Evaluating Erasure Codes in Dicoogle PACS

DICOM (Digital Imaging and Communication in Medicine) is a standard for image and data transmission in medical purpose hardware and is commonly used for viewing, storing, printing and transmitting images. As a part of the way that DICOM transmits files, the PACS (Picture Archiving and Communication System) platform, Dicoogle, has become one of the most in-demand image processing and viewing platforms. However, the Dicoogle PACS architecture does not guarantee image information recovery in the case of information loss. Therefore, this paper proposes a file recovery solution in the Dicoogle architecture. The proposal consists of maximizing the encoding and decoding performance of medical images through computational parallelism. To validate the proposal, the Java programming language based on the Reed-Solomon algorithm is implemented in different performance tests. The experimental results show that the proposal is optimal in terms of image processing time for the Dicoogle PACS storage system.Ministry of Science, Innovation and Universities (MICINN) of Spain PGC2018 098883-B-C44European CommissionPrograma para el Desarrollo Profesional Docente para el Tipo Superior (PRODEP) of MexicoCorporacion Ecuatoriana para el Desarrollo de la Investigacion y la Academia (CEDIA) of Ecuador CEPRA XII-2018-13Universidad de Las Americas (UDLA), Quito, Ecuador IEA.WHP.21.0

A study to understand the acceptance of DICOM Structured Reports on Breast Imaging

Purpose: To create a Digital Imaging and Communication in Medicine Structured Reports (DICOM-SR) Repository and compare the acceptance of Free Text (FT) versus Structured Reports (SR) in communication of Breast Imaging findings. Materials and Methods: It was conceptualized the MamoCatalogue to the structuring of the Reports and the SR were converted into DICOM-SR and integrated with Dicoogle. After that, seven representative Breast Imaging Reports were selected and evaluated by a group of 25 Physicians. Each Physician evaluated the seven Reports, in FT and SR with a 3 months timelag, about their, Structure, Clarity and assertiveness, Diagnostic/Recommendations, Easiness of reading, Full reading, Partially reading with Breast Imaging Reporting and Data System (BI-RADS) focus and Ambiguity. Results: A DICOM-SR Repository was created and the assessment of the acceptance of the FT vs. SR revealed that there is a global trend favoring FT. Nevertheless, a group wise analysis revealed that for Gynaecologists and General Practitioners (GP) the differences between FT and SR weren't significant, unlike what happens with Radiologists. Conclusion: The DICOM-SR Repository allows the query/retrieve data for Reports and the communication with Gynaecologists and GP by SR was satisfactory. Although, Radiologists acceptance must be reinforced upon global communication and management strategy

DicoogleWeb: uma interface web para repositório de imagem médica

Mestrado em Engenharia de Computadores e TelemáticaDicoogle is an open-source software solution designed to manage the information workflow in a PACS as well as the archiving and indexing process of the arriving DICOM files. The current implementation can either be run in server mode (default) or used as a client to connect to another server instance of Dicoogle (via Java RMI). This enables simple access to client workstation within or outside a medical organization. This work will focus on adapting the current implementation of Dicoogle for the Web environment, allowing, system clients, to view medical images, on the majority of devices with network access.O Dicoogle é uma solução de software, em código fonte aberto, que foi desenhada para suportar o fluxo de informação num laboratório de imagem médica. Além disso, está dotado de um mecanismo de indexação que permite indexar todos os metadados contidos nos ficheiros DICOM do seu repositório. A actual implementação pode ser lançada em modo servidor (por omissão) mas também dispõem de um módulo gráfico cliente que pode conectar-se a qualquer instância servidor através de Java RMI. Isto proporciona um acesso simples a estações de trabalho clientes dentro ou fora de uma organização medica. Esta dissertação propõe e implementa uma solução que permite migrar o Dicoogle para ambiente Web. Para além de disponibilizar todas as funcionalidades da versão anterior, a versão Web oferece um conjunto de novos serviços e interface de acesso aos dados

Cloud para comunicações entre instituições médicas

Mestrado em Engenharia de Computadores e TelemáticaAo longo das últimas décadas, os sistemas informáticos que permitem o arquivo e partilha de imagens médicas têm vindo a tornar-se importantes ferramentas de diagnóstico e estudo de patologias, estimando-se um crescimento do volume de informação gerado anualmente de Terabytes para Petabytes. Verifica-se ainda que os equipamentos de aquisição e os locais nos quais se podem encontrar imagens médicas em formato digital têm vindo a tornar-se cada vez mais dispersos. Esta dispersão, associada a diferentes necessidades de fluxo de informação, levantam sérios problemas de organização bem como de acesso integrado aos dados. Nesta dissertação é estudado o uso de tecnologias cloud computing com o objectivo de promover a pesquisa e acesso integrado a informação imagiológica dispersas por várias instituições.Over the last decades, the production of digital medical imaging has been increasing. Moreover, the equipments are more accessible and the places where is possible to produce medical images have become more dispersed. This dispersion, associated with different needs of information flow imposes serious problems and challenges, namely issues related with organization and integrated access to data. Here, the information systems that support the storage and share of medical images have become important diagnostic and therapeutic tools. In this thesis, the cloud computing paradigm is studied with goal of promoting the search and integrated access to imagiological information spread over several facilities

