An Interactive RADIANCE Toolkit for Customizable CT Dose Monitoring and Reporting

The need for tools to monitor imaging-related radiation has grown dramatically in recent years. RADIANCE, a freely available open-source dose-monitoring tool, was developed in response to the need for an informatics solution in this realm. A number of open-source as well as commercial solutions have since been developed to enable radiology practices to monitor radiation dose parameters for modalities ranging from computed tomography to radiography to fluoroscopy. However, it is not sufficient to simply collect this data; it is equally important to be able to review it in the appropriate context. Most of the currently available dose-monitoring solutions have some type of reporting capability, such as a real-time dashboard or a static report. Previous versions of RADIANCE have included a real-time dashboard with pre-set screens that plot effective dose estimates according to different criteria, as well as monthly scorecards to summarize dose estimates for individuals within a radiology practice. In this work, we present the RADIANCE toolkit, a customizable reporting solution that allows users to generate reports of interest to them, summarizing a variety of metrics that can be grouped according to useful parameters. The output of the toolkit can be used for real-time dose monitoring or scheduled reporting, such as to a quality assurance committee. Making dose parameter data more accessible and more meaningful to the user promotes dose reduction efforts such as regular protocol review and optimization, and ultimately improves patient care by decreasing unnecessary radiation exposure.     

The Ensembl analysis pipeline

The Ensembl pipeline is an extension to the Ensembl system which allows automated annotation of genomic sequence. The software comprises two parts. First, there is a set of Perl modules ("Runnables" and "RunnableDBs") which are 'wrappers' for a variety of commonly used analysis tools. These retrieve sequence data from a relational database, run the analysis, and write the results back to the database. They inherit from a common interface, which simplifies the writing of new wrapper modules. On top of this sits a job submission system (the "RuleManager") which allows efficient and reliable submission of large numbers of jobs to a compute farm. Here we describe the fundamental software components of the pipeline, and we also highlight some features of the Sanger installation which were necessary to enable the pipeline to scale to whole-genome analysis.     

Open Source software in medical informatics--why,how and what

'Open Source' is a 20-40 year old approach to licensing and distributing software that has recently burst into public view. Against conventional wisdom this approach has been wildly successful in the general software market--probably because the openness lets programmers the world over obtain, critique, use, and build upon the source code without licensing fees. Linux, a UNIX-like operating system, is the best known success. But computer scientists at the University of California, Berkeley began the tradition of software sharing in the mid 1970s with BSD UNIX and distributed the major internet network protocols as source code without a fee. Medical informatics has its own history of Open Source distribution: Massachusetts General's COSTAR and the Veterans Administration's VISTA software have been distributed as source code at no cost for decades. Bioinformatics, our sister field, has embraced the Open Source movement and developed rich libraries of open-source software. Open Source has now gained a tiny foothold in health care (OSCAR GEHR, OpenEMed). Medical informatics researchers and funding agencies should support and nurture this movement. In a world where open-source modules were integrated into operational health care systems, informatics researchers would have real world niches into which they could engraft and test their software inventions. This could produce a burst of innovation that would help solve the many problems of the health care system. We at the Regenstrief Institute are doing our part by moving all of our development to the open-source model.     

‘Big data’, Hadoop and cloud computing in genomics

Since the completion of the Human Genome project at the turn of the Century, there has been an unprecedented proliferation of genomic sequence data. A consequence of this is that the medical discoveries of the future will largely depend on our ability to process and analyse large genomic data sets, which continue to expand as the cost of sequencing decreases. Herein, we provide an overview of cloud computing and big data technologies, and discuss how such expertise can be used to deal with biology’s big data sets. In particular, big data technologies such as the Apache Hadoop project, which provides distributed and parallelised data processing and analysis of petabyte (PB) scale data sets will be discussed, together with an overview of the current usage of Hadoop within the bioinformatics community.     

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.     

The Orthanc Ecosystem for Medical Imaging

This paper reviews the components of Orthanc, a free and open-source, highly versatile ecosystem for medical imaging. At the core of the Orthanc ecosystem, the Orthanc server is a lightweight vendor neutral archive that provides PACS managers with a powerful environment to automate and optimize the imaging flows that are very specific to each hospital. The Orthanc server can be extended with plugins that provide solutions for teleradiology, digital pathology, or enterprise-ready databases. It is shown how software developers and research engineers can easily develop external software or Web portals dealing with medical images, with minimal knowledge of the DICOM standard, thanks to the advanced programming interface of the Orthanc server. The paper concludes by introducing the Stone of Orthanc, an innovative toolkit for the cross-platform rendering of medical images.     

Learning from hackers: open-source clinical trials

Open sharing of clinical trial data has been proposed as a way to address the gap between the production of clinical evidence and the decision-making of physicians. A similar gap was addressed in the software industry by their open-source software movement. Here, we examine how the social and technical principles of the movement can guide the growth of an open-source clinical trial community.     

