PACS de patologia: uma plataforma centralizada para a gestão de imagem médica de patologia

The clinical area of digital Pathology is still giving its first steps in the development of interoperable solutions that enable the distributed acquisition, storage, and visualization of medical images, including diagnostic support tools. Nowadays, digital management solutions use proprietary image formats and communication protocols that are not compatible with the DICOM standard. Moreover, the available technologies are not mature enough to support the practice of medicine in an area where scanned images can reach several gigapixels, requiring new engineering approaches to support huge volumes of data, in the order of gigabytes per study, that need to be consumed in real time. This dissertation aims to research and develop new technologies and associated information systems, capable of supporting the digital acquisition of pathology images, their centralized archive, sharing, and collaborative review with decision support tools. The result is an innovative web solution, focused on increasing productivity, with safe diagnostics and based on normalized protocols. A common web browser was transformed into a professional workstation that is able to access the image repository at any place and time, regardless of the operating system and without any prior installation.A área de Patologia clínica digital ainda se encontra a dar os primeiros passos no desenvolvimento de soluções interoperáveis que permitam a aquisição, arquivo e visualização distribuída da imagem, incluindo ferramentas de suporte ao diagnóstico. Os atuais cenários de revisão à distância usam aplicações proprietárias que não são interoperáveis com a norma DICOM. Isto deve-se ao facto de a tecnologia não estar suficientemente madura para apoiar a prática clínica numa área em que uma imagem digitalizada pode atingir vários giga-pixels, requerendo novas soluções de engenharia para suportar grandes volumes de dados, da ordem de gigabyte por estudo, que necessitam de ser consumidos remotamente em tempo real. Esta dissertação teve como objetivo estudar e desenvolver tecnologias e sistemas de informação que permitam a aquisição digital da imagem de patologia, o seu arquivo centralizado, a partilha e revisão colaborativa com ferramentas de suporte à decisão. O resultado é uma solução Web inovadora, de elevada produtividade, diagnóstico seguro e baseada em processos e protocolos normalizados. Um Web-browser comum foi transformado numa estação de trabalho capaz de aceder ao arquivo em qualquer altura e qualquer lugar, independentemente do sistema operativo, computador ou dispositivo móvel.Mestrado em Engenharia Informátic

Toward Uniform Implementation Of Parametric Map Digital Imaging And Communication In Medicine Standard In Multisite Quantitative Diffusion Imaging Studies

This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in Medicine (DICOM) in clinical trials that utilize quantitative diffusion-weighted imaging (DWI). Participating sites used a multivendor DWI DICOM dataset of a single phantom to generate parametric maps (PMs) of the apparent diffusion coefficient (ADC) based on two models. The results were evaluated for numerical consistency among models and true phantom ADC values, as well as for consistency of metadata with attributes required by the DICOM standards. This analysis identified missing metadata descriptive of the sources for detected numerical discrepancies among ADC models. Instead of the DICOM PM object, all sites stored ADC maps as DICOM MR objects, generally lacking designated attributes and coded terms for quantitative DWI modeling. Source-image reference, model parameters, ADC units and scale, deemed important for numerical consistency, were either missing or stored using nonstandard conventions. Guided by the identified limitations, the DICOM PM standard has been amended to include coded terms for the relevant diffusion models. Open-source software has been developed to support conversion of site-specific formats into the standard representation

Development and application in clinical practice of Computer-aided Diagnosis systems for the early detection of lung cancer

Lung cancer is the main cause of cancer-related deaths both in Europe and United States, because often it is diagnosed at late stages of the disease, when the survival rate is very low if compared to first asymptomatic stage. Lung cancer screening using annual low-dose Computed Tomography (CT) reduces lung cancer 5-year mortality by about 20% in comparison to annual screening with chest radiography. However, the detection of pulmonary nodules in low-dose chest CT scans is a very difficult task for radiologists, because of the large number (300/500) of slices to be analyzed. In order to support radiologists, researchers have developed Computer aided Detection (CAD) algorithms for the automated detection of pulmonary nodules in chest CT scans. Despite proved benefits of those systems on the radiologists detection sensitivity, the usage of CADs in clinical practice has not spread yet. The main objective of this thesis is to investigate and tackle the issues underlying this inconsistency. In particular, in Chapter 2 we introduce M5L, a fully automated Web and Cloud-based CAD for the automated detection of pulmonary nodules in chest CT scans. This system introduces a new paradigm in clinical practice, by making available CAD systems without requiring to radiologists any additional software and hardware installation. The proposed solution provides an innovative cost-effective approach for clinical structures. In Chapter 3 we present our international challenge aiming at a large-scale validation of state-of-the-art CAD systems. We also investigate and prove how the combination of different CAD systems reaches performances much higher than any best stand-alone system developed so far. Our results open the possibility to introduce in clinical practice very high-performing CAD systems, which miss a tiny fraction of clinically relevant nodules. Finally, we tested the performance of M5L on clinical data-sets. In chapter 4 we present the results of its clinical validation, which prove the positive impact of CAD as second reader in the diagnosis of pulmonary metastases on oncological patients with extra-thoracic cancers. The proposed approaches have the potential to exploit at best the features of different algorithms, developed independently, for any possible clinical application, setting a collaborative environment for algorithm comparison, combination, clinical validation and, if all of the above were successful, clinical practice

