Mastering DICOM with DVTk

The Digital Imaging and Communications in Medicine (DICOM) Validation Toolkit (DVTk) is an open-source framework with potential value for anyone working with the DICOM standard. DICOM’s flexibility requires hands-on experience in understanding ways in which the standard’s interpretation may vary among vendors. DVTk was developed as a clinical engineering tool to aid and accelerate DICOM integration at clinical sites. DVTk is used to provide an independent measurement of the accuracy of a product’s DICOM interface, according to both the DICOM standard and the product’s conformance statement. DVTk has stand-alone tools and a framework with which developers can create new tools. We provide an overview of the architecture of the toolkit, sample scenarios of its utility, and evidence of its relative ease of use. Our goal is to encourage involvement in this open-source project and attract developers to build off and further enrich this platform for DICOM integration testing.     

Evaluation of Free,Open-source,Web-based DICOM Viewers for the Indian National Telemedicine Service (eSanjeevani)

The Digital Image and Communications in Medicine (DICOM) viewer is a very useful component in telemedicine applications. Owing to increased demand, adoption, and prospects of browser-based software in the recent past, web-based DICOM viewers have gained significant ground. There are myriad web-based DICOM viewers which are open source and are available free of cost as stand-alone applications. These freely available tools have rich functionality like the commercial ones. To find an optimal DICOM viewer for integration with a web-based telemedicine solution is quite a challenge, and no research has gone into assessing these freely available DICOM viewers. This research assessed a range of web-based, open-source, and freely available DICOM viewers from the perspective of their integration with the Indian National Telemedicine Solution (eSanjeevani). To introduce teleradiology module in eSanjeevani, a study is carried out to enable viewing of radiological images through DICOM viewer. eSanjeevani is being prepared for a national roll-out at 155,000 health and wellness centers across rural India by the Ministry of Health and Family Welfare (Government of India) under the Ayushman Bharat Scheme (the world’s largest health insurance scheme). In total, 13 free, open-source, and web-based DICOM viewers were identified for evaluation; however, only six were shortlisted as assessed. This study can serve as a one-stop source for researchers looking for a suitable DICOM viewer for their healthcare IT applications.     

OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images

A multidimensional image navigation and display software was designed for display and interpretation of large sets of multidimensional and multimodality images such as combined PET-CT studies. The software is developed in Objective-C on a Macintosh platform under the MacOS X operating system using the GNUstep development environment. It also benefits from the extremely fast and optimized 3D graphic capabilities of the OpenGL graphic standard widely used for computer games optimized for taking advantage of any hardware graphic accelerator boards available. In the design of the software special attention was given to adapt the user interface to the specific and complex tasks of navigating through large sets of image data. An interactive jog-wheel device widely used in the video and movie industry was implemented to allow users to navigate in the different dimensions of an image set much faster than with a traditional mouse or on-screen cursors and sliders. The program can easily be adapted for very specific tasks that require a limited number of functions, by adding and removing tools from the programs toolbar and avoiding an overwhelming number of unnecessary tools and functions. The processing and image rendering tools of the software are based on the open-source libraries ITK and VTK. This ensures that all new developments in image processing that could emerge from other academic institutions using these libraries can be directly ported to the OsiriX program. OsiriX is provided free of charge under the GNU open-source licensing agreement at .     

Patient dose management in digital radiography

Purpose:

To present the experience in patient dose management and the development of an online audit tool for digital radiography.

Materials and methods:

Several tools have been developed to extract the information contained in the DICOM header of digital images, collect radiographic parameters, calculate patient entrance doses and other related parameters, and audit image quality.

Results:

The tool has been used for mammography, and includes images from over 25,000 patients, over 75,000 chest images, 100,000 computed radiography procedures and more than 1,000 interventional radiology procedures. Examples of calculation of skin dose distribution in interventional cardiology based upon information of DICOM header and the results of dosimetric parameters for cardiology procedures in 2006 are presented.

Conclusion:

Digital radiology has great advantages for imaging and patient dose management. Dose reports, QCONLINE systems and the MPPS DICOM service are good tools to optimise procedures and to manage patient dosimetry data. The implementation of the ongoing IEC-DICOM standard for patient dose structured reports will improve dose management in digital radiology.     

