David R. Maffitt

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)—an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.

Collecting 48,000 CT exams for the lung screening study of the National Lung Screening Trial.

From 2002–2004, the Lung Screening Study (LSS) of the National Lung Screening Trial (NLST) enrolled 34,614 participants, aged 55–74 years, at increased risk for lung cancer due to heavy cigarette smoking. Participants, randomized to standard chest X-ray (CXR) or computed tomography (CT) arms at ten screening centers, received up to three imaging screens for lung cancer at annual intervals. Participant medical histories and radiologist-interpreted screening results were transmitted to the LSS coordinating center, while all images were retained at local screening centers. From 2005–2007, all CT exams were uniformly de-identified and delivered to a central repository, the CT Image Library (CTIL), on external hard drives (94%) or CD/DVD (5.9%), or over a secure Internet connection (0.1%). Of 48,723 CT screens performed, only 176 (0.3%) were unavailable (lost, corrupted, compressed) while 48,547 (99.7%) were delivered to the CTIL. Described here is the experience organizing, implementing, and adapting the clinical-trial workflow surrounding the image retrieval, de-identification, delivery, and archiving of available LSS–NLST CT exams for the CTIL, together with the quality assurance procedures associated with those collection tasks. This collection of CT exams, obtained in a specific, well-defined participant population under a common protocol at evenly spaced intervals, and its attending demographic and clinical information, are now available to lung-disease investigators and developers of computer-aided-diagnosis algorithms. The approach to large scale, multi-center trial CT image collection detailed here may serve as a useful model, while the experience reported should be valuable in the planning and execution of future equivalent endeavors.

Creation of a CT Image Library for the Lung Screening Study of the National Lung Screening Trial

The CT Image Library (CTIL) of the Lung Screening Study (LSS) network of the National Lung Screening Trial (NLST) consists of up to three annual screens using CT imaging from each of 17,308 participants with a significant history of smoking but no evidence of cancer at trial enrollment (Fall 2002–Spring 2004). Screens performed at numerous medical centers associated with 10 LSS-NLST screening centers are deidentified of protected health information and delivered to the CTIL via DVD, external hard disk, or Internet/Virtual Private Network transmission. The collection will be completed in late 2006. The CTIL is of potential interest to clinical researchers and software developers of nodule detection algorithms. Its attractiveness lies in its very specific, well-defined patient population, scanned via a common CT protocol, and in its collection of evenly spaced serial screens. In this work, we describe the technical details of the CTIL collection process from screening center retrieval through library storage.

Workstation acquisition node for multicenter imaging studies

This site has a contrast as a central data collection and image analysis center for a multilayer study involving four acquisition sites. Magnetic Resonance and Ultrasound studies are to be acquired at the sites and then transmitted via the Internet to the data collection center. This paper will describe the software architecture of a workstation designed to act as a store and forward node at a remote site. The software receives and stores images in DICOM format on the local hard drive. The workstation provides several different mechanisms for removing local identifying patient information and inserting patient and study identifiers which are specific to the multicenter study. After removing or modifying header information, the user may enqueue the data for transmission to the central repository.

De-identification of Medical Images with Retention of Scientific Research Value.

Online public repositories for sharing research data allow investigators to validate existing research or perform secondary research without the expense of collecting new data. Patient data made publicly available through such repositories may constitute a breach of personally identifiable information if not properly de-identified. Imaging data are especially at risk because some intricacies of the Digital Imaging and Communications in Medicine (DICOM) format are not widely understood by researchers. If imaging data still containing protected health information (PHI) were released through a public repository, a number of different parties could be held liable, including the original researcher who collected and submitted the data, the original researchers institution, and the organization managing the repository. To minimize these risks through proper de-identification of image data, one must understand what PHI exists and where that PHI resides, and one must have the tools to remove PHI without compromising the scientific integrity of the data. DICOM public elements are defined by the DICOM Standard. Modality vendors use private elements to encode acquisition parameters that are not yet defined by the DICOM Standard, or the vendor may not have updated an existing software product after DICOM defined new public elements. Because private elements are not standardized, a common de-identification practice is to delete all private elements, removing scientifically useful data as well as PHI. Researchers and publishers of imaging data can use the tools and process described in this article to de-identify DICOM images according to current best practices.

A Query Tool for Investigator Access to the Data and Images of the National Lung Screening Trial.

The National Cancer Institute (NCI), in conjunction with blinded university, provides a mechanism to enable public access to the study data, CT radiology images, and pathology images from the National Lung Screening Trial (NLST). Access to the data and images is through the NCI-sponsored, blinded university-hosted The Cancer Imaging Archive (TCIA), a repository of more than 40 study collections of cancer images. Once access to the NLST data has been granted by NCI, a Query Tool within TCIA is used to access the NLST data and images. The Query Tool is a simple-to-use menu-driven database application designed to quickly pose queries and retrieve/save results (from 53,452 NLST participants), download CT images (~20 million available), and view pathology images (~1200 available). NLST study data are contained in 17 Query Tool tables with ~370 variables to query. This paper describes Query Tool design, functionality, and usefulness for researchers, clinicians, and software developers to query data, save query results, and download/view images.

Membranes, mitochondria and amoebae: shape models

Most real-world shapes and images are characterized by high variability- they are not rigid, like crystals, for example—but they are strongly structured. Therefore, a fundamental task in the understanding and analysis of such image ensembles is the construction of models that incorporate both variability and structure in a mathematically precise way. The global shape models introduced in Grenanders general pattern theory are intended to do this. In this paper, we describe the representation of two-dimensional mitochondria and membranes in electron microscope photographs, and three-dimensional amoebae in optical sectioning microscopy. There are three kinds of variability to all of these patterns, which these representations accommodate. The first is the variability in shape and viewing orientation. For this, the typical structure is represented via linear, circular and spherical templates, with the variability accomodated via the application of transformations applied to the templates. The transformations...

Regulatory compliance requirements for an open source electronic image trial management system

There is a global need for software to manage imaging based clinical trials to speed basic research and drug development. Such a system must comply with regulatory requirements. The U.S. Food and Drug Administration (FDA) has regulations regarding software development process controls and data provenance tracking. A key unanswered problem is the identification of which data changes are significant given a workflow model for image trial management. We report on the results of our study of provenance tracking requirements and define an architecture and software development process that meets U.S. regulatory requirements using open source software components.

Estimation of allele frequencies from color-multiplexed electropherograms

Parametric model fitting of unprocessed sequencing-gel trace data and a least-squares optimization algorithm provide a method for accurately determiniug allele freqnenties of single nucleotide substitutions in a population. The method uses trace data corn two homozygous individuals and from either a heterozygous individual or a mixed population of templates. A parametric model is fit to each of the traces to estimate the amount of each of the four flnorescent. dyes that is present at each site. The parameters estimated from each trace are then normalized to account, for scalar variations due to differences in the amount of sample loaded. The parameters estimated from the trace of the heterozygous individual or from the mixture are viewed as a weighted sum of the parameters estimated from the traces of the homozygous individuals. The weights, or allele freqnenties, are estimated by mhhizing the sum of squared errors between the linear combination of homozygous traces and the mixed trace. Comparison of allele frequencies estimated by our method to known frequencies at polymorphic sites in three pools of CEPH individuals show that our method is accurate. Our method is automatic and much less laborintensive than previous approaches.