Jasper Lee

A data grid for imaging-based clinical trials

Clinical trials play a crucial role in testing new drugs or devices in modern medicine. Medical imaging has also become an important tool in clinical trials because images provide a unique and fast diagnosis with visual observation and quantitative assessment. A typical imaging-based clinical trial consists of: 1) A well-defined rigorous clinical trial protocol, 2) a radiology core that has a quality control mechanism, a biostatistics component, and a server for storing and distributing data and analysis results; and 3) many field sites that generate and send image studies to the radiology core. As the number of clinical trials increases, it becomes a challenge for a radiology core servicing multiple trials to have a server robust enough to administrate and quickly distribute information to participating radiologists/clinicians worldwide. The Data Grid can satisfy the aforementioned requirements of imaging based clinical trials. In this paper, we present a Data Grid architecture for imaging-based clinical trials. A Data Grid prototype has been implemented in the Image Processing and Informatics (IPI) Laboratory at the University of Southern California to test and evaluate performance in storing trial images and analysis results for a clinical trial. The implementation methodology and evaluation protocol of the Data Grid are presented.

MIDG-Emerging grid technologies for multi-site preclinical molecular imaging research communities

PurposeMolecular imaging is the visualization and identification of specific molecules in anatomy for insight into metabolic pathways, tissue consistency, and tracing of solute transport mechanisms. This paper presents the Molecular Imaging Data Grid (MIDG) which utilizes emerging grid technologies in preclinical molecular imaging to facilitate data sharing and discovery between preclinical molecular imaging facilities and their collaborating investigator institutions to expedite translational sciences research. Grid-enabled archiving, management, and distribution of animal-model imaging datasets help preclinical investigators to monitor, access and share their imaging data remotely, and promote preclinical imaging facilities to share published imaging datasets as resources for new investigators.MethodsThe system architecture of the Molecular Imaging Data Grid is described in a four layer diagram. A data model for preclinical molecular imaging datasets is also presented based on imaging modalities currently used in a molecular imaging center. The MIDG system components and connectivity are presented. And finally, the workflow steps for grid-based archiving, management, and retrieval of preclincial molecular imaging data are described.ResultsInitial performance tests of the Molecular Imaging Data Grid system have been conducted at the USC IPILab using dedicated VMware servers. System connectivity, evaluated datasets, and preliminary results are presented. The results show the system’s feasibility, limitations, direction of future research.ConclusionsTranslational and interdisciplinary research in medicine is increasingly interested in cellular and molecular biology activity at the preclinical levels, utilizing molecular imaging methods on animal models. The task of integrated archiving, management, and distribution of these preclinical molecular imaging datasets at preclinical molecular imaging facilities is challenging due to disparate imaging systems and multiple off-site investigators. A Molecular Imaging Data Grid design, implementation, and initial evaluation is presented to demonstrate the secure and novel data grid solution for sharing preclinical molecular imaging data across the wide-area-network (WAN).

A DICOM-based 2nd generation molecular imaging data grid implementing the IHE XDS-i integration profile

PurposeA Molecular Imaging Data Grid (MIDG) was developed to address current informatics challenges in archival, sharing, search, and distribution of preclinical imaging studies between animal imaging facilities and investigator sites. This manuscript presents a 2nd generation MIDG replacing the Globus Toolkit with a new system architecture that implements the IHE XDS-i integration profile. Implementation and evaluation were conducted using a 3-site interdisciplinary test-bed at the University of Southern California.MethodsThe 2nd generation MIDG design architecture replaces the initial design’s Globus Toolkit with dedicated web services and XML-based messaging for dedicated management and delivery of multi-modality DICOM imaging datasets. The Cross-enterprise Document Sharing for Imaging (XDS-i) integration profile from the field of enterprise radiology informatics was adopted into the MIDG design because streamlined image registration, management, and distribution dataflow are likewise needed in preclinical imaging informatics systems as in enterprise PACS application. Implementation of the MIDG is demonstrated at the University of Southern California Molecular Imaging Center (MIC) and two other sites with specified hardware, software, and network bandwidth.ResultsEvaluation of the MIDG involves data upload, download, and fault-tolerance testing scenarios using multi-modality animal imaging datasets collected at the USC Molecular Imaging Center. The upload, download, and fault-tolerance tests of the MIDG were performed multiple times using 12 collected animal study datasets. Upload and download times demonstrated reproducibility and improved real-world performance. Fault-tolerance tests showed that automated failover between Grid Node Servers has minimal impact on normal download times.ConclusionsBuilding upon the 1st generation concepts and experiences, the 2nd generation MIDG system improves accessibility of disparate animal-model molecular imaging datasets to users outside a molecular imaging facility’s LAN using a new architecture, dataflow, and dedicated DICOM-based management web services. Productivity and efficiency of preclinical research for translational sciences investigators has been further streamlined for multi-center study data registration, management, and distribution.