利用Nutch设计实现生物医学信息垂直搜索引擎

在网络的海量信息搜索过程中,医学情报研究和信息服务机构,经常需要构建面向专题的垂直搜索系统以满足特定人群的需求。本文利用Nutch和Lucene等开源软件设计了一个面向生物医学信息的垂直搜索引擎系统,并对网页信息抓取、格式处理、内容索引和检索等关键技术进行了说明。在此搜索引擎中,通过加入中文分词和增量抓取等模块,提高了中文关键字的识别率,缩短了信息的更新周期。目前该系统已经上线测试,能够获得较为精确和及时的搜索结果。     

Monte-Carlo dosimetry on a realistic cell monolayer geometry exposed to alpha particles

The energy and specific energy absorbed in the main cell compartments (nucleus and cytoplasm) in typical radiobiology experiments are usually estimated by calculations as they are not accessible for a direct measurement. In most of the work, the cell geometry is modelled using the combination of simple mathematical volumes. We propose a method based on high resolution confocal imaging and ion beam analysis (IBA) in order to import realistic cell nuclei geometries in Monte-Carlo simulations and thus take into account the variety of different geometries encountered in a typical cell population. Seventy-six cell nuclei have been imaged using confocal microscopy and their chemical composition has been measured using IBA. A cellular phantom was created from these data using the ImageJ image analysis software and imported in the Geant4 Monte-Carlo simulation toolkit. Total energy and specific energy distributions in the 76 cell nuclei have been calculated for two types of irradiation protocols: a 3 MeV alpha particle microbeam used for targeted irradiation and a 23?Pu alpha source used for large angle random irradiation. Qualitative images of the energy deposited along the particle tracks have been produced and show good agreement with images of DNA double strand break signalling proteins obtained experimentally. The methodology presented in this paper provides microdosimetric quantities calculated from realistic cellular volumes. It is based on open-source oriented software that is publicly available.     

dcm4che在医疗信息集成中的初步应用

作为临床医学影像和对象管理的开源资源,dcm4che提供了D ICOM标准和HL7标准接口。dcm4che严格遵循IHE规范,是医疗信息集成中理想且强大的开发工具,但目前相关研究较少。本文在介绍IHE规范及医疗信息集成的基础上,介绍了dcm4che开源工具包在医疗信息系统中的功能特性和初步应用,其中包括dcm4che提供的各种接口及功能模块,重点介绍了dcm4che对D ICOM、HL7标准的完美支持,并利用部分应用实例阐述了严格遵循IHE规范的dcm4che开源工具包在医疗信息集成中的可用性、高效性和可移植性。本项工作为将来在医疗信息集成中大规模运用dcm4che提供了研究基础。     

ViewGene: a graphical tool for polymorphism visualization and characterization

The human genome project is producing an enormous amount of sequence data, based on which single base changes between individuals can be identified. Unfortunately, computer tools that were adequate for sequence assembly are less than ideal for the characterization of polymorphism data [single nucleotide (snp) or insertion/deletion (indel)] and other sequence features, and their relationship to each other. We have developed viewGene as a flexible tool that takes input from a number of sequence formats and analysis programs (Genbank, FASTA, RepeatMasker, Cross match, BLAST, user-defined data) to construct a sequence reference scaffold that can be viewed through a simple graphical interface. polymorphisms generated from many sources can be added to this scaffold through the same sequence formats, with a variety of options to control what is displayed. Large amounts of polymorphism data can be organized so that patterns and haplotypes can be readily discerned. In our laboratory, viewGene has been used to view annotated genbank records, find nonrepetitive sequence fragments for polymorphism detection, and visualize similarity search results. Manipulation, cross-referencing, and haplotype viewing of snp data are essential for quality assessment and identification of variants associated with genetic disease, and viewGene provides all three of these important functions.     

Design and Implementation of an Open Source Indexing Solution for a Large Set of Radiological Reports and Images

This paper hopes to share the insights we experienced during designing, building, and running an indexing solution for a large set of radiological reports and images in a production environment for more than 3 years. Several technical challenges were encountered and solved in the course of this project. One hundred four million words in 1.8 million radiological reports from 1989 to the present were indexed and became instantaneously searchable in a user-friendly fashion; the median query duration is only 31 ms. Currently, our highly tuned index holds 332,088 unique words in four languages. The indexing system is feature-rich and language-independent and allows for making complex queries. For research and training purposes it certainly is a valuable and convenient addition to our radiology informatics toolbox. Extended use of open-source technology dramatically reduced both implementation time and cost. All software we developed related to the indexing project has been made available to the open-source community covered by an unrestricted Berkeley Software Distribution-style license.     

Decoding the human genome sequence

The year 2000 is marked by the production of the sequence of the human genome. A 'working draft' of high quality sequence covering 90% of the genome has been determined and a quarter is in finished form, including the first two completed chromosomes. All sequence data from the project is made freely available to the community via the Internet, for further analysis and exploitation. The challenge which lies ahead is to decipher the information. Knowledge of the human genome sequence will enable us to understand how the genetic information determines the development, structure and function of the human body. We will be able to explore how variations within our DNA sequence cause disease, how they affect our interaction with our environment and ultimately to develop new and effective ways to improve human health.     

