the ventricles and white matter (Chai et al., 2012), PCA-based estimator of the global signal (Carbonell et al., 2011). The fMRI volumes were not spatially smoothed.ReferencesAd-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J. S., Mok, K., Ivanov, O., Vincent, R. D., Lepage, C., Lerch, J., Fombonne, E., Evans, A. C., 2006. The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization. Neuroimage, Florence, Italy.Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage 51 (3), 1126–1139. URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082F. Carbonell, P. Bellec, A. Shmuel. Validation of a superposition model of global and system-specific resting state activity reveals anti-correlated networks. Brain Connectivity 2011 1(6): 496-510. doi:10.1089/brain.2011.0065Chai, X. J., Castan, A. N. N., Ongr, D., Whitfield-Gabrieli, S., Jan. 2012. Anticorrelations in resting state networks without global signal regression. NeuroImage 59 (2), 1420-1428. http://dx.doi.org/10.1016/j.neuroimage.2011.08.048 Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlinear registration-based segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294.Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., Jan. 2011. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54 (1), 313–327. URLhttp://dx.doi.org/10.1016/j.neuroimage.2010.07.033Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064. URLhttp://dx.doi.org/10.1016/j.mri.2009.06.004Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Feb. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154. URLhttp://dx.doi.org/10.1016/j.neuroimage.2011.10.018

NiI is Wurtzite structured and crystallizes in the hexagonal P6_3mc space group. The structure is three-dimensional. Ni1+ is bonded to four equivalent I1- atoms to form corner-sharing NiI4 tetrahedra. There are one shorter (2.51 Å) and three longer (2.52 Å) Ni–I bond lengths. I1- is bonded to four equivalent Ni1+ atoms to form corner-sharing INi4 tetrahedra.

Files:

*.nii are NIfTI-1 formatted image files, http://nifti.nimh.nih.gov/. ImageJ is a suitable viewer, http://imagej.nih.gov/ij/

CINE.nii: CINE MRI frames acquired during the cardiac cycle of a mouse.
DCE001noreacq.nii: DCE MRI frames of mouse 1 without reacquisition.
DCE001reacq.nii: DCE MRI frames of mouse 1 with reacquisition.
DCE00n.nii: DCE MRI frames of mouse n with reacquisition (n = [2,7]).

LungAngio.avi
avi movie file showing angiographic blood volume image of the thorax of mouse 1.
Generated from the difference between DCE MRI frames 10 and 11 of DCE001reacq.nii.

*.txt are ASCII text files.

reacq00n.txt: DCE MRI reacquisition file for mouse n (n = [1,7]).
The files show duplicate successive entries for those acquisition blocks that were reacquired.
The number of uncorrupted acquisition blocks per breath interval can be deduced from the files.

timestamp00n.txt: DCE MRI timestamp file for mouse n (n = [1,7]).
The files show timestamps for the acquisition of the centre of k-space for each DCE MRI frame. Automated and immediate reacquisition of data that are corrupted by respiratory motion has been developed to improve respiration insensitive scanning in small animal MRI. Methods have been developed to maximise the efficiency with which respiratory and cardio-respiratory synchronised scans can be acquired and provide quantitative image data that exhibit a remarkably low level of motion artefact. 3D dynamic contrast enhanced (DCE)-MRI is performed with a mean frame interval of 13.7 s which enables early passage of contrast agent to be detected in the major vessels, the heart and lungs, and essentially presents an angiographic blood volume image of the mouse thorax. 2D steady state maintained CINE MRI is performed with a 3.24 ms frame interval starting within 3.24 ms of the cardiac R-wave. The CINE data exhibit very homogeneous image intensities throughout the cardiac cycle without any amplitude modulations, often referred to as 'flashing', which should enable more robust quantitative analyses of cardiac function.

Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing

Latest version: repository_v2.0.zip, please ignore repository.zip

Open data repository Knab et al. Prediction of stroke outcome in mice based on non-invasvive MRI and behavioral testing

Content:

README.txt

This information

dat

Contains MRI data in NIFTI format and secondary data from atlas registration. For documentation of atlas registration files see https://pubmed.ncbi.nlm.nih.gov/28829217/
Files used for the manuscript:
t2.nii: t2 weighted image acquired 24 h post stroke
masklesion.nii: manually delineated lesion
x_masklesion.nii: lesion in atlas space
ix_ANO.nii: Allen brain atlas in native space (i.e. matching t2.nii)
Lesion volume was calculated by volume of voxels unequal 0 in x_masklesion.nii
Overlap of regions defined by ix_ANO.nii with masklesion.nii were used for calculating percent damage in each atlas region

prediction_models

Contains separated training and test data as xlsx and csv files with lesion volumes in cubic mm of the Allen brain atlas space, percent damage per atlas region and behavioral data. The training data was used as input for training prediction models in MATLAB, the results were created using the test data.
The files have following sturcture:
Column 1: animal ID
Columns 2-537: MRI regions (column title corresponds to the region number as used in the Allen common coordinate framework)
Column 538: lesion volume
Column 539: initial performance (subacute deficit) = mean performance/deficit on days 2-6
Column 540: mean performance/deficit on days 2-6 = initial performance (subacute deficit) - this column equals column 539 but has different header which was used to train the residual from initial deficit
Column 541: residual performance/deficit
Column 542: test or training group
Consecutive rows contain data for each animal specified by the animal id

