Lausanne_TOF-MRA_Aneurysm_Cohort

uploaded by Tommaso Di Noto on 2021-09-28 - 27 days ago
last modified on 2021-09-27 - 28 days ago
authored by Tommaso Di Noto, Guillaume Marie, Sebastien Tourbier, Yasser Alemán-Gómez, Oscar Esteban, Guillaume Saliou, Meritxell Bach Cuadra, Patric Hagmann, Jonas Richiardi
1610
We found 1 Warning in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-000/ses-20101230/anat/sub-000_ses-20101230_T1w.nii.gz

The most common set of dimensions is: 30,512,512 (voxels), This file has the dimensions: 37,420,448 (voxels).

/sub-001/ses-20101230/anat/sub-001_ses-20101230_T1w.nii.gz

The most common resolution is: 3.90mm x 0.56mm x 0.56mm, This file has the resolution: 4.40mm x 0.45mm x 0.45mm.

/sub-001/ses-20101230/anat/sub-001_ses-20101230_angio.nii.gz

The most common set of dimensions is: 350,448,160 (voxels), This file has the dimensions: 384,512,90 (voxels). The most common resolution is: 0.47mm x 0.47mm x 0.70mm, This file has the resolution: 0.39mm x 0.39mm x 1.00mm.

/sub-002/ses-20101230/anat/sub-002_ses-20101230_T1w.nii.gz

The most common set of dimensions is: 30,512,512 (voxels), This file has the dimensions: 35,420,448 (voxels).

/sub-006/ses-20101227/anat/sub-006_ses-20101227_T1w.nii.gz

The most common set of dimensions is: 25,512,512 (voxels), This file has the dimensions: 35,420,448 (voxels). The most common resolution is: 6.00mm x 0.47mm x 0.47mm, This file has the resolution: 3.90mm x 0.56mm x 0.56mm.

/sub-006/ses-20101227/anat/sub-006_ses-20101227_angio.nii.gz

The most common set of dimensions is: 384,512,90 (voxels), This file has the dimensions: 350,448,144 (voxels). The most common resolution is: 0.39mm x 0.39mm x 1.00mm, This file has the resolution: 0.47mm x 0.47mm x 0.70mm.

/sub-107/ses-20100218/anat/sub-107_ses-20100218_T1w.nii.gz

The most common set of dimensions is: 35,420,448 (voxels), This file has the dimensions: 35,784,784 (voxels). The most common resolution is: 3.90mm x 0.56mm x 0.56mm, This file has the resolution: 4.00mm x 0.29mm x 0.29mm.

/sub-107/ses-20100218/anat/sub-107_ses-20100218_angio.nii.gz

The most common set of dimensions is: 352,448,160 (voxels), This file has the dimensions: 512,512,140 (voxels). The most common resolution is: 0.47mm x 0.47mm x 0.70mm, This file has the resolution: 0.41mm x 0.41mm x 0.55mm.

/sub-120/ses-20100112/anat/sub-120_ses-20100112_T1w.nii.gz

The most common set of dimensions is: 35,420,448 (voxels), This file has the dimensions: 35,784,784 (voxels). The most common resolution is: 3.90mm x 0.56mm x 0.56mm, This file has the resolution: 4.00mm x 0.29mm x 0.29mm.

/sub-120/ses-20100112/anat/sub-120_ses-20100112_angio.nii.gz

The most common set of dimensions is: 350,448,160 (voxels), This file has the dimensions: 512,512,140 (voxels). The most common resolution is: 0.51mm x 0.51mm x 0.70mm, This file has the resolution: 0.41mm x 0.41mm x 0.55mm.

and 67 more files

OpenNeuro Accession Number: ds003821
Files: 1189, Size: 13.46GB, Subjects: 284, Sessions: 245
No Available Tasks
Available Modalities: MRI

README

This dataset was obtained from the Lausanne University Hospital (CHUV). Accession #: ds-aneurysm Description: This dataset set is composed of 284 TOF-MRA subjects, of which 127 are healthy patients and 157 have brain aneurysm(s).

Please cite the following reference if you use this dataset:

Di Noto, et al. "Towards clinically applicable automated aneurysm detection in TOF-MRA: weak labels, anatomical knowledge, and open data." arXiv preprint arXiv:2103.06168v3 (2021).

