Percorrer por autor "Martins, Bruno"
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- Medical imaging and the new coronavirus: facing a disruptive threatPublication . Figueiredo, Sérgio; Martins, Bruno; Nogueira, Fábio; Ribeiro, RicardoABSTRACT - In December 2019, a novel coronavirus was identified in Wuhan (China), from a cluster of patients with unexplained pneumonia and termed SARS-CoV-2, responsible for Coronavirus Disease (COVID)-19. Medical imaging modalities play an important role in the management of COVID-19 infection. Consequently, it is necessary to identify strategies and procedures for infection control in the medical imaging department. This document aims to present the standard operating procedures (SOPs) of radiology (RD) and nuclear medicine (NM) units and to provide information about the potential contribution of the medical imaging modalities in the present COVID-19 scenario. Chest Computed Tomography (CT) is considered the prime imaging tool for COVID-19 diagnosis and follow-up. Chest X-ray (CXR), with less accuracy, seems to be an important tool, in particular where mobility is an issue. Likewise, the first reports have been published on the specific potential value of 18F-FDG PET (fluorodeoxyglucose) (positron emission tomography) imaging, predominantly related to incidental findings with ground-glass opacities and matched FDG uptake.
- Níveis de referência de diagnóstico em mamografiaPublication . Martins, Bruno; Machado, Nuno; Parafita, Rui; Carvoeiras, Pedro; Azevedo, João; Trindade, Hugo; Pinto, Ildefonso; Teixeira, NunoNíveis de exposição em exames de mamografia: 1) os NRD permitem a orientação em relação às doses a utilizar num exame radiológico específico para um doente padrão; 2) não representam um limite de dose; 3) são normalmente definidos pelo valor que engloba 75% das doses mais baixas da população alvo, para um determinado exame; 4) permitem uma triagem dos resultados observados, com o fim de evitar exposições injustificadamente altas ou baixas; 5) promover a utilização de uma pequena gama de valores que representem uma boa prática para uma imagem médica específica.
- Visual analytics for spatiotemporal eventsPublication . Silva, Ricardo Almeida; Moura Pires, João; Datia, Nuno; Santos, Maribel Yasmina; Martins, Bruno; Birra, FernandoCrimes, forest fires, accidents, infectious diseases, or human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, as it can highlight useful patterns or insights and enhance the user’ perception of phenomena. For this reason, modeling phenomena at different LoDs is needed to increase the analytical value of the data, as there is no exclusive LOD at which the data can be analyzed. Current practices work mainly on a single LoD of the phenomena, driven by the analysts’ perception, ignoring that identifying the suitable LoDs is a key issue for pointing relevant patterns. This article presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs do interesting patterns emerge, or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets supported the evaluation and validation of VAST, suggesting LoDs with different interesting spatiotemporal patterns and pointing the type of expected patterns.
- Visualising hidden spatiotemporal patterns at multiple levels of detailPublication . Silva, Ricardo Almeida; Moura Pires, João; Datia, Nuno; Santos, Maribel Yasmina; Martins, Bruno; Birra, FernandoCrimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, enhancing the user's perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected. Modeling phenomena at different LoDs is needed, as there is no exclusive LoD at which data can be analyzed. Current practices work mainly on a single LoD, driven by the analysts perception, ignoring the fact that the identification of the suitable LoDs is a key issue for pointing relevant patterns. This paper presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs patterns emerge or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets allowed the evaluation of VAST, which was able to suggest LoDs with different interesting spatiotemporal patterns and the type of expected patterns.