General Consumer Communication Tools for Improved Image Management and Communication in Medicine

We elected to explore new technologies emerging on the general consumer market that can improve and facilitate image and data communication in medical and clinical environment. These new technologies developed for communication and storage of data can improve the user convenience and facilitate the communication and transport of images and related data beyond the usual limits and restrictions of a traditional picture archiving and communication systems (PACS) network. We specifically tested and implemented three new technologies provided on Apple computer platforms. (1) We adopted the iPod, a MP3 portable player with a hard disk storage, to easily and quickly move large number of DICOM images. (2) We adopted iChat, a videoconference and instant-messaging software, to transmit DICOM images in real time to a distant computer for conferencing teleradiology. (3) Finally, we developed a direct secure interface to use the iDisk service, a file-sharing service based on the WebDAV technology, to send and share DICOM files between distant computers. These three technologies were integrated in a new open-source image navigation and display software called OsiriX allowing for manipulation and communication of multimodality and multidimensional DICOM image data sets. This software is freely available as an open-source project at . Our experience showed that the implementation of these technologies allowed us to significantly enhance the existing PACS with valuable new features without any additional investment or the need for complex extensions of our infrastructure. The added features such as teleradiology, secure and convenient image and data communication, and the use of external data storage services open the gate to a much broader extension of our imaging infrastructure to the outside world.     

VirtualPACS—A Federating Gateway to Access Remote Image Data Resources over the Grid

Collaborations in biomedical research and clinical studies require that data, software, and computational resources be shared between geographically distant institutions. In radiology, there is a related issue of sharing remote DICOM data over the Internet. This paper focuses on the problem of federating multiple image data resources such that clients can interact with them as if they are stored in a centralized PACS. We present a toolkit, called VirtualPACS, to support this functionality. Using the toolkit, users can perform standard DICOM operations (query, retrieve, and submit) across distributed image databases. The key features of the toolkit are: (1) VirtualPACS makes it easy to use existing DICOM client applications for data access; (2) it can easily be incorporated into an imaging workflow as a DICOM source; (3) using VirtualPACS, heterogeneous collections of DICOM sources are exposed to clients through a uniform interface and common data model; and (4) DICOM image databases without DICOM messaging can be accessed.

Open-Source,Web-Based Dashboard Components for DICOM Connectivity

The administration of a DICOM network within an imaging healthcare institution requires tools that allow for monitoring of connectivity and availability for adequate uptime measurements and help guide technology management strategies. We present the implementation of an open-source widget for the Dashing framework that provides basic dashboard functionality allowing for monitoring of a DICOM network using network “ping” and DICOM “C-ECHO” operations.     

We Built This House; It’s Time to Move in: Leveraging Existing DICOM Structure to More Completely Utilize Readily Available Detailed Contrast Administration Information

The Digital Imaging and Communications in Medicine (DICOM) standard is the universal format for interoperability in medical imaging. In addition to imaging data, DICOM has evolved to support a wide range of imaging metadata including contrast administration data that is readily available from many modern contrast injectors. Contrast agent, route of administration, start and stop time, volume, flow rate, and duration can be recorded using DICOM attributes [1]. While this information is sparsely and inconsistently recorded in routine clinical practice, it could potentially be of significant diagnostic value. This work will describe parameters recorded by automatic contrast injectors, summarize the DICOM mechanisms available for tracking contrast injection data, and discuss the role of such data in clinical radiology.     

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.     

A Reliable,Low-Cost Picture Archiving and Communications System for Small and Medium Veterinary Practices Built Using Open-Source Technology

Picture Archiving and Communications Systems (PACS) are the most needed system in a modern hospital. As an integral part of the Digital Imaging and Communications in Medicine (DICOM) standard, they are charged with the responsibility for secure storage and accessibility of the diagnostic imaging data. These machines need to offer high performance, stability, and security while proving reliable and ergonomic in the day-to-day and long-term storage and retrieval of the data they safeguard. This paper reports the experience of the authors in developing and installing a compact and low-cost solution based on open-source technologies in the Veterinary Teaching Hospital for the University of Torino, Italy, during the course of the summer of 2012. The PACS server was built on low-cost x86-based hardware and uses an open source operating system derived from Oracle OpenSolaris (Oracle Corporation, Redwood City, CA, USA) to host the DCM4CHEE PACS DICOM server (DCM4CHEE, http://www.dcm4che.org). This solution features very high data security and an ergonomic interface to provide easy access to a large amount of imaging data. The system has been in active use for almost 2 years now and has proven to be a scalable, cost-effective solution for practices ranging from small to very large, where the use of different hardware combinations allows scaling to the different deployments, while the use of paravirtualization allows increased security and easy migrations and upgrades.     

Open-Source Radiation Exposure Extraction Engine (RE3) with Patient-Specific Outlier Detection

We present an open-source, picture archiving and communication system (PACS)-integrated radiation exposure extraction engine (RE3) that provides study-, series-, and slice-specific data for automated monitoring of computed tomography (CT) radiation exposure. RE3 was built using open-source components and seamlessly integrates with the PACS. RE3 calculations of dose length product (DLP) from the Digital imaging and communications in medicine (DICOM) headers showed high agreement (R ²?=?0.99) with the vendor dose pages. For study-specific outlier detection, RE3 constructs robust, automatically updating multivariable regression models to predict DLP in the context of patient gender and age, scan length, water-equivalent diameter (D _w), and scanned body volume (SBV). As proof of concept, the model was trained on 811 CT chest, abdomen + pelvis (CAP) exams and 29 outliers were detected. The continuous variables used in the outlier detection model were scan length (R ² ?=?0.45), D _w (R ²?=?0.70), SBV (R ²?=?0.80), and age (R ²?=?0.01). The categorical variables were gender (male average 1182.7?±?26.3 and female 1047.1?±?26.9 mGy cm) and pediatric status (pediatric average 710.7?±?73.6 mGy cm and adult 1134.5?±?19.3 mGy cm).     

