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

TractoR: Magnetic Resonance Imaging and Tractography with R

Statistical techniques play a major role in contemporary methods for analyzing magnetic resonance imaging (MRI) data. In addition to the central role that classical statistical methods play in research using MRI, statistical modeling and machine learning techniques are key to many modern data analysis pipelines. Applications for these techniques cover a broad spectrum of research, including many preclinical and clinical studies, and in some cases these methods are working their way into widespread routine use.In this manuscript we describe a software tool called TractoR (for “Tractography with R”), a collection of packages for the R language and environment, along with additional infrastructure for straightforwardly performing common image processing tasks. TractoR provides general purpose functions for reading, writing and manipulating MR images, as well as more specific code for fitting signal models to diffusion MRI data and performing tractography, a technique for visualizing neural connectivity

Compressão de imagem médica para arquivos de alto desempenho

Information systems and the medical subject are two widespread topics that have interwoven so that medical help could become more efficient. This relation has bred the PACS and the international standard DICOM directed to the organization of digital medical information. The concept of image compression is applied to most images throughout the web. The compression formats used for medical imaging have become outdated. The new formats that have been developed in the past few years are candidates for replacing the old ones in such contexts, possibly enhancing the process. Before they are adopted, an evaluation should be carried out that validates their admissibility. This dissertation reviews the state of the art of medical imaging information systems, namely PACS systems and the DICOM standard. Furthermore, some topics of image compression are covered, such as the metrics for evaluating the algorithms’ performance, finalizing with a survey of four modern formats: JPEG XL, AVIF, and WebP. Two software projects were developed, where the first one carries out an analysis of the formats based on the metrics, using DICOM datasets and producing results that can be used for creating recommendations on the format’s use. The second consists of an application that encodes and decodes medical images with the formats covered in this dissertation. This proof-of-concept works as a medical imaging archive for the storage, distribution, and visualization of compressed data.Os sistemas de informação e o assunto médico são dois temas difundidos que se entrelaçam para que a ajuda médica se torne mais eficiente. Essa relação deu origem ao PACS e ao padrão internacional DICOM direcionado à organização da informação médica digital. O conceito de compressão de imagem é aplicado à maioria das imagens em toda a web. Os formatos de compressão usados para imagens médicas tornaram-se desatualizados. Os novos formatos desenvolvidos nos últimos anos são candidatos a substituir os antigos nesses contextos, possivelmente potencializando o processo. Antes de serem adotados, deve ser realizada uma avaliação que valide sua admissibilidade. Esta dissertação revisa o estado da arte dos sistemas de informação de imagens médicas, nomeadamente os sistemas PACS e a norma DICOM. Além disso, são abordados alguns tópicos de compressão de imagens, como as métricas para avaliação do desempenho dos algoritmos, finalizando com um levantamento de três formatos modernos: JPEG XL, AVIF e WebP. Foram desenvolvidos dois projetos de software, onde o primeiro realiza uma análise dos formatos com base nas métricas, utilizando conjuntos de dados DICOM e produzindo resultados que podem ser utilizados para a criação de recomendações sobre o uso do formato. A segunda consiste numa aplicação capaz de codificar e descodificar imagens médicas com os formatos abordados nesta dissertação. Essa prova de conceito funciona como um arquivo de imagens médicas para armazenamento, distribuição e visualização de dados compactados.Mestrado em Engenharia de Computadores e Telemátic

Mathematical Modeling of the Human Brain

This open access book bridges common tools in medical imaging and neuroscience with the numerical solution of brain modelling PDEs. The connection between these areas is established through the use of two existing tools, FreeSurfer and FEniCS, and one novel tool, the SVM-Tk, developed for this book. The reader will learn the basics of magnetic resonance imaging and quickly proceed to generating their first FEniCS brain meshes from T1-weighted images. The book's presentation concludes with the reader solving a simplified PDE model of gadobutrol diffusion in the brain that incorporates diffusion tensor images, of various resolution, and complex, multi-domain, variable-resolution FEniCS meshes with detailed markings of anatomical brain regions. After completing this book, the reader will have a solid foundation for performing patient-specific finite element simulations of biomechanical models of the human brain

Decision support system for cardiovascular problems

The DISHEART project aims at developing a new computer based decision support system (DSS) integrating medical image data, modelling, simulation, computational Grid technologies and artificial intelligence methods for assisting clinical diagnosis and intervention in cardiovascular problems. The RTD goal is to improve and link existing state of the art technologies in order to build a computerised cardiovascular model for the analysis of the heart and blood vessels. The resulting DISHEART DSS interfaces computational biomechanical analysis tools with the information coming from multimodal medical images. The computational model is coupled to an artificial neural network (ANN) based decision model that can be educated for each particular patient with data coming from his/her images and/or analyses. The DISHEART DSS system is validated in trials of clinical diagnosis, surgical intervention and subject-specific design of medical devices in the cardiovascular domain. The DISHEART DSS also contributes to a better understanding of cardiovascular morphology and function as inferred from routine imaging examinations. Four reputable medical centers in Europe took an active role in the validation and dissemination of the DISHEART DSS as well as the elaboration of computational material and medical images. The integrated DISHEART DSS supports health professionals in taking promptly the best possible decision for prevention, diagnosis and treatment. Emphasis was put in the development of userfriendly, fast and reliable tools and interfaces providing access to heterogeneous health information sources, as well as on new methods for decision support and risk analysis. The use of Grid computing technology is essential in order to optimise and distribute the heavy computational work required for physical modelling and numerical simulations and especially for the parametric analysis required for educating the DSS for every particular application. The four end user SMEs participating in the project benefits from the new DISHEART DSS. The companies COMPASS, QUANTECH and Heartcore will market the DSS among public and private organizations related to the cardiovascular field. EndoArt will exploit the DISHEART DSS as a support for enhanced design and production of clinical devices. The partnership was sought in order to gather the maximum complementary of skills for the successful development of the project Disheart DSS, requiring experts in Mechanical sciences, Medical sciences, Informatic, and FEM technique to grow up the testes.Postprint (published version

Description and Experience of the Clinical Testbeds

This deliverable describes the up-to-date technical environment at three clinical testbed demonstrator sites of the 6WINIT Project, including the adapted clinical applications, project components and network transition technologies in use at these sites after 18 months of the Project. It also provides an interim description of early experiences with deployment and usage of these applications, components and technologies, and their clinical service impact

Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML.

Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address