Atherosclerotic plaque characterization using plaque area variation in IVUS images during compression: a computational investigation

INTRODUCTION: The rupture of atherosclerotic plaques causes millions of death yearly. It is known that the kind of predominant tissue is associated with its dangerousness. In addition, the mechanical properties of plaques have been proved to be a good parameter to characterize the type of tissue, important information for therapeutic decisions. METHODS: Therefore, we present an alternative and simple way to discriminate tissues. The procedure relies on computing an index, the ratio of the plaque area variation of a suspecting plaque, using images acquired with vessel and plaques, pre and post-deformation, under different intraluminal pressure. Numerical phantoms of coronary cross-sections with different morphological aspects, and simulated with a range of properties, were used for evaluation. RESULTS: The outcomes provided by this index and a widely used one were compared, so as to measure their correspondence. As a result, correlations up to 99%, a strong agreement with Bland-Altman and very similar histograms between the two indices, have shown a good level of equivalence between the methods. CONCLUSION: The results demonstrated that the proposed index discriminates highly lipidic from fibro-lipidic and calcified tissues in many situations, as good as the widely used index, yet the proposed method is much simpler to be computed.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Universidade Federal de São Paulo (UNIFESP) Instituto de Ciência e Tecnologia Departamento de Ciência e TecnologiaUniversidade de São Paulo Escola Politécnica Departamento de Engenharia de Telecomunicações e ControleUNIFESP, Instituto de Ciência e Tecnologia Depto. de Ciência e TecnologiaSciEL

Assessing the Scientific Research Productivity of a Brazilian Healthcare Institution: A Case Study at the Heart Institute of São Paulo, Brazil

INTRODUCTION: The present study was motivated by the need to systematically assess the research productivity of the Heart Institute (InCor), Medical School of the University of São Paulo, Brazil. OBJECTIVE: To explore methodology for the assessment of institutional scientific research productivity. MATERIALS AND METHODS: Bibliometric indicators based on searches for author affiliation of original scientific articles or reviews published in journals indexed in the databases Web of Science, MEDLINE, EMBASE, LILACS and SciELO from January 2000 to December 2003 were used in this study. The retrieved records were analyzed according to the index parameters of the journals and modes of access. The number of citations was used to calculate the institutional impact factor. RESULTS: Out of 1253 records retrieved from the five databases, 604 original articles and reviews were analyzed; of these, 246 (41%) articles were published in national journals and 221 (90%) of those were in journals with free online access through SciELO or their own websites. Of the 358 articles published in international journals, 333 (93%) had controlled online access and 223 (67%) were available through the Capes Portal of Journals. The average impact of each article for InCor was 2.224 in the period studied. CONCLUSION: A simple and practical methodology to evaluate the scientific production of health research institutions includes searches in the LILACS database for national journals and in MEDLINE and the Web of Science for international journals. The institutional impact factor of articles indexed in the Web of Science may serve as a measure by which to assess and review the scientific productivity of a research institution

Tomographic reconstruction of images with Poisson noise: projection estimation.

A reconstrução tomográfica de imagens com ruído Poisson tem grandes aplicações em medicina nuclear. A demanda por informações mais complexas, como por exemplo, várias secções de um órgão, e a necessidade de reduzir a dosagem radioativa a que o paciente é submetido, requerem métodos adequados para a reconstrução de imagem com baixa contagem, no caso, baixa relação sinal/ruído. A abordagem estatística, utilizando a máxima verossimilhança (ML) e o algoritmo Expectation-Maximization (EM), produz melhores resultados do que os métodos tradicionais, pois incorpora a natureza estatística do ruído no seu modelo. A presente tese apresenta uma solução alternativa, considerando também o modelo de ruído Poisson, que produz resultados comparáveis ao do ML-EM, porém com custo computacional bem menor. A metodologia proposta consiste, basicamente, em se estimar as projeções considerando o modelo de formação das projeções ruidosas, antes do processo da reconstrução. São discutidos vários estimadores otimizados, inclusive Bayesianos. Em especial, é mostrado que a transformação de ruído Poisson em ruído aditivo Gaussiano e independente do sinal (transformação de Anscombe), conjugada à estimativa, produz bons resultados. Se as projeções puderem ser consideradas, aproximadamente, transformadas de Radon da imagem a ser reconstruída, então pode ser aplicado um dos métodos da transformada para a reconstrução tomográfica. Dentre estes métodos, o da aplicação direta da transformada de Fourier foi avaliado mais detalhadamente devido ao seu grande potencial para reconstruções rápidas com processamento vetorial e processamento paralelo. A avaliação do método proposto foi realizada através de simulações, onde foram geradas as imagens originais e as projeções com ruído Poisson. Os resultados foramcomparados com métodos clássicos como a filtragem-retroprojeção, o ART e o ML-EM. Em particular, a transformação de Anscombe conjungada ao estimador heurístico (filtro de Maeda), mostrou resultados próximos aos do ML-EM, porém com tempo de processamento bem menor. Os resultados obtidos mostram a viabilidade da presente proposta vir a ser utilizada em aplicações clínicas na medicina nuclear.Tomographic reconstruction of images with Poisson noise is in important problem in nuclear medicine. The need for more complete information, like the reconstruction of several sections of an organ, and the necessity to reduce patient absorbed radioactivity, suggest better methods to reconstruct images with low-count and low signal-to-noise ratio. Statistical approaches using Maximum Likelihood (ML) and the Expectation-Maximization (EM) algorithm lead to better results than classical methods, since ML-EM considers in its model the stochastic nature of the noise. This thesis presents an alternative solution, also using a Poisson noise model, that produces similar results as compared to ML-EM, but with much less computational cost. The proposed technique basically consists of projection estimation before reconstruction, taking into account a model for the formation of the noisy projections. Several optimal and Bayesian estimators are analysed. It is shown that the transformation of Poisson noise into Gaussian additive and independent noise (Anscombe Transformation), followed by estimation, yields good results. If the projection can be assumed as Radon transform of the image to be reconstructed, then it is possible to reconstruct using one of the transform methods. Among these methods, the Direct Fourier Method was analysed in detail, due to its applicability for fast reconstruction using array processors and parallel processing. Computer simulations were used in order to access this proposed technique. Phantoms and phantom projections with Poisson noise were generated. The results were compared with traditional methods like Filtering-Backprojection, Algebraic Rconstruction Technique (ART) and ML-EM. Specifically, the Anscombe transformation together with a heuristic estimator (Maeda\'s filter) produced results comparable to ML-EM, but spending only a fraction of the processing time