Development and Evaluation of a Low-Cost and High-Capacity DICOM Image Data Storage System for Research

Thin-slice CT data, useful for clinical diagnosis and research, is now widely available but is typically discarded in many institutions, after a short period of time due to data storage capacity limitations. We designed and built a low-cost high-capacity Digital Imaging and COmmunication in Medicine (DICOM) storage system able to store thin-slice image data for years, using off-the-shelf consumer hardware components, such as a Macintosh computer, a Windows PC, and network-attached storage units. “Ordinary” hierarchical file systems, instead of a centralized data management system such as relational database, were adopted to manage patient DICOM files by arranging them in directories enabling quick and easy access to the DICOM files of each study by following the directory trees with Windows Explorer via study date and patient ID. Software used for this system was open-source OsiriX and additional programs we developed ourselves, both of which were freely available via the Internet. The initial cost of this system was about $3,600 with an incremental storage cost of about $900 per 1 terabyte (TB). This system has been running since 7th Feb 2008 with the data stored increasing at the rate of about 1.3 TB per month. Total data stored was 21.3 TB on 23rd June 2009. The maintenance workload was found to be about 30 to 60 min once every 2 weeks. In conclusion, this newly developed DICOM storage system is useful for research due to its cost-effectiveness, enormous capacity, high scalability, sufficient reliability, and easy data access.Key words: Data storage, archive, computed tomography, PACS, thin-slice CT     

MRIdb: Medical Image Management for Biobank Research

Clinical picture archiving and communications systems provide convenient, efficient access to digital medical images from multiple modalities but can prove challenging to deploy, configure and use. MRIdb is a self-contained image database, particularly suited to the storage and management of magnetic resonance imaging data sets for population phenotyping. It integrates a mature image archival system with an intuitive web-based user interface that provides visualisation and export functionality. In addition, utilities for auditing, data migration and system monitoring are included in a virtual machine image that is easily deployed with minimal configuration. The result is a freely available turnkey solution, designed to support epidemiological and imaging genetics research. It allows the management of patient data sets in a secure, scalable manner without requiring the installation of any bespoke software on end users’ workstations. MRIdb is an open-source software, available for download at http://www3.imperial.ac.uk/bioinfsupport/resources/software/mridb.     

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.In recent years, there has been a rapid expansion in the number of next-generation sequencing platforms, including Illumina (Bentley et al. 2008), the Applied Biosystems SOLiD System (McKernan et al. 2009), 454 Life Sciences (Roche) (Margulies et al. 2005), Helicos HeliScope (Shendure and Ji 2008), and most recently Complete Genomics (Drmanac et al. 2010). Many tools have been created to work with next-generation sequencer data, from read based aligners like MAQ (Li et al. 2008a), BWA (Li and Durbin 2009), and SOAP (Li et al. 2008b), to single nucleotide polymorphism and structural variation detection tools like BreakDancer (Chen et al. 2009), VarScan (Koboldt et al. 2009), and MAQ. Although these tools are highly effective in their problem domains, there still exists a large development gap between sequencing output and analysis results, in part because tailoring these analysis tools to answer specific scientific questions can be laborious and difficult. General frameworks are available for processing next-generation sequencing data but tend to focus on specific classes of analysis problems—like quality assessment of sequencing data, as in PIQA (Martinez-Alcantara et al. 2009)—or require specialized knowledge of an existing framework, as in BioConductor in the ShortRead toolset (Morgan et al. 2009). The lack of sophisticated and flexible programming frameworks that enable downstream analysts to access and manipulate the massive sequencing data sets in a programmatic way has been a hindrance to the rapid development of new tools and methods.With the emergence of the SAM file specification (Li et al. 2009) as the standard format for storage of platform-independent next-generation sequencing data, we saw the opportunity to implement an analysis programming framework which takes advantage of this common input format to simplify the up-front coding costs for end users. Here, we present the Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce (Dean and Ghemawat 2008). By separating specific analysis calculations from common data management infrastructure, tools are easy to write while benefiting from ongoing improvements to the core GATK. The GATK engine is constantly being refined and optimized for correctness, stability, and CPU and memory efficiency; this well-structured software core allows the GATK to support advanced features such as distributed and automatic shared-memory parallelization. Here, we highlight the capabilities of the GATK, which has been used to implement a range of analysis methods for projects like The Cancer Genome Atlas (http://cancergenome.nih.gov) and the 1000 Genomes Project (http://www.1000genomes.org), by describing the implementation of depth of coverage analysis tools and a Bayesian single nucleotide polymorphism (SNP) genotyper, and show the application of these tools to the 1000 Genomes Project pilot data.     

