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Summary
Lack of access to projection data from patient CT scans is a major limitation for the development and validation of new CT reconstruction algorithms. However, patient projection data acquired using commercial CT scanners are stored in a proprietary format, requiring investigators to have research agreements in place with the CT manufacturer to be able to read the data correctly. These proprietary data formats for projection data, making it unlikely that investigators will be able to validate their algorithm on data from more than one manufacturer.
To meet this critical need, we are building , researchers at the Mayo Clinic, with funding from the National Institute of Biomedical Imaging and Bioengineering (EB 017185), have built a library of CT patient projection data in an open and vendor-neutral format. This format, referred to as DICOM-CT-PD, which is an extended DICOM format that contains sinogram CT projection data , and acquisition geometry, patient information, and pathology identification. The library consists of scans of various types, including head scans, chest scans, abdomen scans, electrocardiogram (ECG)-gated scans, and dual-energy scans. For each scan, three types of data are provided, including DICOM-CT-PD projection data at various dose levels, reconstructed CT images, and a free-form text file. Several instructional documents are provided to help the users extract information from DICOM-CT-PD files, including a dictionary file for the DICOM-CT-PD format, a DICOM-CT-PD reader, and a user manual. Radiologist detection performance based on the reconstructed CT images is also provided. So far 150 head cases, 150 chest cases, and 150 abdomen cases have been collected for potential inclusion. The final library will include a selection of 50 head, chest, and abdomen scans each from at least two different manufacturers, and a few ECG-gated scans and dual-source, dual-energy scans. It will be freely available to academic researchers, and is expected to greatly facilitate the development and validation of CT reconstruction algorithms.
Projection data from patient CT scans, especially those with known pathology, are essential to the development and validation of new reconstruction algorithms. However, the patient projection data collected from commercial CT scanners are proprietary, which means researchers need research agreements with CT vendors to access the data; the projection data collected from commercial CT scanners are also vendor-specific, which means each CT vendor stores the projection data in its own format (the geometries used to store the projection images and the acquisition parameters, the unit used to store the acquisition parameters, and the precision and range of the numerical values might all differ from vendor to vendor).
To allow more researchers to access patient CT projection data with minimum efforts, a library of CT patient projection data is being built and will be freely available to academic researchers. The projection data in the library were decoded from commercial CT scans, and have been converted into an open and standard format.
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Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
de-identified patient projection data in the library were decoded with help of the manufacturer and have been converted into an open and standard format.
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Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- Mayo Clinic, Rochester, MN, with special thanks to David DeLone, MD, Jeff Fidler, MD, and David Levin, MD, from the Department of Radiology, who reviewed all Siemens cases and provided annotated data of identified pathology.
- Mayo Clinic Arizona, Scottsdale, AZ, with special thanks to Amy Hara, MD, from the Department of Radiology, who with her colleagues reviewed all GE cases and provided annotated data of identified pathology.
- University of Maryland school of Medicine, Baltimore, MD, with special thanks to Charles White, MD, from the Department of Radiology, who with his colleagues reviewed all Philips cases and provided annotated data of identified pathology.
We also acknowledge the following individuals who assisted in converting their company’s proprietary data format into DICOM-CT-PD:
- Karl Stierstorfer, PhD, and Thomas Flohr, PhD, from Siemens Healthcare, Forchheim, Germany.
- Jiang Hsieh, PhD, from GE Healthcare, Waukesha, WI.
- Noel Black, PhD from Philips Healthcare, Cleveland, OH, and Ewald Roessl, PhD, from Philips Research Laboratories, Hamburg, Germany.
This work would not have been possible without the support and assistance of many additional individuals.
From the NIBIB, these include:
- Roderic Pettigrew, M.D., Ph.D., for his vision for and support of the sub-mSv CT U01 program.
- William Heetderks, M.D., Ph.D.; Krishna Kandarpa, M.D., Ph.D.; and Jill Heemskerk, Ph.D. for their ongoing support of and interest in this initiative
- The dedicated and helpful program officers for this award: Hector Lopez, Ph.D., Anthony Sastre, Ph.D., and Behrouz Shabestari , Ph.D.
We have greatly valued the insightful comments and helpful suggestions from the external advisory committee and consultants from Mayo Clinic’s sub-mSv CT U01 award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) (EB 017185), the other NIBIB sub-mSv CT U01 recipients, and additional consultants, including:
- Norbert Pelc, Sc.D. (Stanford University)
- Mark Baker, M.D. (Cleveland Clinic)
- Kyle Meyers, Ph.D. (U.S. Food and Drug Administration)
- Matthew Kupinski, Ph.D. (University of Arizona)
- Alicia Toledano, Ph.D. (Biostatistics Consulting, LLC)
- Peter Edic, Ph.D. (General Electric Global Research)
- Ge Wang, Ph.D. (Rensselaer Polytechnic Institute)
- Hengyong Yu, Ph.D. (University of Massachusetts Lowell)
- Daniel Sodickson, M.D., Ph.D. (New York University Langone Health)
- Aaron Sodickson, M.D., Ph.D. (Brigham and Women’s Hospital)
- Ricardo Otazo, M.D. (Memorial Sloan Kettering)
- Webster Stayman, Ph.D. (Johns Hopkins University)
- Grace Gang, Ph.D. (Johns Hopkins University)
- Reuven Levinson (Philips Healthcare)
- Jeffrey Fessler, Ph.D. (University of Michigan)
- David Clunie, MBBS (PixelMed Publishing)
From the University of Arkansas for Medical Sciences, host of The Cancer Imaging Archive,
- Geri Blake
- William Bennett
- Justin Kirby
- Kirk Smith
From Mayo Clinic’s CT Clinical Innovation Center, located within the Department of Radiology, this work would not have been possible without the skill, perseverance, and can-do attitude of the Mayo research team, including faculty, trainees, and support staff who worked together across every aspect of this project. Sincere gratitude is expressed to each of the following individuals:
- Cynthia McCollough, Ph.D.
- Joel Fletcher, Ph.D.
- David Holmes, III, Ph.D.
- Shuai Leng, Ph.D.
- Lifeng Yu, Ph.D.
- Rickey Carter, Ph.D.
- Baiyu Chen, Ph.D.
- Xinhui Duan, Ph.D.
- Kyle McMillan, Ph.D.
- Hao Gong, Ph.D.
- Gregory Michalak, Ph.D.
- Liqiang Ren, Ph.D.
- Tammy Drees
- Maria Shiung
Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
- Continue with any names from additional submitting sites if collection consists of more that one.
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