Jorge Documet

Data grid for large-scale medical image archive and analysis

Storage and retrieval technology for large-scale medical image systems has matured significantly during the past ten years but many implementations still lack cost-effective backup and recovery solutions. As an example, a PACS (Picture Archiving and Communication system) in a general medical center requires about 40 Terabytes of storage capacity for seven years. Despite many healthcare centers are relying on PACS for 24/7 clinical operation, current PACS lacks affordable fault-tolerance storage strategies for archive, backup, and disaster recovery. Existing solutions are difficult to administer, and often time consuming for effective recovery after a disaster. For this reason, PACS still encounters unexpected downtime for hours or days, which could cripple daily clinical service and research operations. Grid Computing represents the latest and most exciting technology to evolve from the familiar realm of parallel, peer-to-peer, and client-server models that can address the problem of fault-tolerant storage for backup and recovery of medical images. We have researched and developed a novel Data Grid testbed involving several federated PAC systems based on grid computing architecture. By integrating grid architecture to the PACS DICOM (Digital Imaging and Communication in Medicine) environment, in addition to use its own storage device, a PACS also uses a federated Data Grid composing of several PAC systems for off-site backup archive. In case its own storage fails, the PACS can retrieve its image data from the Data Grid timely and seamlessly. The design reflects the Globus Toolkit 3.0 five-layer architecture of the grid computing: Fabric, Resource, Connectivity, Collective, and Application Layers. The testbed consists of three federated PAC systems, the Fault-Tolerant PACS archive server at the Image Processing and Informatics Laboratory, the clinical PACS at Saint Johns Health Center, and the clinical PACS at the Healthcare Consultation Center II, USC Health Science Campus.In the testbed, we also implement computational services in the Data Grid for image analysis and data mining. The federated PAC systems can use this resource by sharing image data and computational services available in the Data Grid for image analysis and data mining application.In the paper, we first review PACS and its clinical operation, followed by the description of the Data Grid architecture in the testbed. Different scenarios of using the DICOM store and query/retrieve functions of the laboratory model to demonstrate the fault-tolerance features of the Data Grid are illustrated. The status of current clinical implementation of the Data Grid is reported. An example of using the digital hand atlas for bone age assessment of children is presented to describe the concept of computational services in the Data Grid.

A multimedia electronic patient record (ePR) system for image-assisted minimally invasive spinal surgery

PurposeThis paper presents the concept of bridging the gap between diagnostic images and image-assisted surgical treatment through the development of a one-stop multimedia electronic patient record (ePR) system that manages and distributes the real-time multimodality imaging and informatics data that assists the surgeon during all clinical phases of the operation from planning Intra-Op to post-care follow-up. We present the concept of this multimedia ePR for surgery by first focusing on image-assisted minimally invasive spinal surgery as a clinical application.MethodsThree clinical phases of minimally invasive spinal surgery workflow in Pre-Op, Intra-Op, and Post-Op are discussed. The ePR architecture was developed based on the three-phased workflow, which includes the Pre-Op, Intra-Op, and Post-Op modules and four components comprising of the input integration unit, fault-tolerant gateway server, fault-tolerant ePR server, and the visualization and display. A prototype was built and deployed to a minimally invasive spinal surgery clinical site with user training and support for daily use.SummaryA step-by-step approach was introduced to develop a multimedia ePR system for imaging-assisted minimally invasive spinal surgery that includes images, clinical forms, waveforms, and textual data for planning the surgery, two real-time imaging techniques (digital fluoroscopic, DF) and endoscope video images (Endo), and more than half a dozen live vital signs of the patient during surgery. Clinical implementation experiences and challenges were also discussed.

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.