Here is an overview of the dataset organization: every original folder (i.e. non-derivative) contains the TOF-MRA and the T1w volume of the subjects. Instead, the "derivatives" folder contains 3 sub-folders:

1) "manual_masks": for controls, it only contains the skull-stripped TOF-MRA volume; for patients, it contains the skull-stripped volume and the binary manual mask(s) of the aneurysm(s). 2) "N4_bias_field_corrected": for every subject, it contains the N4-bias-field-corrected TOF-MRA volume and the corresponding N4-bias-field-corrected skull-stripped volume. 3) "registrations": it contains 3 sub-folders 3.1) "reg_metrics": for every subject/session, it contains the registration quality metrics. We performed 2 registrations: MNI_2_T1w, and T1w_2_TOF. For each of these registrations, we save the ANTsNeighborhoodCorrelation and MattesMutualInformation of the ANTs package. The metrics can be used to check which are the subjects for which the registration was not accurate. 3.2) "reg_params": for every subject/session, it contains the parameters used in the registration (i.e., warp fields and .mat files). 3.3) "vesselMNI_2_angioTOF": for every subject/session, it contains the probabilistic vessel atlas (Mouches et al., 2019) co-registered to TOF-MRA subject space.

Authors

  • Tommaso Di Noto
  • Guillaume Marie
  • Sebastien Tourbier
  • Yasser Alemán-Gómez
  • Oscar Esteban
  • Guillaume Saliou
  • Meritxell Bach Cuadra
  • Patric Hagmann
  • Jonas Richiardi

Dataset DOI

10.18112/openneuro.ds003821.v1.0.0

License

CC0

Acknowledgements

None

How to Acknowledge

None

Funding

  • This work is supported by interdisciplinary fund of the Faculty of Biology and Medicine of the Lausanne University, the Centre d’Imagerie BioMedicale (CIBM) of the University of Lausanne (UNIL), and the Swiss National Science Foundation (grant numbers: 185872 for OE, 170873 for ST, 185897 for YA)

References and Links

  • If using this dataset, please cite our paper: 'Towards clinically applicable automated aneurysm detection in TOF-MRA: weak labels, anatomical knowledge, and open data' arXiv preprint arXiv:2103.06168v3 (2021).

Ethics Approvals

How To Cite

Copy
Tommaso Di Noto and Guillaume Marie and Sebastien Tourbier and Yasser Alemán-Gómez and Oscar Esteban and Guillaume Saliou and Meritxell Bach Cuadra and Patric Hagmann and Jonas Richiardi (2021). Lausanne_TOF-MRA_Aneurysm_Cohort. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003821.v1.0.0
More citation info

Lausanne_TOF-MRA_Aneurysm_Cohort

uploaded by Tommaso Di Noto on 2021-09-28 - 27 days ago
last modified on 2021-09-27 - 28 days ago
authored by Tommaso Di Noto, Guillaume Marie, Sebastien Tourbier, Yasser Alemán-Gómez, Oscar Esteban, Guillaume Saliou, Meritxell Bach Cuadra, Patric Hagmann, Jonas Richiardi
1610

OpenNeuro Accession Number: ds003821
Files: 1189, Size: 13.46GB, Subjects: 284, Sessions: 245
No Available Tasks
Available Modalities: MRI

README

This dataset was obtained from the Lausanne University Hospital (CHUV). Accession #: ds-aneurysm Description: This dataset set is composed of 284 TOF-MRA subjects, of which 127 are healthy patients and 157 have brain aneurysm(s).

Please cite the following reference if you use this dataset:

Di Noto, et al. "Towards clinically applicable automated aneurysm detection in TOF-MRA: weak labels, anatomical knowledge, and open data." arXiv preprint arXiv:2103.06168v3 (2021).

Here is an overview of the dataset organization: every original folder (i.e. non-derivative) contains the TOF-MRA and the T1w volume of the subjects. Instead, the "derivatives" folder contains 3 sub-folders:

1) "manual_masks": for controls, it only contains the skull-stripped TOF-MRA volume; for patients, it contains the skull-stripped volume and the binary manual mask(s) of the aneurysm(s). 2) "N4_bias_field_corrected": for every subject, it contains the N4-bias-field-corrected TOF-MRA volume and the corresponding N4-bias-field-corrected skull-stripped volume. 3) "registrations": it contains 3 sub-folders 3.1) "reg_metrics": for every subject/session, it contains the registration quality metrics. We performed 2 registrations: MNI_2_T1w, and T1w_2_TOF. For each of these registrations, we save the ANTsNeighborhoodCorrelation and MattesMutualInformation of the ANTs package. The metrics can be used to check which are the subjects for which the registration was not accurate. 3.2) "reg_params": for every subject/session, it contains the parameters used in the registration (i.e., warp fields and .mat files). 3.3) "vesselMNI_2_angioTOF": for every subject/session, it contains the probabilistic vessel atlas (Mouches et al., 2019) co-registered to TOF-MRA subject space.