Linking Whole-Slide Microscope Images with DICOM by Using JPEG2000 Interactive Protocol

The use of digitized histopathologic specimens (also known as whole-slide images (WSIs)) in clinical medicine requires compatibility with the Digital Imaging and Communications in Medicine (DICOM) standard. Unfortunately, WSIs usually exceed DICOM image object size limit, making it impossible to store and exchange them in a straightforward way. Moreover, transmitting the entire DICOM image for viewing is ineffective for WSIs. With the JPEG2000 Interactive Protocol (JPIP), WSIs can be linked with DICOM by transmitting image data over an auxiliary connection, apart from patient data. In this study, we explored the feasibility of using JPIP to link JPEG2000 WSIs with a DICOM-based Picture Archiving and Communications System (PACS). We first modified an open-source DICOM library by adding support for JPIP as described in the existing DICOM Supplement 106. Second, the modified library was used as a basis for a software package (JVSdicom), which provides a proof-of-concept for a DICOM client–server system that can transmit patient data, conventional DICOM imagery (e.g., radiological), and JPIP-linked JPEG2000 WSIs. The software package consists of a compression application (JVSdicom Compressor) for producing DICOM-compatible JPEG2000 WSIs, a DICOM PACS server application (JVSdicom Server), and a DICOM PACS client application (JVSdicom Workstation). JVSdicom is available for free from our Web site (), which also features a public JVSdicom Server, containing example X-ray images and histopathology WSIs of breast cancer cases. The software developed indicates that JPEG2000 and JPIP provide a well-working solution for linking WSIs with DICOM, requiring only minor modifications to current DICOM standard specification.     

New tools for MHC research from machine learning and predictive algorithms to the tumour immunopeptidome

At a time when immunology seeks to progress ever more rapidly from characterization of a microbial or tumour antigen to the immune correlates that may define protective T‐cell immunity, there is a need for robust tools to enable accurate predictions of peptide–major histocompatibility complex (pMHC) and peptide–MHC–T‐cell receptor binding. Improvements in the curation of data sets from high throughput pMHC analysis, such as the NIH Immune Epitope Database (IEDB), and the associated developments of predictive tools rooted in machine‐learning approaches, are having significant impact. When such approaches are linked to the powerful empirical immunopeptidome data sets from peptide MHC elution and mass spectrometry, there is considerable potential for rapid translation to T‐cell therapies and vaccines.     

Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?

Background

User content posted through Twitter has been used for biosurveillance, to characterize public perception of health-related topics, and as a means of distributing information to the general public. Most of the existing work surrounding Twitter and health care has shown Twitter to be an effective medium for these problems but more could be done to provide finer and more efficient access to all pertinent data. Given the diversity of user-generated content, small samples or summary presentations of the data arguably omit a large part of the virtual discussion taking place in the Twittersphere. Still, managing, processing, and querying large amounts of Twitter data is not a trivial task. This work describes tools and techniques capable of handling larger sets of Twitter data and demonstrates their use with the issue of antibiotics.

Objective

This work has two principle objectives: (1) to provide an open-source means to efficiently explore all collected tweets and query health-related topics on Twitter, specifically, questions such as what users are saying and how messages are spread, and (2) to characterize the larger discourse taking place on Twitter with respect to antibiotics.

Methods

Open-source software suites Hadoop, Flume, and Hive were used to collect and query a large number of Twitter posts. To classify tweets by topic, a deep network classifier was trained using a limited number of manually classified tweets. The particular machine learning approach used also allowed the use of a large number of unclassified tweets to increase performance.

Results

Query-based analysis of the collected tweets revealed that a large number of users contributed to the online discussion and that a frequent topic mentioned was resistance. A number of prominent events related to antibiotics led to a number of spikes in activity but these were short in duration. The category-based classifier developed was able to correctly classify 70% of manually labeled tweets (using a 10-fold cross validation procedure and 9 classes). The classifier also performed well when evaluated on a per category basis.

Conclusions

Using existing tools such as Hive, Flume, Hadoop, and machine learning techniques, it is possible to construct tools and workflows to collect and query large amounts of Twitter data to characterize the larger discussion taking place on Twitter with respect to a particular health-related topic. Furthermore, using newer machine learning techniques and a limited number of manually labeled tweets, an entire body of collected tweets can be classified to indicate what topics are driving the virtual, online discussion. The resulting classifier can also be used to efficiently explore collected tweets by category and search for messages of interest or exemplary content.