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Image archive of CT lung cancer patients followed during treatment ('RIDER' acronym stands for "Reference Image Database to Evaluate Response"). Each case consists of full-chest DICOM CT’s exams at multiple time-intervals during the course of a patient’s illness and therapy. The collection is a resource for developing and testing CAD 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.
Download: RIDER Database Resource: Plans for a Public-Private Partnership: White Paper
RIDER Database Resource: Plans for a Public-Private Partnership: Executive Summary
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This collection is a large portion of a colon polyp screening trial of CT colonography * and contains: original DICOM CT image studies (aka: 'Virtual Colonoscopy, or VC"), radiology reports, optical colonoscopy reports with jpeg optical endoscopic images, movie files (in *.avi format) of the optical exam, and pathology report of removed polyps – though not all 'annotation' files are available for each CT case (but all case data are abstracted in the downloadable MSAccess file – see following). This image archive is a resource for CAD developers to advance VC visualization and polyp detection. The 800+ cases are drawn from a Dept of Defense-sponsored study published by Pickhardt, et al. in 2003 of 1233 asymptomatic adults. It reported a sensitivity of 93.8% for VC detection of polyps >10mm in size. CT virtual colonoscopy (VC) was promptly followed by conventional endoscopic colonoscopy (OC) - see publication, N Engl J Med 2003;349:2191-200, for full description of trial methodology. The patients underwent standard 24-hour colonic preparation by oral administration of 90 ml of sodium phosphate and 10 mg of bisacodyl; then consumed 500 ml of barium (2.1 percent by weight) for solid-stool tagging and 120 ml of Gastrografin to opacify luminal fluid.
To efficiently download only selected DICOM cases by querying the results data (e.g. cases with certain sized polyps), an MS Access relational database file is provided that can be downloaded and opened locally to find case subsets of greatest interest.
The MSAccess file can be downloaded by clicking here: DOD_VC.mdb
*This collection is made available from the Walter Reed Army Medical Center Virtual Colonoscopy Collection in collaboration with National Cancer Institute, NIH: If you use this 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.
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The LIDC image collection consists of low-dose helical CT scans with marked-up annotated lesions from adults screened for lung cancer. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis.
For more information click on the following links:
LIDC Program: https://webarchive.library.unt.edu/eot2008/20090131211406/http://imaging.cancer.gov/programsandresources/InformationSystems/LIDC
Download: XML File Documentation Annotated XML File
Please note: We recently discovered that, for a subset of cases, inconsistent rating systems were used among the 5 sites with regard to the spiculation and lobulation characteristics of lesions identified as nodules > 3 mm. We are in the process of correcting this situation. The XML nodule characteristics data as it exists for some cases will be impacted by this error.
LIDC Radiologist Instructions for Spatial Location and Extent Estimates
LIDC Publications Policy
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20 PET/CT phantom scan collections carried out under the aegis of the Society of Nuclear Medicine (SNM) to discern the uniformity of clinical imaging instrumentation at various sites. Obtained in cooperation with Society of Nuclear Medicine as a resource for increased quantitative understanding of machine acquisition, analytic reproducibility and image processing.
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PET/CT, MRI, and DCE-MRI image databases along with clinical metadata annotations from selected clinical trials in which imaging is used. The collections will be intended to stimulate research in reliable quantitative imaging as a biomarker for response to therapy.
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