Search

PURPOSE Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community METHODS A modular technology platform is presented that would allow existing data resources, such as TCIA, to evolve into a comprehensive data resource that meets the needs of users engaged in translational research for imaging-based precision medicine. This Platform for Imaging in Precision Medicine (PRISM) helps streamline the deployment and improve TCIA’s efficiency and sustainability. More importantly, its inherent modular architecture facilitates a piecemeal adoption by other data repositories. RESULTS PRISM includes services for managing radiology and pathology images and features and associated clinical data. A semantic layer is being built to help users explore diverse collections and pool data sets to create specialized cohorts. PRISM includes tools for image curation and de-identification. It includes image visualization and feature exploration tools. The entire platform is distributed as a series of containerized microservices with representational state transfer interfaces. CONCLUSION PRISM is helping modernize, scale, and sustain the technology stack that powers TCIA. Repositories can take advantage of individual PRISM services such as de-identification and quality control. PRISM is helping scale image informatics for cancer research at a time when the size, complexity, and demands to integrate image data with other precision medicine data-intensive commons are mounting.

OBJECTIVE. As health care moves into a new era of increasing information vulnerability, radiologists should understand that they may be using systems that are exposed to altered data or data that contain malicious elements. This article explains the vulnerabilities of DICOM images and discusses requirements to properly secure these images from cyberattacks. CONCLUSION. There is an important need to properly secure DICOM images from attacks and tampering. The solutions described in this article will go a long way to achieving this goal.

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use., Measurement(s) Deidentification • Clinical Data Technology Type(s) data synthesis • digital curation Factor Type(s) imaging type Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14802774

PURPOSE We summarize the AAPM TG248 Task Group report on interoperability assessment for the commissioning of medical imaging acquisition systems in order to bring needed attention to the value and role of quality assurance testing throughout the imaging chain. METHODS To guide the clinical physicist involved in commissioning of imaging systems, we describe a framework and tools for incorporating interoperability assessment into imaging equipment commissioning. RESULTS While equipment commissioning may coincide with equipment acceptance testing, its scope may extend beyond validation of product or purchase specifications. Equipment commissioning is meant to provide assurance that a system is ready for clinical use, and system interoperability plays an essential role in the clinical use of an imaging system. CONCLUSION The functionality of a diagnostic imaging system extends beyond the acquisition console and depends on interoperability with a host of other systems such as the Radiology Information System, a Picture Archive and Communication System, post-processing software, treatment planning software, and clinical viewers.

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 researcher's 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.

The Cancer Imaging Archive (TCIA) is the U.S. National Cancer Institute’s repository for cancer imaging and related information. TCIA contains 30.9 million radiology images representing data collected from approximately 37,568 subjects. This data is organized into collections by tumor-type with many collections also including analytic results or clinical data. TCIA staff carefully de-identify and curate all incoming collections prior to making the information available via web browser or programmatic interfaces. Each published collection within TCIA is assigned a Digital Object Identifier that references the collection. Additionally, researchers who use TCIA data may publish the subset of information used in their analysis by requesting a TCIA generated Digital Object Identifier. This data descriptor is a review of a selected subset of existing publicly available TCIA collections. It outlines the curation and publication methods employed by TCIA and makes available 15 collections of cancer imaging data.

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.

Reusable, publicly available data is a pillar of open science. The Cancer Imaging Archive (TCIA) is an open image archive service supporting cancer research. TCIA collects, de-identifies, curates and manages rich collections of oncology image data. Image data sets have been contributed by 28 institutions and additional image collections are underway. Since June of 2011, more than 2,000 users have registered to search and access data from this freely available resource. TCIA encourages and supports cancer-related open science communities by hosting and managing the image archive, providing project wiki space and searchable metadata repositories. The success of TCIA is measured by the number of active research projects it enables (>40) and the number of scientific publications and presentations that are produced using data from TCIA collections (39).

Title XIII of Division A and Title IV of Division B of the American Recovery and Reinvestment Act (ARRA) of 2009 [1] include a provision commonly referred to as the "Health Information Technology for Economic and Clinical Health Act" or "HITECH Act" that is intended to promote the electronic exchange of health information to improve the quality of health care. Subtitle D of the HITECH Act includes key amendments to strengthen the privacy and security regulations issued under the Health Insurance Portability and Accountability Act (HIPAA). The HITECH act also states that "the National Coordinator" must consult with the National Institute of Standards and Technology (NIST) in determining what standards are to be applied and enforced for compliance with HIPAA. This has led to speculation that NIST will recommend that the government impose the Federal Information Security Management Act (FISMA) [2], which was created by NIST for application within the federal government, as requirements to the public Electronic Health Records (EHR) community in the USA. In this paper we will describe potential impacts of FISMA on medical image sharing strategies such as teleradiology and outline how a strict application of FISMA or FISMA-based regulations could have significant negative impacts on information sharing between care providers.

