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Lung Image Database
Charles R. Meyer, Ph.D.
University of Michigan
Grant Number: U01CA091099
This grant application was awarded in response to RFA CA-01-001, the Lung Image Database Consortium (LIDC) Resource for Imaging Research. As a member of the LIDC will participate in formulating the multi-institutional lung imaging database acquisition and quality control specification, and begin collecting cases and populating a local database according to specifications resulting from the multi-institutional development of consensus guidelines.
Our clinical collaborators at the University of Michigan participating in this project have already had significant experience recruiting lung patients for another lung database project, the National Emphysema Treatment Trials (NETT). In this project Michigan ranked second in the number of patients screened for the study, and first in the enrollment of patients that passed the screen.
The Department of Radiology and the University Hospitals are committed to the acquisition of new generation CT and PET scanners over the duration of the LICD project. In direct support of the goals of a previously funded P01 as well as those of the LIDC, we have purchased a 4-CPU PowerEdge Dell server running RedHat Linux, configured with over 0.4 TB of RAID storage, all running on an uninterruptible power supply. The Raid and system disks are Past Funding Opportunities onto a LTO tape via ARCserveIT backup software. We have tested the installation of the Apache web server, PHP scripting language, and MySQL database. The system also supports the execution of AVS5, an application development environment for the manipulation and visualization of 3D data.
For the LIDC project we will implement the scrubbing of DICOM headers of unique patient identifiers, the population of a SQL database, and storage of associated CT datasets. Appropriate security and encryption has been addressed as well. The LIDC database will be available for public sharing through direct Internet access from our lab. The user web-interface will support identification of subsets of CT scans using SQL that may be downloaded for training/testing of CAD algorithms.
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