Recuperação de informação multimodal em repositórios de imagem médica

The proliferation of digital medical imaging modalities in hospitals and other diagnostic facilities has created huge repositories of valuable data, often not fully explored. Moreover, the past few years show a growing trend of data production. As such, studying new ways to index, process and retrieve medical images becomes an important subject to be addressed by the wider community of radiologists, scientists and engineers. Content-based image retrieval, which encompasses various methods, can exploit the visual information of a medical imaging archive, and is known to be beneficial to practitioners and researchers. However, the integration of the latest systems for medical image retrieval into clinical workflows is still rare, and their effectiveness still show room for improvement. This thesis proposes solutions and methods for multimodal information retrieval, in the context of medical imaging repositories. The major contributions are a search engine for medical imaging studies supporting multimodal queries in an extensible archive; a framework for automated labeling of medical images for content discovery; and an assessment and proposal of feature learning techniques for concept detection from medical images, exhibiting greater potential than feature extraction algorithms that were pertinently used in similar tasks. These contributions, each in their own dimension, seek to narrow the scientific and technical gap towards the development and adoption of novel multimodal medical image retrieval systems, to ultimately become part of the workflows of medical practitioners, teachers, and researchers in healthcare.A proliferação de modalidades de imagem médica digital, em hospitais, clínicas e outros centros de diagnóstico, levou à criação de enormes repositórios de dados, frequentemente não explorados na sua totalidade. Além disso, os últimos anos revelam, claramente, uma tendência para o crescimento da produção de dados. Portanto, torna-se importante estudar novas maneiras de indexar, processar e recuperar imagens médicas, por parte da comunidade alargada de radiologistas, cientistas e engenheiros. A recuperação de imagens baseada em conteúdo, que envolve uma grande variedade de métodos, permite a exploração da informação visual num arquivo de imagem médica, o que traz benefícios para os médicos e investigadores. Contudo, a integração destas soluções nos fluxos de trabalho é ainda rara e a eficácia dos mais recentes sistemas de recuperação de imagem médica pode ser melhorada. A presente tese propõe soluções e métodos para recuperação de informação multimodal, no contexto de repositórios de imagem médica. As contribuições principais são as seguintes: um motor de pesquisa para estudos de imagem médica com suporte a pesquisas multimodais num arquivo extensível; uma estrutura para a anotação automática de imagens; e uma avaliação e proposta de técnicas de representation learning para deteção automática de conceitos em imagens médicas, exibindo maior potencial do que as técnicas de extração de features visuais outrora pertinentes em tarefas semelhantes. Estas contribuições procuram reduzir as dificuldades técnicas e científicas para o desenvolvimento e adoção de sistemas modernos de recuperação de imagem médica multimodal, de modo a que estes façam finalmente parte das ferramentas típicas dos profissionais, professores e investigadores da área da saúde.Programa Doutoral em Informátic

Implementación de sistemas PACS mediante redes P2P y almacenamiento en “la nube”

El diagnóstico medico por imágenes representa en nuestros días, uno de los pilares fundamentales para el tratamiento de enfermedades. El presente trabajo describe la metodología e integración de tecnologías para acelerar la velocidad de transferencias de imágenes de alta calidad diagnóstica sin pérdida de información, sobre conexiones de datos de baja velocidad asimétricas; utilizando tecnología P2P. Logramos, de esta manera, vincular regiones geográficas diversas y equipamiento diverso.Sociedad Argentina de Informática e Investigación Operativ

