Summary
This data set consists of 392 high resolution (1.5mm axial slice thickness) GammaKnife MR/CT pairs collected concurrently. Using the SRS frame, the T1-MR images were rigidly registered to the corresponding CTs. The data set is approximately equal parts healthy patients (trigeminal neuralgia or acoustic neuroma) and patients with metastatic disease. Cases were excluded for anatomic altering lesions (i.e. meningiomas) or tumors nearby the hippocampus. Contours of the left and right hippocampus were created by the researchers following the RTOG 0933 protocol and a head contour was created to be used to mask out the SRS frame.
The dataset also includes:
- MR Sequences: Pregadolinium (Sagittal T1, axial multiecho, axial FLAIR, axial MPR) or Postgadolinium (Axial T1, axial MPR) or T2 MRI for benign cases
- Clinical data: Tumor primaries or benign diagnosis (TGN, Acoustic neuroma)
- GammaKnife treatment planning contours. Although we just upgraded our GammaKnife system so I am not certain all will be available.
- Deep learning generated contours of the hippocampus from this publication: https://doi.org/10.1002/mp.14098
The high-resolution MR/CT image pairs allows researchers to investigate segmentation of the hippocampus, and to contour additional structures to expand upon the availability of cranial segmentation data.
Additional Information
All MR images designated as ‘Co-registered to CT’ in the Series Description were interpolated to the CT frame of reference, potentially altering the field of view or voxel resolution. For all co-registered MR images, the original primary images and registration files are provided for use as needed.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Beaumont Research Institute
Data Access
| Data Type | Download all or Query/Filter |
|---|---|
Images, Segmentations, and Radiation Therapy Structures/Doses/Plans (DICOM, XX.X GB) << latter two items only if DICOM SEG/RTSTRUCT/RTDOSE/PLAN exist >> | (Download requires the NBIA Data Retriever) |
| Tissue Slide Images (SVS, XX.X GB) | |
| Clinical data (CSV) | |
| Genomics (web) |
Click the Versions tab for more info about data releases.
Please contact help@cancerimagingarchive.net with any questions regarding usage.
Detailed Description
Image Statistics | |
|---|---|
Modalities | |
Number of Patients | 392 |
Number of Studies | |
Number of Series | |
Number of Images | |
| Images Size (GB) |
<< Add any additional information as needed below. Likely would be something from site. >>
We have rigidly registered all MR images to the CT images
The submission data consists of 390 patient cases sourced from Beamont's Gamma Knife center. All images have a cranial field of view. Of the cases, 191 patients were treated for trigeminal neuralgia and 199 patients were treated for brain metastases (4 to 26 per patient).
For each patient, there is one (1) MR Imaging study, one (1) CT imaging study and one (1) Radiotherapy structure set (RTSTRUCT) consisting of contours generated by researchers. Both images represent a single time point and were acquired less than an hour apart. Each file is encoded in the DICOM standard. The data set is stored in the MIM Software clinical database.
They usually anonymize the data using MIM on their end. Justin asked if we can get a copy of the original data prior to that step. Evan replied,
"I included that statement to comply with our institutional policy to not allow PHI off premise. But, seeing that the TCIA submission process anonymizes on-site, I see no reason why I would need to first use MIM (or similar software) prior to using the TCIA submission process. So, to answer your question, we intend to work with the curators to submit the unadulterated data."
Citations & Data Usage Policy
Data Citation
DOI goes here. Create using Datacite with information from Collection Approval form
Publication Citation
Porter, E., Fuentes, P., Siddiqui, Z., Thompson, A., Levitin, R., Solis, D., Myziuk, N., & Guerrero, T. (2020). Hippocampus segmentation on noncontrast CT using deep learning. Medical Physics, 47(7), 2950–2961. https://doi.org/10.1002/mp.14098
Acknowledgement
Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal.
TCIA Citation
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7
Other Publications Using This Data
TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.
Version X (Current): Updated yyyy/mm/dd
| Data Type | Download all or Query/Filter |
|---|---|
| Images (DICOM, xx.x GB) | (Requires NBIA Data Retriever.) |
| Clinical Data (CSV) | Link |
| Other (format) |
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