NeuroProv: Provenance data visualisation for neuroimaging analyses

© 2019 Elsevier Ltd Visualisation underpins the understanding of scientific data both through exploration and explanation of analysed data. Provenance strengthens the understanding of data by showing the process of how a result has been achieved. With the significant increase in data volumes and algorithm complexity, clinical researchers are struggling with information tracking, analysis reproducibility and the verification of scientific output. In addition, data coming from various heterogeneous sources with varying levels of trust in a collaborative environment adds to the uncertainty of the scientific outputs. This provides the motivation for provenance data capture and visualisation support for analyses. In this paper a system, NeuroProv is presented, to visualise provenance data in order to aid in the process of verification of scientific outputs, comparison of analyses, progression and evolution of results for neuroimaging analyses. The experimental results show the effectiveness of visualising provenance data for neuroimaging analyses

Abdominal Aortic Aneurysm: Can the Anaconda™ Custom-Made Device Deliver? An International Perspective

INTRODUCTION: Since the introduction of endovascular aortic repair (EVAR), it has demonstrated excellent clinical outcomes and has replaced open surgical repair (OSR) in the treatment of abdominal aortic aneurysms (AAA). AAA is a life-threatening abnormal dilation of the abdominal aorta to 1.5 times its normal diameter. Several commercial EVAR devices exist on the global market, with the Terumo Aortic Fenestrated Anaconda™ graft showing superiority. In this study, we sought to provide an international perspective using multicenter-multinational data on the Anaconda™ device characteristics, design, and delivery, and discuss relevant literature. MATERIALS AND METHODS: This study represents a cross-sectional international analysis of custom-made fenestrated Anaconda™ device. Ethical and legal approval for data collection was obtained from each of the local authorities. For the statistical analysis, SPSS 28 for Windows and R were utilized. Pearson’s chi-square analysis was used to assess differences in cumulative distribution frequencies between select variables. Statistical significance for all two-tailed tests was set at p < 0.05. RESULTS: A total of 5,030 Anaconda™ devices were implanted during the 9-year study period in 27 countries spanning 6 continents. The predominant device category was bifurcate (83.6%), whereas the most common proximal ring stent configuration being standard (64.5%). All devices were delivered within 8 weeks of diagnosis, with most being implanted within 6–8 weeks (55.4%). The Anaconda™ was indicated in the 3,891 (77.4%) patients due to competitor rejection/inability to treat unsuitable/complex aortic anatomy. In the remaining 1,139 (22.6%) patients, it was utilized based on surgeon preference. Almost all devices (95%) were delivered along with a prototype. Of the total 5,030 Anaconda™ devices, 438 (8.7%) used 0–1 fenestrations, 2,349 (46.7%) used 2–3, while 2,243 (44.6%) utilized 4, 5, or 6 fenestrations. DISCUSSION: The Terumo Aortic Fenestrated Anaconda™ device features a highly unique and innovative design that enables it to treat highly complex aortic anatomy while achieving excellent results. The Anaconda™’s custom-made approach allows it to be tailored to individual patient anatomy, in addition to the device prototype provided by Terumo Aortic optimize clinical outcomes. Finally, the fenestrated Anaconda™ is a highly versatile device offering a wide range of device categories, configurations, and sizes

Software toolkit for modeling, simulation and control of soft robots

International audienceThe technological differences between traditional robotics and soft robotics have an impact on all of the modeling tools generally in use, including direct kinematics and inverse models, Jacobians, and dynamics. Due to the lack of precise modeling and control methods for soft robots, the promising concepts of using such design for complex applications (medicine, assistance, domestic robotics...) cannot be practically implemented. This paper presents a first unified software framework dedicated to modeling, simulation and control of soft robots. The framework relies on continuum mechanics for modeling the robotic parts and boundary conditions like actuators or contacts using a unified representation based on Lagrange multipliers. It enables the digital robot to be simulated in its environment using a direct model. The model can also be inverted online using an optimization-based method which allows to control the physical robots in the task space. To demonstrate the effectiveness of the approach, we present various soft robots scenarios including ones where the robot is interacting with its environment. The software has been built on top of SOFA, an open-source framework for deformable online simulation and is available at https://project.inria.fr/softrobot