Authors

  • Tommaso Di Noto
  • Guillaume Marie
  • Sebastien Tourbier
  • Yasser Alemán-Gómez
  • Oscar Esteban
  • Guillaume Saliou
  • Meritxell Bach Cuadra
  • Patric Hagmann
  • Jonas Richiardi

Dataset DOI

10.18112/openneuro.ds003821.v1.0.0

License

CC0

Acknowledgements

None

How to Acknowledge

None

Funding

  • This work is supported by interdisciplinary fund of the Faculty of Biology and Medicine of the Lausanne University, the Centre d’Imagerie BioMedicale (CIBM) of the University of Lausanne (UNIL), and the Swiss National Science Foundation (grant numbers: 185872 for OE, 170873 for ST, 185897 for YA)

References and Links

  • If using this dataset, please cite our paper: 'Towards clinically applicable automated aneurysm detection in TOF-MRA: weak labels, anatomical knowledge, and open data' arXiv preprint arXiv:2103.06168v3 (2021).

Ethics Approvals

How To Cite

Copy
Tommaso Di Noto and Guillaume Marie and Sebastien Tourbier and Yasser Alemán-Gómez and Oscar Esteban and Guillaume Saliou and Meritxell Bach Cuadra and Patric Hagmann and Jonas Richiardi (2021). Lausanne_TOF-MRA_Aneurysm_Cohort. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003821.v1.0.0
More citation info

Dataset File Tree

Git Hash: 7ca0628 

BIDS Validation

We found 1 Warning in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-000/ses-20101230/anat/sub-000_ses-20101230_T1w.nii.gz

The most common set of dimensions is: 30,512,512 (voxels), This file has the dimensions: 37,420,448 (voxels).

/sub-001/ses-20101230/anat/sub-001_ses-20101230_T1w.nii.gz

The most common resolution is: 3.90mm x 0.56mm x 0.56mm, This file has the resolution: 4.40mm x 0.45mm x 0.45mm.

/sub-001/ses-20101230/anat/sub-001_ses-20101230_angio.nii.gz

The most common set of dimensions is: 350,448,160 (voxels), This file has the dimensions: 384,512,90 (voxels). The most common resolution is: 0.47mm x 0.47mm x 0.70mm, This file has the resolution: 0.39mm x 0.39mm x 1.00mm.

/sub-002/ses-20101230/anat/sub-002_ses-20101230_T1w.nii.gz

The most common set of dimensions is: 30,512,512 (voxels), This file has the dimensions: 35,420,448 (voxels).

/sub-006/ses-20101227/anat/sub-006_ses-20101227_T1w.nii.gz

The most common set of dimensions is: 25,512,512 (voxels), This file has the dimensions: 35,420,448 (voxels). The most common resolution is: 6.00mm x 0.47mm x 0.47mm, This file has the resolution: 3.90mm x 0.56mm x 0.56mm.

/sub-006/ses-20101227/anat/sub-006_ses-20101227_angio.nii.gz

The most common set of dimensions is: 384,512,90 (voxels), This file has the dimensions: 350,448,144 (voxels). The most common resolution is: 0.39mm x 0.39mm x 1.00mm, This file has the resolution: 0.47mm x 0.47mm x 0.70mm.

/sub-107/ses-20100218/anat/sub-107_ses-20100218_T1w.nii.gz

The most common set of dimensions is: 35,420,448 (voxels), This file has the dimensions: 35,784,784 (voxels). The most common resolution is: 3.90mm x 0.56mm x 0.56mm, This file has the resolution: 4.00mm x 0.29mm x 0.29mm.

/sub-107/ses-20100218/anat/sub-107_ses-20100218_angio.nii.gz

The most common set of dimensions is: 352,448,160 (voxels), This file has the dimensions: 512,512,140 (voxels). The most common resolution is: 0.47mm x 0.47mm x 0.70mm, This file has the resolution: 0.41mm x 0.41mm x 0.55mm.

/sub-120/ses-20100112/anat/sub-120_ses-20100112_T1w.nii.gz

The most common set of dimensions is: 35,420,448 (voxels), This file has the dimensions: 35,784,784 (voxels). The most common resolution is: 3.90mm x 0.56mm x 0.56mm, This file has the resolution: 4.00mm x 0.29mm x 0.29mm.

/sub-120/ses-20100112/anat/sub-120_ses-20100112_angio.nii.gz

The most common set of dimensions is: 350,448,160 (voxels), This file has the dimensions: 512,512,140 (voxels). The most common resolution is: 0.51mm x 0.51mm x 0.70mm, This file has the resolution: 0.41mm x 0.41mm x 0.55mm.

and 67 more files

Dataset File Tree

Git Hash: 7ca0628 

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