Tomographic reconstruction of images with Poisson noise: projection estimation.

A reconstrução tomográfica de imagens com ruído Poisson tem grandes aplicações em medicina nuclear. A demanda por informações mais complexas, como por exemplo, várias secções de um órgão, e a necessidade de reduzir a dosagem radioativa a que o paciente é submetido, requerem métodos adequados para a reconstrução de imagem com baixa contagem, no caso, baixa relação sinal/ruído. A abordagem estatística, utilizando a máxima verossimilhança (ML) e o algoritmo Expectation-Maximization (EM), produz melhores resultados do que os métodos tradicionais, pois incorpora a natureza estatística do ruído no seu modelo. A presente tese apresenta uma solução alternativa, considerando também o modelo de ruído Poisson, que produz resultados comparáveis ao do ML-EM, porém com custo computacional bem menor. A metodologia proposta consiste, basicamente, em se estimar as projeções considerando o modelo de formação das projeções ruidosas, antes do processo da reconstrução. São discutidos vários estimadores otimizados, inclusive Bayesianos. Em especial, é mostrado que a transformação de ruído Poisson em ruído aditivo Gaussiano e independente do sinal (transformação de Anscombe), conjugada à estimativa, produz bons resultados. Se as projeções puderem ser consideradas, aproximadamente, transformadas de Radon da imagem a ser reconstruída, então pode ser aplicado um dos métodos da transformada para a reconstrução tomográfica. Dentre estes métodos, o da aplicação direta da transformada de Fourier foi avaliado mais detalhadamente devido ao seu grande potencial para reconstruções rápidas com processamento vetorial e processamento paralelo. A avaliação do método proposto foi realizada através de simulações, onde foram geradas as imagens originais e as projeções com ruído Poisson. Os resultados foramcomparados com métodos clássicos como a filtragem-retroprojeção, o ART e o ML-EM. Em particular, a transformação de Anscombe conjungada ao estimador heurístico (filtro de Maeda), mostrou resultados próximos aos do ML-EM, porém com tempo de processamento bem menor. Os resultados obtidos mostram a viabilidade da presente proposta vir a ser utilizada em aplicações clínicas na medicina nuclear.Tomographic reconstruction of images with Poisson noise is in important problem in nuclear medicine. The need for more complete information, like the reconstruction of several sections of an organ, and the necessity to reduce patient absorbed radioactivity, suggest better methods to reconstruct images with low-count and low signal-to-noise ratio. Statistical approaches using Maximum Likelihood (ML) and the Expectation-Maximization (EM) algorithm lead to better results than classical methods, since ML-EM considers in its model the stochastic nature of the noise. This thesis presents an alternative solution, also using a Poisson noise model, that produces similar results as compared to ML-EM, but with much less computational cost. The proposed technique basically consists of projection estimation before reconstruction, taking into account a model for the formation of the noisy projections. Several optimal and Bayesian estimators are analysed. It is shown that the transformation of Poisson noise into Gaussian additive and independent noise (Anscombe Transformation), followed by estimation, yields good results. If the projection can be assumed as Radon transform of the image to be reconstructed, then it is possible to reconstruct using one of the transform methods. Among these methods, the Direct Fourier Method was analysed in detail, due to its applicability for fast reconstruction using array processors and parallel processing. Computer simulations were used in order to access this proposed technique. Phantoms and phantom projections with Poisson noise were generated. The results were compared with traditional methods like Filtering-Backprojection, Algebraic Rconstruction Technique (ART) and ML-EM. Specifically, the Anscombe transformation together with a heuristic estimator (Maeda\'s filter) produced results comparable to ML-EM, but spending only a fraction of the processing time

Anais do IX SIBGRAPI (1996) 189-196 Automatic Detection of the Craniometric Points for Craniofacial Identification

An automatic method for the detection of craniometric points in video based skull images is presented. This method assumes that skull images are in frontal view and erect posture of the head. An acquisition protocol is described, that facilitates the segmentation process. Although the proposed method uses simple algorithms of the image processing theory, it produces good results for undamaged skulls and is potentially very useful in the craniofacial identification process