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  • Synthetic and Phantom MR Images for Determining Deformable Image Registration Accuracy (MRI-DIR)
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Summary

Two sets of images were created to evaluate deformable image registration accuracy. The first set contains CT, T1-, and T2-weighted images from a porcine phantom. The phantom was implanted with ten 0.35 mm gold markers and then immobilized in a plastic container with movable dividers. The porcine phantom was compressed in 4 different ways and images were acquired in each position. The markers were visible on the CT scans but not the MR scans due to the selected voxel size. Therefore, the markers do not interfere with the registration between MR images and the marker locations can be obtained from the CT images to determine accuracy. The second set of images are synthetic images derived from 28 head and neck squamous cell carcinoma patients who had pre-, mid-, and post-radiotherapy treatment MR scans. From these patients, inter- and intra-patient models were created. Four synthetic pre-treatment images were created by using the inter-patient model on a selected template patient. Four synthetic post-treatment images were created for each synthetic pre-treatment image using the intra-patient model.

Rachel B. Ger, Jinzhong Yang, Yao Ding, Megan C. Jacobsen, Carlos E. Cardenas, Clifton D. Fuller, Rebecca M. Howell, Heng Li, R. Jason Stafford, Shouhao Zhou, Laurence E. Court


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Data Access

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Data TypeDownload all or Query/Filter
Images (DICOM, 1.4 GB)

 

Deformation Vector Fields (MATLAB)

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Detailed Description

Collection Statistics

 

Modalities

CT, MR, RTSTRUCT, Matlab

Number of Patients

9

Number of Studies

25

Number of Series

61

Number of Images

3596

Image Size (GB)1.4

Supporting Documentation

In addition to the DICOM images in TCIA,

Citations & Data Usage Policy 

 This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License.  See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.

Please be sure to include the following citations in your work if you use this data set:

Data Citation

Rachel B. Ger, Jinzhong Yang, Yao Ding, Megan C. Jacobsen, Carlos E. Cardenas, Clifton D. Fuller, Rebecca M. Howell, Heng Li, R. Jason Stafford, Shouhao Zhou, Laurence E. Court (2018). Data from Synthetic and Phantom MR Images for Determining Deformable Image Registration Accuracy (MRI-DIR). The Cancer Imaging Archive. http://dx.doi.org/10.7937/K9/TCIA.2018.3f08iejt

Publication Citation

Coming soon

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. (paper)

Other Publications Using This Data

TCIA maintains a list of publications which leverage our data. At this time we are not aware of any publications based on this data. If you have a publication you'd like to add please contact the TCIA Helpdesk.

 

Version 1 (Current): Updated 2018/06/30

Data TypeDownload all or Query/Filter
Images (DICOM,  1.4 GB)

 

Deformation Vector Fields (MATLAB)

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