The repository also contains all trained models, prediction results for the test data and tables with resulting median absolute error (MedAE) and 5th, 25th, 75th and 95 absolute error quantiles for each model.
The model files end with '_models.mat' and contain 50 independently trained models each. Each model version is specified by number 1-50.
The result files end with '_test_results.mat' or '_test_results.xlsx', files with MedAE and quantiles end with '_test_errors.xlsx' or '_test_errors.csv. The common part of filenames specifies the used paradigm
Folder 'subacute deficit prediction' contains:
- initial_performance_from_lesion_volume: prediction of subacute deficit using lesion volume
- initial_performance_from_segmented_mri: prediction of subacute deficit using segmented mri
Folder 'long-term outcome prediction' contains:
- lesion_volume: prediction of residual deficit using lesion volume
- segmented_mri: prediction of residual deficit using segmented_mri
- initial_performance: prediction of residual deficit using subacute deficit
Folder 'mri_inc_oob_imp' contains models trained using increasing number of mri segments sorted according to the out-of-bag importance. The number of used segments is given in the file name. The models, results and errors are separated in subfolders.

Files with equal file name and different extension always contain the same data

templates
Allen atlas, template, brain mask, hemisphere masks, tissue probability masks in NIFTI format including annotations of region IDs and parameter.m file for use in MATLAB toolbox ANTx2

Intended as supplementary data to the manuscript "High-Resolution Metabolic Imaging of High-Grade Gliomas using 7T-CRT-FID-MRSI"

Authors:
Gilbert Hangel, Cornelius Cadrien, Philipp Lazen, Julia Furtner, Alexandra Lipka, Eva Hečková, Lukas Hingerl, Stanislav Motyka, Stephan Gruber, Bernhard Strasser, Barbara Kiesel, Mario Mischkulnig, Matthias Preusser, Thomas Roetzer, Adelheid Wöhrer, Georg Widhalm, Karl Rössler, Siegfried Trattnig and Wolfgang Bogner

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MRSI Maps from the Vienna 7T scanner in MINC format
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Contact: wolfgang.bogner@meduniwien.ac.at , gilbert.hangel@meduniwien.ac.at
https://hfmr.meduniwien.ac.at/

For use with NIFTI-displaying software.

MRSI method published as Hingerl et al 2020, doi: 10.1097/RLI.0000000000000626

Patient 1:
Glioblastoma WHO Grade 4, with IDH1 mutation, male
Patient 2:
Glioblastoma WHO Grade 4, with IDH1 mutation, male
(Same numbers as in the manuscript)

MRI template and 120-region atlas for the mouse lemur primate Microcebus murinus.

Generated from 34 animals aged 15-58 months old scanned at 7T using a T2-weighted sequence, resolution 115 × 115 × 230 µm. The code developed to create and manipulate the template has been refined into general procedures for registering small mammal brain MR images, available within a python module sammba-mri (SmAll-maMMals BrAin MRI; https://sammba-mri.github.io/). The template was up-sampled to 91 µm isotropic for hand-segmentation of structures, and also used to create probability maps of grey matter, white matter and cerebro-spinal fluid.

if used for publication please cite:

A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy
Nachiket A Nadkarni, Salma Bougacha, Clément Garin, Marc Dhenain, Jean-Luc Picq
Jan 2019
NeuroImage 185, 85-95
DOI: 10.1016/J.NEUROIMAGE.2018.10.010
https://www.sciencedirect.com/science/article/pii/S1053811918319694

This statistic depicts the average annual performance of the Nifty 50 Index in India from years 2011 to 2024. In 2024, the average annual Nifty 50 Index was reported as 23,644.8, an increase from the previous year where the value was 21,731.4.

This dataset contains unprocessed task-free functional MRI (fMRI) data acquired in three different mammalian species: long-tailed macaques (Macaca fascicularis), common marmosets (Callithrix jacchus), and rats (Rattus Norvegicus, Wistar strain). The data were obtained during isoflurane anesthesia, with the animals intubated and mechanically ventilated. All experiments were carried out in accordance with the guidelines from Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes.

Related paper

This dataset supplements the following preprint:

Sirmpilatze N, Mylius J, Ortiz-Rios M, Baudewig J, Paasonen J, Golkowski D, Ranft A, Ilg R, Gröhn O, Boretius S. 2021. Spatial signatures of anesthesia-induced burst-suppression differ between primates and rodents. bioRxiv. doi:10.1101/2021.10.15.464515

Data structure

The main data files are organized into four zipped folders - Macaque.zip, Marmoset.zip, Rat1.zip, Rat2.zip - each constituting a dataset formatted according to the Brain Imaging Data Structure specifications (BIDS v1.6.0).

