Image and Clinical Data References
Welcome to the NIBIB's image and clinical data reference page. These publicly-available data are generated by clinical trials supported by the NIH or other public-private partnerships.
Below you will find a brief description of each project and data collection. Following the links provided, you can learn more about the project and data set. All projects listed have links to download the images and clinical data. Registration may be required to access the data. In addition, many of the sites provide search and query mechanisms.
We welcome your feedback and suggestions regarding the content on this page including the availability of other publicly-available clinical data with an imaging component. Please send comments/suggestions to: info@nibib.nih.gov.
Contents
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Alzheimer's Disease Neuroimaging Initiative (ADNI)
The ADNI is a 5-year research project (begun on October 1, 2004) to study the rate of change of cognition, brain structure and function, and biomarkers in people with mild cognitive impairment (MCI) and early Alzheimer's disease. It is a $60 million public/private partnership, including Federal agencies (National Insitute on Aging, NIBIB, and the Food and Drug Administration), private companies, and advocacy organizations, to test how serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers (in blood, urine, and CSF), and clinical and neuropsychological assessment can best be combined to measure the progression of disease. More information about the ADNI can be found at: http://adni-info.org.
The overall goal of this project is to identify which biomarkers best track the progress of mild cognitive impairment and Alzheimer's disease, and can be used in clinical trials to increase the efficiency of drug development. Other goals include developing improved methods for clinical trials and providing a large database that will improve the design of treatment trials.
The ADNI includes 200 elderly controls, 400 individuals with mild cognitive impairment, and 200 individuals with Alzheimer's disease. The raw and processed brain imaging and biomarker data are available at: http://www.loni.ucla.edu/ADNI.
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Osteoarthritis Initiative (OAI)
The OAI is a multi-center, longitudinal, prospective observational study of knee osteoarthritis (OA). The overall aim of the OAI is to develop a public domain research resource to facilitate the scientific evaluation of biomarkers for osteoarthritis as potential surrogate endpoints for disease onset and progression. More information about the OAI can be found at: http://www.oai.ucsf.edu/datarelease.
Publicly-available data include:
Cinical
- Baseline questionnaire and exam data for the first half of the cohort (approximately 2,600 participants).
Imaging
- Baseline X-ray and MRI images for a sample of 200 progression and incidence cohort participants.
- Baseline and 12-month X-ray and MRI images for a sample of 160 progression cohort participants.
- Imaging operations manuals: Radiographic (X-ray) Manual (Version 2.1, 8/23/06) and MRI Manual (Version 1.0j, 10/3/06).
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Reference Image Database to Evaluate Response (RIDER)
The Reference Image Database to Evaluate Response (RIDER) to therapy in lung cancer began as a highly leveraged and collaborative pilot project, initiated in September 2004, by the NCI's Cancer Imaging Program, NCI’s Center for Bioinformatics, the NIBIB, the Cancer Prevention and Research Foundation, and with information technology support from the Radiological Society of North America (RSNA). RIDER consists of a computed tomography (CT) image archive of lung cancer patients followed during treatment. Each case consists of full-chest DICOM CT exams at multiple time intervals during the course of a patient's illness and therapy. The collection is a resource for developing and testing computer-aided design software to analyze tumor change at multiple time points as the patient responds to therapy or the disease progresses. Selected cases from this indexed collection (in full DICOM format) can be retrieved and downloaded from the archive by a variety of DICOM-indexed queries. DICOM images and meta data can be found here: http://ncia.nci.nih.gov/collections/.
More information about RIDER can be found here: Rider Database Resource and Plans for a Public-Private Partnership
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Virtual Colonscopy Collection
This collection is a large portion of a colon polyp screening trial of computed tomography (CT) colonography* and contains: original DICOM CT image studies (Virtual Colonscopy) radiology reports, optical colonoscopy reports with optical endoscopic images, movie files of the optical exam, and pathology reports of removed polyps. This image archive is a resource for computer-assisted diagnosis developers to advance virtual colonoscopy visualization and polyp detection. The over 800 cases are drawn from a Department of Defense-sponsored study published in 2003 of 1233 asymptomatic adults which reported a sensitivity of 93.8% for virtual colonoscopic detection of polyps greater than 10 mm in size.
An MS Access relational database is provided that can be downloaded and opened locally to find case subsets of greatest interest. The Virtual Colonoscopy DICOM image data and annotation data can be searched and downloaded here: http://ncia.nci.nih.gov/collections/.
*This collection is made available from the Walter Reed Army Medical Center Virtual Colonoscopy Collection in collaboration with the National Cancer Institute, NIH. If you use data or images from this web site in publications, research manuscripts, proposals, or other technical documents, you must acknowledge that the data has been provided courtesy of Dr. Richard Choi, Virtual Colonoscopy Center, Walter Reed Army Medical Center, Washington, DC.
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Lung Image Database Consortium (LIDC)
The Lung Imaging Database Consortium (LIDC) collection consists of low-dose helical CT scans with annotated lesions from adults screened for lung cancer. It is an Internet-accessible international resource for the development, training, and evaluation of computer-assisted diagnostic methods for lung cancer detection and diagnosis. LIDC image and meta data can be searched and downloaded here: http://ncia.nci.nih.gov/collections/.
For more information about the LIDC Program, visit: http://imaging.cancer.gov/programsandresources/InformationSystems/LIDC.
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International Consortium for Brain Mapping (ICBM)
The International Consortium for Brain Mapping (ICBM) is a new brain atlas and database that uses an anatomically-labeled brain template to help determine variability among populations of brains. All brain images are warped to this target brain so that the images can pick up individual anatomical locations and be appropriately labeled.
A web interface provides the means to query the database using a combination of subject demographics and scan-related attributes. Authorized users may view representations of the data and form collections of datasets that can be downloaded or fed directly into the Pipeline environment for distributed processing and analysis. ICBM and meta data can be found here: http://www.loni.ucla.edu/ICBM/Databases/.
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Last Updated On 10/06/2011