Purpose: Lack of reproducibility in scientific research, particularly in healthcare, has become an increasing problem in recent years. This is especially important in the emerging field of radiomics/radiogenomics where large data sets and huge numbers of feature variables lead to an increased risk of spurious correlations which are not actually driven by biology. To address this problem we have developed an open-access database called The Cancer Imaging Archive (TCIA) which allows researchers to share the original image data necessary to accurately compare and validate research methods. Methods: Scalable processes for data de-identification were developed which leverage DICOM (PS 3.15 Annex E) standards to ensure compliance with HIPAA regulations. Submission software is customized for each new data set and a team of trained experts assist submitters with the upload process. Data are organized into collections related to a particular cancer type, modality, or research question. Collections may be open-access or restricted to specific users. Users may browse and download the data via their web browser or using programmatic interfaces. Digital object identifiers can be created to allow easy re-use of data or citations in related publications. A helpdesk is available to answer questions from TCIA users. Results: TCIA contains over 52 data collections (46 publicly accessible) for a total of 26.5 million images and associated data. Sixteen of the TCIA data collections support radiogenomics research by linking imaging from subjects with genomic, clinical and pathology data that are available on The Cancer Genome Atlas data portal or Gene Expression Omnibus. Approximately 3,000 users visit the site monthly. More than 90 manuscripts and 12 DOIs have been published relating to TCIA data. Conclusion: TCIA provides a wealth of high-value imaging data to the imaging research community, as well as comprehensive services required to support its user community and facilitate continued growth. Funded by NCI Contract No. HHSN261200800001E

The Cancer Bioinformatics Grid (caBIG™) program was created by the National Cancer Institute to facilitate sharing of IT infrastructure, data, and applications among the National Cancer Institute-sponsored cancer research centers. The program was launched in February 2004 and now links more than 50 cancer centers. In April 2005, the In Vivo Imaging Workspace was added to promote the use of imaging in cancer clinical trials. At the inaugural meeting, four special interest groups (SIGs) were established. The Software SIG was charged with identifying projects that focus on open-source software for image visualization and analysis. To date, two projects have been defined by the Software SIG. The eXtensible Imaging Platform project has produced a rapid application development environment that researchers may use to create targeted workflows customized for specific research projects. The Algorithm Validation Tools project will provide a set of tools and data structures that will be used to capture measurement information and associated needed to allow a gold standard to be defined for the given database against which change analysis algorithms can be tested. Through these and future efforts, the caBIG™ In Vivo Imaging Workspace Software SIG endeavors to advance imaging informatics and provide new open-source software tools to advance cancer research.

For the case of radiographic contrast media flowing through an otherwise stationary object, a theoretical analysis shows that image enhancement with optimal dose utilization can be achieved by varying the x-ray intensity during the sequence of exposures. The need for variable intensity derives from the conflicting requirements of continuous visualization of the time course of the contrast media and the fact that dose utilization in a difference image is maximized when all the x-ray photons are divided between minimum and maximum opacification. A set of optimal weights for combining multiple images are derived as a function of the x-ray intensity in each image and the effects of photon noise, camera (video system) noise, and digital truncation noise. It is shown theoretically that controlled variation of x-ray intensity may improve the signal to noise to dose ratio by approximately a factor of 2 when compared to conventional matched filtering. If the x-ray intensity remains constant for all images, the optimal filter weights reduce to those of the expected matched filter. A simple implementation of optimal filtering using only two intensities is described mathematically and demonstrated experimentally.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A system is being developed to test the possibility of doing peripheral, digital subtraction angiography (DSA) with a single contrast injection using a moving gantry system. Given repositioning errors that occur between the mask and contrast-containing images, factors affecting the success of subtractions following image registration have been investigated theoretically and experimentally. For a 1 mm gantry displacement, parallax and geometric image distortion (pin-cushion) both give subtraction errors following registration that are approximately 25% of the error resulting from no registration. Image processing techniques improve the subtractions. The geometric distortion effect is reduced using a piece-wise, 8 parameter unwarping method. Plots of image similarity measures versus pixel shift are well behaved and well fit by a parabola, leading to the development of an iterative, automatic registration algorithm that uses parabolic prediction of the new minimum. The registration algorithm converges quickly (less than 1 second on a MicroVAX) and is relatively immune to the region of interest (ROI) selected.

We are investigating the technical feasibility of a novel acquisition scheme for peripheral angiography that consists of taking images from a gantry that continuously sweeps back-and-forth while the contrast is flowing. The clinical advantage of such a multiple-pass system is that images at each position are spread over a longer time interval, increasing the chance of imaging contrast filled arteries and thus decreasing the need for retakes. Image acquisition during rapid sweeping is technically feasible. The duration of x-ray pulses is short enough to reduce the extent of motion blurring to less than one pixel, using mA and kV parameters available on our angiography system. Contrast and mask image pair superpositioning is excellent, permitting DSA processing.