NEArBy : normalização lexical na pesquisa de imagens cerebrais com atlas

Mestrado em Engenharia de Computadores e TelemáticaBrain atlases have been used as spatial references to classify and tag either structural or functional topological information from brain images. Semantic information obtained from the existing image data is thus spatially mapped according the atlas descriptors. However the process of classifying and tagging brain images using an atlas is often tedious and mostly dependent on human observation and validation. At the same time, even when available, it is often difficult to use, particularly when using standard query and retrieve services in modern imaging repositories (e.g. DICOM based PACS). In this work we propose NEArBy, a cloud based solution that provides query and retrieve services based on brain atlas semantics that can be easily integrated in existing DICOM based imaging repositories. Using a web interface, NEArBy supports not only typical DICOM query retrieve searches but also query tokens matching the brain atlas dictionary. To automate the semantic tagging of the brain images we rely on external methods to identify relevant spatial features that are later labelled using standard brain atlas. Being DICOM a tag based standard, atlas related tags are then privately embedded into DICOM files as NEArBy JSON descriptors using lexicon as proposed in NeuroLex. These descriptors encode the mapping between feature type, spatial location in the atlas and the respective atlas tag. JSON encoded tags are also suitable for indexing by a medical imaging Q/R tool such as Dicoogle allowing queries based both on standard DICOM tags and specifically on atlas related tokens included by NEArBy middleware. NEArBy provides a new way to perform non- patient centric queries over neuro-imaging repositories using technical and atlas based topological information. During this dissertation, the NEArBy potential usage is illustrated over a set of functional magnetic resonance imaging (fMRI) datasets using the web user interface to formulate the queries with atlas related criteria and access the retrieved results.Brain atlases have been used as spatial references to classify and tag either structural or functional topological information from brain images. Semantic information obtained from the existing image data is thus spatially mapped according the atlas descriptors. However the process of classifying and tagging brain images using an atlas is often tedious and mostly dependent on human observation and validation. At the same time, even when available, it is often difficult to use, particularly when using standard query and retrieve services in modern imaging repositories (e.g. DICOM based PACS). In this work we propose NEArBy, a cloud based solution that provides query and retrieve services based on brain atlas semantics that can be easily integrated in existing DICOM based imaging repositories. Using a web interface, NEArBy supports not only typical DICOM query retrieve searches but also query tokens matching the brain atlas dictionary. To automate the semantic tagging of the brain images we rely on external methods to identify relevant spatial features that are later labelled using standard brain atlas. Being DICOM a tag based standard, atlas related tags are then privately embedded into DICOM files as NEArBy JSON descriptors using lexicon as proposed in NeuroLex. These descriptors encode the mapping between feature type, spatial location in the atlas and the respective atlas tag. JSON encoded tags are also suitable for indexing by a medical imaging Q/R tool such as Dicoogle allowing queries based both on standard DICOM tags and specifically on atlas related tokens included by NEArBy middleware. NEArBy provides a new way to perform non- patient centric queries over neuro-imaging repositories using technical and atlas based topological information. During this dissertation, the NEArBy potential usage is illustrated over a set of functional magnetic resonance imaging (fMRI) datasets using the web user interface to formulate the queries with atlas related criteria and access the retrieved results

Implementación de sistemas PACS mediante redes P2P y almacenamiento en “la nube”

El diagnóstico medico por imágenes representa en nuestros días, uno de los pilares fundamentales para el tratamiento de enfermedades. El presente trabajo describe la metodología e integración de tecnologías para acelerar la velocidad de transferencias de imágenes de alta calidad diagnóstica sin pérdida de información, sobre conexiones de datos de baja velocidad asimétricas; utilizando tecnología P2P. Logramos, de esta manera, vincular regiones geográficas diversas y equipamiento diverso.Sociedad Argentina de Informática e Investigación Operativ

Plataforma web de monitorização de dose de radiação em imagem clínica

Mestrado em Engenharia de Computadores e TelemáticaA monitorização sistemática da exposição dos cidadãos à radiação ionizante associada aos procedimentos imagiológicos é fundamental para garantir a qualidade dos serviços clínicos. Esta atividade é importante no controlo de desempenho, na optimização de protocolos e na rápida rectificação das práticas erradas. Em teoria, os episódios de radiodiagnóstico devem sempre manter a exposição à radiação tão baixa quanto razoavelmente possível (princípio ALARA), preservando a qualidade de diagnóstico. Os sistemas de monitorização de dose automáticos podem ser úteis em todas as fases de procedimentos radiológicos, ajudando os profissionais de saúde a melhorar os seus comportamentos de dosimetria. Mais ainda, a exposição aplicada nos procedimentos deve ser planeada individualmente, o que significa que a monitorização da dose também deverá ser. Além disso, o acesso integrado à história imagiológica do paciente pode ser útil para efetuar um melhor tratamento. No entanto, muitos dos atuais sistemas de informação não permitem efetuar análise de dose e a sua monitorização contínua é rara. Neste contexto, o contributo desta dissertação é o Dose Center, uma ferramenta centrada no paciente que permite monitorizar e analisar a dose de radiação. Ela tem capacidade para extrair informação proveniente de diferentes fontes e permite uma visualização integrada de toda a informação relativa aos pacientes, quais os estudos realizados, a dose efetiva e cumulativa de radiação. A ferramenta permite ainda sinalizar os casos que excedam os limites pré-definidos de radiação, uma inequívoca contribuição para a melhoria da segurança do paciente.Systematic monitoring of radiation dose exposure is a key factor to increase the quality of radiological services. This activity may lead to performance control, protocol optimization and rapid rectification of wrong practices. Moreover, dose monitoring can help the healthcare professionals to improve their dosimetric behaviors. In theory, radiodiagnostic episodes should always keep the radiation exposure as low as reasonably achievable (ALARA), while preserving the quality of diagnosis. Hence, the applied exposure in the radiology departments shall be individually planned, which means that the dose monitoring should be performed individually to ensure an appropriate dose usage. Automatic dose monitoring systems may be helpful during all the phases of radiologic procedures and the integrated access to imagiologic history may be helpful to do a better patient treatment. However, many of actual healthcare information systems do not allow dose analysis and its continuous monitoring is rare. In this context, this document proposes the Dose Center, a software platform that provides a patient-centric radiation dose analysis and a monitoring system that was designed to automatically extract and analyze dose reports captured from distinct data sources. It provides several data analytics views like, for instance, by modality or patient, including the studies effective and cumulative dose radiation. Cases exceeding the radiation thresholds are signalizing, contributing this way to improve the patient safety