Correction of image distortions in magnetic resonance imaging: intensity inhomogeneities correction and evaluation

Usando a técnica de imagem por ressonância magnética (IRM) é possível adquirir uma vasta quantidade de informação tanto anatómica como funcional, obtendo-se assim melhores diagnósticos e ajuda no tratamento de pacientes. O diagnóstico pode ser melhorado utilizando ferramentas de análise automática das imagens. Essa análise poderá sofrer a influência de alguns artefactos, sendo um deles a distorção de intensidades, causado por não-uniformidades da excitação e recepção a quando do uso do pulso de rádio frequência (RF), assim com também é devido às interacções electrodinâmicas com o objecto. O desejo de resolver este problema levou ao desenvolvimento de vários algoritmos com diferentes bases teóricas. Realizaram-se ainda variadas avaliações e comparações entre eles, mas, até agora, não foi encontrado um algoritmo que possa ser considerado inquestionavelmente o melhor para todos os tipos de dados adquiridos com IRM. Para resolver esta ambiguidade, neste trabalho não é proposto um novo algoritmo. Foram usados quatro conhecidos algoritmos para a correcção destas não-uniformidades, de maneira a tentar encontrar o melhor método de correcção para cada tipo de aquisição de IRM. Foram utilizadas imagens simuladas com não-uniformidades na intensidade, geradas com parâmetros de sequência idênticos às aquisições reais, de maneira a se puder obter imagens com intensidades próximas das imagens clínicas e observar como este factor influencia os algoritmos de correcção. As imagens corrigidas foram avaliadas com diversos parametros relacionados com a qualidade de imagem. Verificou-se que a performance de cada algoritmo varia dependendo do tipo de imagem que está a corrigir. Não foi encontrado um algoritmo que fosse superior a todos os outros para todas a situações. A avaliação do melhor algoritmo foi realizada tendo em conta o propósito das imagens e o parâmetro que se pretende melhorar. A escolha do método mais adequado para um tipo específico de imagens provou ser um passo importante quando se procura melhorar uma análise subsequente da imagem, neste caso segmentação. Estas melhorias poderão ser usadas para apoiar decisões médicas e melhorar assim os cuidados de saúde. Palavras-chave: Correcção da não-homogeneidade, MRI, segmentação, qualidade de imagem, processamento de image

Canine Nasal Aspergillosis: review of diagnostic challenges about a case report

Canine Nasal Aspergillosis: review of diagnostic challenges about a case repor

Self-assembled bioactive colloidal gels as injectable multiparticle shedding platforms

Self-assembled colloidal gels are highly versatile 3D nanocluster platforms with potential to overcome the rapid clearing issues associated with standard free nanotherapeutics administration. However, the development of nanoassembled colloidal gels exhibiting autonomous multiparticle release from the bulk particle network remains elusive. Herein, we generated multiparticle colloidal gels from two nanosized building blocks: cationic poly(d,l-lactide-co-glycolide)-polyethylenimine (PLGA-PEI) nanoparticles and anionic zein-hyaluronan (HA) nanogels that assemble into macrosized 3D constructs via attractive electrostatic forces. The resulting colloidal gels exhibited high stability in complex culture medium as well as fit-to-shape moldable properties and injectability. Moreover, nanoassembled colloidal gels encapsulated bioactive quercetin flavonoids with high loading efficacy and presented remarkable anti-inflammatory activities, reducing key proinflammatory biomarkers in inflammation-activated macrophages. More importantly, because of their rationally selected building blocks zein-HA/PLGA-PEI, self-assembled colloidal platforms displayed autonomous multiparticle shedding. Both positive and negative particles released from the colloidal system were efficiently internalized by macrophages along time as evidenced by quantitative particle uptake analysis. Overall, the generated nanostructured gels represent an implantable versatile platform for focalized multiparticle delivery. In addition, the possibility to combine a higher number of particle species with different properties or stimuli-responsiveness enables the manufacturing of combinatorial nanostructured gels for numerous biomedical applications.publishe

Classification of Huntington's Disease Stage with Features Derived from Structural and Diffusion-Weighted Imaging

The purpose of this study was to classify Huntington's disease (HD) stage using support vector machines and measures derived from T1- and diffusion-weighted imaging. The effects of feature selection approach and combination of imaging modalities are assessed. Fourteen premanifest-HD individuals (Pre-HD; on average > 20 years from estimated disease onset), eleven early-manifest HD (Early-HD) patients, and eighteen healthy controls (HC) participated in the study. We compared three feature selection approaches: (i) whole-brain segmented grey matter (GM; voxel-based measure) or fractional anisotropy (FA) values; (ii) GM or FA values from subcortical regions-of-interest (caudate, putamen, pallidum); and (iii) automated selection of GM or FA values with the algorithm Relief-F. We assessed single- and multi-kernel approaches to classify combined GM and FA measures. Significant classifications were achieved between Early-HD and Pre-HD or HC individuals (accuracy: generally, 85% to 95%), and between Pre-HD and controls for the feature FA of the caudate ROI (74% accuracy). The combination of GM and FA measures did not result in higher performances. We demonstrate evidence on the high sensitivity of FA for the classification of the earliest Pre-HD stages, and successful distinction between HD stages

Erratum to: Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data (Journal of Medical Systems, (2016), 40, 11, (243), 10.1007/s10916-016-0594-2)

O artigo encontra-se disponível em: http://hdl.handle.net/1822/52841The original version of this article unfortunately contained an error. The correct spelling of Augustin Ibañez should be Agustin Ibañez.info:eu-repo/semantics/publishedVersio

Processing time reduction: an application in living human high-resolution diffusion magnetic resonance imaging data

Um errata deste artigo encontra-se disponível em: http://hdl.handle.net/1822/52993High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50 % without losing signal’s quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data.We thank the financial support by QREN, FEDER, COMPETE, Investigador FCT, FCT Ciencia 2007, FCT PTDC/SAU-BEB/100147/2008, FCT Project Scope UID/CEC/00319/2013, and the ERASMUS projects (FCT stands for "Fundacao para a Ciencia e Tecnologia"). We are thankful the relevant scientific conversations with Alard Roebroeck, Rainer Goebel, Van Wedeen, and Gina Caetano. Data collection for this work was in part from "Human Connectome Project" (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.info:eu-repo/semantics/publishedVersio