Design and implementation of a fault-tolerant and dynamic metadata database for clinical trials

In recent imaging-based clinical trials, quantitative image analysis (QIA) and computer-aided diagnosis (CAD) methods are increasing in productivity due to higher resolution imaging capabilities. A radiology core doing clinical trials have been analyzing more treatment methods and there is a growing quantity of metadata that need to be stored and managed. These radiology centers are also collaborating with many off-site imaging field sites and need a way to communicate metadata between one another in a secure infrastructure. Our solution is to implement a data storage grid with a fault-tolerant and dynamic metadata database design to unify metadata from different clinical trial experiments and field sites. Although metadata from images follow the DICOM standard, clinical trials also produce metadata specific to regions-of-interest and quantitative image analysis. We have implemented a data access and integration (DAI) server layer where multiple field sites can access multiple metadata databases in the data grid through a single web-based grid service. The centralization of metadata database management simplifies the task of adding new databases into the grid and also decreases the risk of configuration errors seen in peer-to-peer grids. In this paper, we address the design and implementation of a data grid metadata storage that has fault-tolerance and dynamic integration for imaging-based clinical trials.

Data migration and persistence management in a medical imaging informatics data grid

The Medical Imaging Informatics Data Grid project is an enterprise infrastructure solution developed at the University of Southern California for archiving digital medical images and structured reports. Migration methodology and policies are needed to maintain continuous data availability as data volumes are being copied and/or moved within a data grids multi-site storage devices. In the event a storage device is unavailable, a copy of its contents should be available at a live secondary storage device within the data grid to provide continuous data availability. In the event a storage device within the data grid is running out of space, select data volumes should be moved seamlessly to a tier-2 storage device for long-term storage, without interruption to front-end users. Thus the database and file migration processes involved must not disrupt the existing workflows in the data grid model. This paper discusses the challenges, policies, and protocols required to provide data persistence through data migration in the Medical Imaging Informatics Data Grid.

Assuring image authenticity within a data grid using lossless digital signature embedding and a HIPAA-compliant auditing system

A Data Grid for medical images has been developed at the Image Processing and Informatics Laboratory, USC to provide distribution and fault-tolerant storage of medical imaging studies across Internet2 and public domain. Although back-up policies and grid certificates guarantee privacy and authenticity of grid-access-points, there still lacks a method to guarantee the sensitive DICOM images have not been altered or corrupted during transmission across a public domain. This paper takes steps toward achieving full image transfer security within the Data Grid by utilizing DICOM image authentication and a HIPAA-compliant auditing system. The 3-D lossless digital signature embedding procedure involves a private 64 byte signature that is embedded into each original DICOM image volume, whereby on the receiving end the signature can to be extracted and verified following the DICOM transmission. This digital signature method has also been developed at the IPILab. The HIPAA-Compliant Auditing System (H-CAS) is required to monitor embedding and verification events, and allows monitoring of other grid activity as well. The H-CAS system federates the logs of transmission and authentication events at each grid-access-point and stores it into a HIPAA-compliant database. The auditing toolkit is installed at the local grid-access-point and utilizes Syslog [1], a client-server standard for log messaging over an IP network, to send messages to the H-CAS centralized database. By integrating digital image signatures and centralized logging capabilities, DICOM image integrity within the Medical Imaging and Informatics Data Grid can be monitored and guaranteed without loss to any image quality.