Making sense of mobile health data: an open architecture to improve individual- and population-level health

Mobile phones and devices, with their constant presence, data connectivity, and multiple intrinsic sensors, can support around-the-clock chronic disease prevention and management that is integrated with daily life. These mobile health (mHealth) devices can produce tremendous amounts of location-rich, real-time, high-frequency data. Unfortunately, these data are often full of bias, noise, variability, and gaps. Robust tools and techniques have not yet been developed to make mHealth data more meaningful to patients and clinicians. To be most useful, health data should be sharable across multiple mHealth applications and connected to electronic health records. The lack of data sharing and dearth of tools and techniques for making sense of health data are critical bottlenecks limiting the impact of mHealth to improve health outcomes. We describe Open mHealth, a nonprofit organization that is building an open software architecture to address these data sharing and "sense-making" bottlenecks. Our architecture consists of open source software modules with well-defined interfaces using a minimal set of common metadata. An initial set of modules, called InfoVis, has been developed for data analysis and visualization. A second set of modules, our Personal Evidence Architecture, will support scientific inferences from mHealth data. These Personal Evidence Architecture modules will include standardized, validated clinical measures to support novel evaluation methods, such as n-of-1 studies. All of Open mHealth's modules are designed to be reusable across multiple applications, disease conditions, and user populations to maximize impact and flexibility. We are also building an open community of developers and health innovators, modeled after the open approach taken in the initial growth of the Internet, to foster meaningful cross-disciplinary collaboration around new tools and techniques. An open mHealth community and architecture will catalyze increased mHealth efficiency, effectiveness, and innovation.     

Automated Gait Analysis Through Hues and Areas (AGATHA): A Method to Characterize the Spatiotemporal Pattern of Rat Gait

While rodent gait analysis can quantify the behavioral consequences of disease, significant methodological differences exist between analysis platforms and little validation has been performed to understand or mitigate these sources of variance. By providing the algorithms used to quantify gait, open-source gait analysis software can be validated and used to explore methodological differences. Our group is introducing, for the first time, a fully-automated, open-source method for the characterization of rodent spatiotemporal gait patterns, termed Automated Gait Analysis Through Hues and Areas (AGATHA). This study describes how AGATHA identifies gait events, validates AGATHA relative to manual digitization methods, and utilizes AGATHA to detect gait compensations in orthopaedic and spinal cord injury models. To validate AGATHA against manual digitization, results from videos of rodent gait, recorded at 1000 frames per second (fps), were compared. To assess one common source of variance (the effects of video frame rate), these 1000 fps videos were re-sampled to mimic several lower fps and compared again. While spatial variables were indistinguishable between AGATHA and manual digitization, low video frame rates resulted in temporal errors for both methods. At frame rates over 125 fps, AGATHA achieved a comparable accuracy and precision to manual digitization for all gait variables. Moreover, AGATHA detected unique gait changes in each injury model. These data demonstrate AGATHA is an accurate and precise platform for the analysis of rodent spatiotemporal gait patterns.     

基于LEADTOOLS的DICOM标准医学图像通讯的研究

支持DICOM协议是PACS系统的基本特征,而DICOM存储和查询获取则是DICOM通讯中的关键环节。本文利用LEADTOOLS14.5是LEADTOOLS公司最新开发的医学软件开发包,它提供了完成DICOM通信的各种类库,在此基础上本文对SCU和SCP之间的DICOM协商、存储、查询与获取等通讯过程进行了探讨。为各种医学信号与图像设备、服务器、及诊断工作站,提供了符合DICOM 3.0标准的通信功能。     

Open Source Software Projects of the caBIG™ In Vivo Imaging Workspace Software Special Interest Group

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.     

A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System

Open-source development can provide a platform for innovation by seeking feedback from community members as well as providing tools and infrastructure to test new standards. Vendors of proprietary systems may delay adoption of new standards until there are sufficient incentives such as legal mandates or financial incentives to encourage/mandate adoption. Moreover, open-source systems in healthcare have been widely adopted in low- and middle-income countries and can be used to bridge gaps that exist in global health radiology. Since 2011, the authors, along with a community of open-source contributors, have worked on developing an open-source radiology information system (RIS) across two communities—OpenMRS and LibreHealth. The main purpose of the RIS is to implement core radiology workflows, on which others can build and test new radiology standards. This work has resulted in three major releases of the system, with current architectural changes driven by changing technology, development of new standards in health and imaging informatics, and changing user needs. At their core, both these communities are focused on building general-purpose EHR systems, but based on user contributions from the fringes, we have been able to create an innovative system that has been used by hospitals and clinics in four different countries. We provide an overview of the history of the LibreHealth RIS, the architecture of the system, overview of standards integration, describe challenges of developing an open-source product, and future directions. Our goal is to attract more participation and involvement to further develop the LibreHealth RIS into an Enterprise Imaging System that can be used in other clinical imaging including pathology and dermatology.