• Each BIDS-formatted dataset contains subfolders for individual subjects (e.g. sub-01, sub-02, etc.), as well as a tab-separated text file, participants.tsv, with some essential information about the subjects (e.g. age, weight, sex).
• Each subject-specific folder contains subfolders named func and anat, storing fMRI and structural MRI data respectively. The (f)MRI data are provided in NIfTI format (suffixed with .nii.gz). Each NIfTI file is accompanied by a .json sidecar holding metadata.
• The func subfolders also include tab-separated text files named as {sub-id}_scans.tsv (e.g. sub-01_scans.tsv). These files provide additional information on the fMRI runs within the func subfolder, such as the isoflurane concentration during the acquisition of the fMRI run, duration of the run, etc.
• The column names in participants.tsv and {sub-id}_scans.tsv files are explained in accompanying participants.json and {sub-id}_scans.json files.

BIDS-formatted datasets

The basic characteristics of the datasets are given below. More details can be found in the preprint.

1. Macaque
   • Institution: German Primate Center (Deutsches Primatenzentrum GmbH - Leibniz-Institut für Primatenforschung), Göttingen, Germany
   • MR system: Siemens MAGNETOM Prisma 3T
   • Anatomical MRI scan: T1-weighted (MPRAGE), 1 per subject
   • fMRI scan: GE-EPI, 1 or 2 runs per subject, run duration 600 - 1200 s
   • Subjects: 13 Macaca fascicularis
   • Age range: 6.8 - 19.8 years
   • Weight range: 3.6 - 8.1 kg
   • Sex: all females
   • Ethics oversight: Lower Saxony State Office for Consumer Protection and Food Safety, Hannover, Germany (approval number 33.19-42502-04-16/2278)
2. Marmoset
   • Institution: German Primate Center (Deutsches Primatenzentrum GmbH - Leibniz-Institut für Primatenforschung), Göttingen, Germany
   • MR system: Bruker BioSpec 9.4 T, equpped with B-GA 20S gradient
   • Anatomical MRI scan: Proton density-weighted (PDw) with magnetization transfer (MT) pulse , 1 per subject
   • fMRI scan: GE-EPI, 1 run per subject, run duration 600 s
   • Subjects: 20 Callithrix jacchus
   • Age range: 1.9 - 14.2 years
   • Weight range: 337 - 517 g
   • Sex: 10 females
   • Ethics oversight: Lower Saxony State Office for Consumer Protection and Food Safety, Hannover, Germany (approval number 33.19-42502-04-17/2496)
3. Rat1
   • Institution: German Primate Center (Deutsches Primatenzentrum GmbH - Leibniz-Institut für Primatenforschung), Göttingen, Germany
   • MR system: Bruker BioSpec 9.4 T, equpped with B-GA 12S2 gradient
   • Anatomical MRI scan: T2-weighted (TurboRARE) , 1 per subject
   • fMRI scan: GE-EPI, 6 runs per subject (except for sub-10: 4 runs), run duration 720 s
   • Subjects: 11 Rattus norvegicus, Wistar strain
   • Weight range: 350 - 450 g
   • Sex: all females
   • Ethics oversight: Lower Saxony State Office for Consumer Protection and Food Safety, Hannover, Germany (approval number 33.19-42502-04-15/2042)
4. Rat2
   • Institution: A.I.V. Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
   • MR system: Bruker PharmaScan 7 T
   • Anatomical MRI scan: NOT provided
   • fMRI scan: GE-EPI, 6 runs per subject (except for sub-10: 4 runs), run duration 720 s
   • Subjects: 6 Rattus norvegicus, Wistar strain
   • Weight range: 265 - 350 g
   • Sex: all males
   • Ethics oversight: Animal Ethics Committee of the Provincial Government of Southern Finland

Example data

Before you commit to downloading the BIDS-formatted datasets, we encourage you to examine the example data that we provide in the root folder. These include one anatomical (stuctural MRI) and one functional (fMRI) scan from each of the four datasets (Rat2 contains functional scans only), with their respecitve .json sidecars. A preview of these example scans is provided by 0_preview.pdf.

Proestrus datasetsGeneral informationThe rats were in the proestrus stages of their cycle. All slices are along the transverse plane. The uteri were stained with PhosphoTungstic Acid (PTA).ContentsThere are two folders for two different rats with ID codes AWA026 and AWB018. Each folder contains a downsampled subfolder which contains the downsampled uCT images (png format) in a .nii.gz archives (NIfTI format) and the corresponding segmentation masks of the muscle layers (png format) in a .nii.gz archives (NIfTI format). The configuration files (TOML format) for the downsampled and full resolution datasets are provided. The images in the archives have been downsampled by a factor of 4 using the code available in the uterine-microCT repository (https://github.com/virtual-uterus/uterine-microCT)