Implementation of an ASP model offsite backup archive for clinical images utilizing Internet 2

With the development of PACS technology and an increasing demand by medical facilities to become filmless, there is a need for a fast and efficient method of providing data backup for disaster recovery and downtime scenarios. At the Image Processing Informatics Lab (IPI), an ASP Backup Archive was developed using a fault-tolerant server with a T1 connection to serve the PACS at the St. Johns Health Center (SJHC) Santa Monica, California. The ASP archive server has been in clinical operation for more than 18 months, and its performance was presented at this SPIE Conference last year. This paper extends the ASP Backup Archive to serve the PACS at the USC Healthcare Consultation Center II (HCC2) utilizing an Internet2 connection. HCC2 is a new outpatient facility that recently opened in April 2004. The Internet2 connectivity between USCs HCC2 and IPI has been established for over one year. There are two novelties of the current ASP model: 1) Use of Internet2 for daily clinical operation, and 2) Modifying the existing backup archive to handle two sites in the ASP model. This paper presents the evaluation of the ASP Backup Archive based on the following two criteria: 1) Reliability and performance of the Internet2 connection between HCC2 and IPI using DICOM image transfer in a clinical environment, and 2) Ability of the ASP Fault-Tolerant backup archive to support two separate clinical PACS sites simultaneously. The performances of using T1 and Internet2 at the two different sites are also compared.

A Solution for Archiving and Retrieving Preclinical Molecular Imaging Data in PACS Using a DICOM Gateway

Advances in biology, computer technology and imaging technology have given rise to a scientific specialty referred to as molecular imaging, which is the in vivo imaging of cellular and molecular pathways using contrast-enhancing targeting agents. Increasing amounts of molecular imaging research are being performed at pre-clinical stages, generating diverse datasets that are unstructured and thereby lacking in archiving and distribution solutions. Since PACS in radiology is a mature clinical archiving solution, a method is proposed to convert current imaging files from preclinical molecular imaging studies into DICOM formats for archival and retrieval from PACS systems. A web-based DICOM gateway is presented with an emphasis on metadata mapping in the DICOM header, system connectivity, and overall user workflow. This effort to conform preclinical imaging data to the DICOM standard is necessary to utilize current PACS solutions for preclinical imaging data content archiving and distribution.

A study-centric database model for organizing multimodality images and metadata in animal imaging research facilities

Research images and findings reports generated during imaging-based small animal imaging experiments are typically kept by imaging facilities on workstations or by investigators on burned DVDs. There usually lacks structure and organization to these data content, and are limited to directory and file names to help users find their data files. A study-centric database design is a fundamental step towards imaging systems integration and also a research data grid infrastructure for multi-institution collaborations and translational research. This paper will present a novel relational database model to maintain experimental metadata for studies, raw imaging files, post-processed images, and quantitative findings, all generated during most imaging-based animal-model studies. The integration of experimental metadata into a single database can alleviate current investigative dependency on hand-written records for current and previous experimental data. Furthermore, imaging workstations and systems that are integrated with this database can be streamlined in their data workflow with automated query services. This novel database model is being implemented in a molecular imaging data grid for evaluation with animal-model imaging studies provided from the Molecular Imaging Center at USC.

A virtualized infrastructure for molecular imaging research using a data grid model

The animal-to-researcher workflow in many of todays small animal imaging center is burdened with proprietary data limitations, inaccessible back-up methods, and imaging results that are not easily viewable across campus. Such challenges decrease the amount of scans performed per day at the center and requires researchers to wait longer for their images and quantified results. Furthermore, data mining at the small animal imaging center is often limited to researcher names and date-labelled archiving hard-drives. To gain efficiency and reliable access to small animal imaging data, such a center needs to move towards an integrated workflow with file format normalization services, metadata databases, expandable archiving infrastructure, and comprehensive user interfaces for query / retrieval tools - achieving all in a cost-effective manner. This poster presentation demonstrates how grid technology can support such a molecular imaging and small animal imaging research community to bridge the needs between imaging modalities and clinical researchers. Existing projects have utilized the Data Grid in PACS tier 2 backup solutions, where fault-tolerance is a high priority, as well as imagingbased clinical trials where data security and auditing are primary concerns. Issues to be addressed include, but are not limited to, novel database designs, file format standards, virtual archiving and distribution workflows, and potential grid computing for 3-D reconstructions, co-registration, and post-processing analysis.