The data storage grid: the next generation of fault-tolerant storage for backup and disaster recovery of clinical images

Grid Computing represents the latest and most exciting technology to evolve from the familiar realm of parallel, peer-to-peer and client-server models that can address the problem of fault-tolerant storage for backup and recovery of clinical images. We have researched and developed a novel Data Grid testbed involving several federated PAC systems based on grid architecture. By integrating a grid computing architecture to the DICOM environment, a failed PACS archive can recover its image data from others in the federation in a timely and seamless fashion. The design reflects the five-layer architecture of grid computing: Fabric, Resource, Connectivity, Collective, and Application Layers. The testbed Data Grid architecture representing three federated PAC systems, the Fault-Tolerant PACS archive server at the Image Processing and Informatics Laboratory, Marina del Rey, the clinical PACS at Saint Johns Health Center, Santa Monica, and the clinical PACS at the Healthcare Consultation Center II, USC Health Science Campus, will be presented. The successful demonstration of the Data Grid in the testbed will provide an understanding of the Data Grid concept in clinical image data backup as well as establishment of benchmarks for performance from future grid technology improvements and serve as a road map for expanded research into large enterprise and federation level data grids to guarantee 99.999 % up time.

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 Multimedia Comprehensive Informatics System with Decision Support Tools for a Multi-site Collaboration Research of Stroke Rehabilitation

Stroke is a major cause of adult disability. The Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (I-CARE) clinical trial aims to evaluate a therapy for arm rehabilitation after stroke. A primary outcome measure is correlative analysis between stroke lesion characteristics and standard measures of rehabilitation progress, from data collected at seven research facilities across the country. Sharing and communication of brain imaging and behavioral data is thus a challenge for collaboration. A solution is proposed as a web-based system with tools supporting imaging and informatics related data. In this system, users may upload anonymized brain images through a secure internet connection and the system will sort the imaging data for storage in a centralized database. Users may utilize an annotation tool to mark up images. In addition to imaging informatics, electronic data forms, for example, clinical data forms, are also integrated. Clinical information is processed and stored in the database to enable future data mining related development. Tele-consultation is facilitated through the development of a thin-client image viewing application. For convenience, the system supports access through desktop PC, laptops, and iPAD. Thus, clinicians may enter data directly into the system via iPAD while working with participants in the study. Overall, this comprehensive imaging informatics system enables users to collect, organize and analyze stroke cases efficiently.

PACS-Based Computer-Aided Detection and Diagnosis

The ultimate goal of Picture Archiving and Communication System (PACS)-based Computer-Aided Detection and Diagnosis (CAD) is to integrate CAD results into daily clinical practice so that it becomes a second reader to aid the radiologist’s diagnosis. Integration of CAD and Hospital Information System (HIS), Radiology Information System (RIS) or PACS requires certain basic ingredients from Health Level 7 (HL7) standard for textual data, Digital Imaging and Communications in Medicine (DICOM) standard for images, and Integrating the Healthcare Enterprise (IHE) workflow profiles in order to comply with the Health Insurance Portability and Accountability Act (HIPAA) requirements to be a healthcare information system. Among the DICOM standards and IHE workflow profiles, DICOM Structured Reporting (DICOM-SR); and IHE Key Image Note (KIN), Simple Image and Numeric Report (SINR) and Post-processing Work Flow (PWF) are utilized in CAD-HIS/RIS/PACS integration. These topics with examples are presented in this chapter.

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.

A tracking and verification system implemented in a clinical environment for partial HIPAA compliance

The paper describes the methodology for the clinical design and implementation of a Location Tracking and Verification System (LTVS) that has distinct benefits for the Imaging Department at the Healthcare Consultation Center II (HCCII), an outpatient imaging facility located on the USC Health Science Campus. A novel system for tracking and verification of patients and staff in a clinical environment using wireless and facial biometric technology to monitor and automatically identify patients and staff was developed in order to streamline patient workflow, protect against erroneous examinations and create a security zone to prevent and audit unauthorized access to patient healthcare data under the HIPAA mandate. This paper describes the system design and integration methodology based on initial clinical workflow studies within a clinical environment. An outpatient center was chosen as an initial first step for the development and implementation of this system.

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.