XR, music and neurodiversity: design and application of new mixed reality technologies that facilitate musical intervention for children with autism spectrum conditions

This thesis, accompanied by the practice outputs,investigates sensory integration, social interaction and creativity through a newly developed VR-musical interface designed exclusively for children with a high-functioning autism spectrum condition (ASC).The results aim to contribute to the limited expanse of literature and research surrounding Virtual Reality (VR) musical interventions and Immersive Virtual Environments (IVEs) designed to support individuals with neurodevelopmental conditions. The author has developed bespoke hardware, software and a new methodology to conduct field investigations. These outputs include a Virtual Immersive Musical Reality Intervention (ViMRI) protocol, a Supplemental Personalised, immersive Musical Experience(SPiME) programme, the Assisted Real-time Three-dimensional Immersive Musical Intervention System’ (ARTIMIS) and a bespoke (and fully configurable) ‘Creative immersive interactive Musical Software’ application (CiiMS). The outputs are each implemented within a series of institutional investigations of 18 autistic child participants. Four groups are evaluated using newly developed virtual assessment and scoring mechanisms devised exclusively from long-established rating scales. Key quantitative indicators from the datasets demonstrate consistent findings and significant improvements for individual preferences (likes), fear reduction efficacy, and social interaction. Six individual case studies present positive qualitative results demonstrating improved decision-making and sensorimotor processing. The preliminary research trials further indicate that using this virtual-reality music technology system and newly developed protocols produces notable improvements for participants with an ASC. More significantly, there is evidence that the supplemental technology facilitates a reduction in psychological anxiety and improvements in dexterity. The virtual music composition and improvisation system presented here require further extensive testing in different spheres for proof of concept

Sistemas interativos e distribuídos para telemedicina

doutoramento Ciências da ComputaçãoDurante as últimas décadas, as organizações de saúde têm vindo a adotar continuadamente as tecnologias de informação para melhorar o funcionamento dos seus serviços. Recentemente, em parte devido à crise financeira, algumas reformas no sector de saúde incentivaram o aparecimento de novas soluções de telemedicina para otimizar a utilização de recursos humanos e de equipamentos. Algumas tecnologias como a computação em nuvem, a computação móvel e os sistemas Web, têm sido importantes para o sucesso destas novas aplicações de telemedicina. As funcionalidades emergentes de computação distribuída facilitam a ligação de comunidades médicas, promovem serviços de telemedicina e a colaboração em tempo real. Também são evidentes algumas vantagens que os dispositivos móveis podem introduzir, tais como facilitar o trabalho remoto a qualquer hora e em qualquer lugar. Por outro lado, muitas funcionalidades que se tornaram comuns nas redes sociais, tais como a partilha de dados, a troca de mensagens, os fóruns de discussão e a videoconferência, têm o potencial para promover a colaboração no sector da saúde. Esta tese teve como objetivo principal investigar soluções computacionais mais ágeis que permitam promover a partilha de dados clínicos e facilitar a criação de fluxos de trabalho colaborativos em radiologia. Através da exploração das atuais tecnologias Web e de computação móvel, concebemos uma solução ubíqua para a visualização de imagens médicas e desenvolvemos um sistema colaborativo para a área de radiologia, baseado na tecnologia da computação em nuvem. Neste percurso, foram investigadas metodologias de mineração de texto, de representação semântica e de recuperação de informação baseada no conteúdo da imagem. Para garantir a privacidade dos pacientes e agilizar o processo de partilha de dados em ambientes colaborativos, propomos ainda uma metodologia que usa aprendizagem automática para anonimizar as imagens médicasDuring the last decades, healthcare organizations have been increasingly relying on information technologies to improve their services. At the same time, the optimization of resources, both professionals and equipment, have promoted the emergence of telemedicine solutions. Some technologies including cloud computing, mobile computing, web systems and distributed computing can be used to facilitate the creation of medical communities, and the promotion of telemedicine services and real-time collaboration. On the other hand, many features that have become commonplace in social networks, such as data sharing, message exchange, discussion forums, and a videoconference, have also the potential to foster collaboration in the health sector. The main objective of this research work was to investigate computational solutions that allow us to promote the sharing of clinical data and to facilitate the creation of collaborative workflows in radiology. By exploring computing and mobile computing technologies, we have designed a solution for medical imaging visualization, and developed a collaborative system for radiology, based on cloud computing technology. To extract more information from data, we investigated several methodologies such as text mining, semantic representation, content-based information retrieval. Finally, to ensure patient privacy and to streamline the data sharing in collaborative environments, we propose a machine learning methodology to anonymize medical images

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