No description available.

La pratique clinique a été profondément transformée par l'explosion technologique, ces dernières décades, des techniques d'imagerie médicale. L'expansion de la radiologie interventionnelle a ainsi rendu possible des procédures dites « minimalement invasives » au cours desquelles la thérapie est délivrée directement au niveau de la région pathologique via des micro-outils guidés par imagerie à travers le système vasculaire. Des systèmes dits « C-arm », générant une imagerie rayons X planaire temps-réelle en faible dose, sont utilisés pour le guidage. Ils ont offert plus récemment la possibilité d'une visualisation tridimensionnelle par le biais d'acquisitions tomographiques. C'est dans ce contexte de reconstruction tomographique que s'inscrivent ces travaux de thèse. Ils s'attèlent en particulier à corriger les artefacts de mouvement dus aux variations temporelles des vaisseaux injectés et se concentrent sur un aspect central de la tomographie, à savoir l'échantillonnage angulaire. La théorie du compressed sensing identifie les conditions sous lesquelles des données sous-échantillonnées peuvent être reconstruites en minimisant une fonctionnelle qui combine un terme de fidélité quadratique et une contrainte parcimonieuse. S'appuyant sur cette théorie, un formalisme original de reconstruction est proposé : il repose sur la rétroprojection filtrée itérative, les algorithmes proximaux, la minimisation de normes L1 et l'homotopie. Ce formalisme est ensuite dérivé pour intégrer différentes contraintes spatiales et temporelles. Une telle stratégie s'avère plus performante que la rétroprojection filtrée analytique utilisée dans la pratique clinique, permettant la réduction d'artefacts de mouvement et d'échantillonnage dans des cas cliniques bien identifiés de l'imagerie cérébrale et abdominale. Les résultats obtenus soulignent l'une des principales contributions de ce travail, à savoir : l'importance de l'homotopie, en supplément de la régularisation, pour améliorer la qualité image, un gain indispensable dans le domaine d'applicabilité / Medical imaging has known great advances over the past decades to become a powerful tool for the clinical practice. It has led to the tremendous growth of interventional radiology, in which medical devices are inserted and manipulated under image guidance through the vascular system to the pathology location and then used to deliver the therapy. In these minimally-invasive procedures, X-ray guidance is carried out with C-arm systems through two-dimensional real-time projective low-dose images. More recently, three-dimensional visualization via tomographic acquisition has also become available. This work tackles tomographic reconstruction in the aforementioned context. More specifically, it deals with the correction of motion artifacts that originate from the temporal variations of the contrast-enhanced vessels and thus tackles a central aspect of tomography: data (angular) sampling. The compressed sensing theory identifies conditions under which subsampled data can be recovered through the minimization of a least-square data fidelity term combined with sparse constraints. Relying on this theory, an original reconstruction framework is proposed based on iterative filtered backprojection, proximal splitting, `1-minimization and homotopy. This framework is derived for integrating several spatial and temporal penalties. Such a strategy is shown to outperform the analytical filtered backprojection algorithm that is used in the current clinical practice by reducing motion and sampling artifacts in well-identified clinical cases, with focus on cerebral and abdominal imaging. The obtained results emphasize one of the key contributions of this work that is the importance of homotopy in addition to regularization, to provide much needed image quality improvement in the suggested domain of applicability.

O objetivo deste trabalho foi avaliar a influência das correções de atenuação e espalhamento na reconstrução quantitativa em SPECT. O estudo foi baseado em diversas simulações de Monte Carlo, com ênfase especial no modelo torso-cardíaco matemático (MCAT). Para a reconstrução, foi utilizado o algoritmo iterativo ML-EM com projetor-retroprojetor modificado pelo mapa de atenuação. Para avaliar a correção de espalhamento, foram simulados os espectros energéticos, com múltiplas ordens de espalhamento Compton. O método da dupla janela de energia (Jaszczak) foi aplicado, devido a sua simplicidade, e as imagens corrigidas foram comparadas com as de fótons primários. Foram analisadas as escolhas das janelas do fotopico e espalhamento, além da dependência do fator de espalhamento k com a distribuição de atividades do objeto. Duas abordagens foram adotadas para a obtenção dos mapas de atenuação: a estimativa do mapa uniforme diretamente dos dados de emissão, sem o uso de imagens de transmissão; e o borramento de mapas não-uniformes, reconstruídos a partir das projeções por transmissão. A estimativa do mapa de atenuação diretamente dos sinogramas de emissão baseou-se nas condições de consistência da transformada de Radon atenuada. Neste caso, foram estudados os efeitos de diferentes contagens e vários coeficientes de atenuação iniciais sobre as imagens corrigidas. Os mapas de atenuação não-uniformes foram borrados com um \"kernei\" gaussiano, aplicados nas correções e os efeitos na quantificação foram analisados. Os espectros energéticos emitidos pelo modelo MCAT mostraram que os fótons espalhados não poderiam ser excluídos a contento, mesmo que fossem utilizadas janelas de aquisição estreitas sobre o fotopico. Em relação a correção de Jaszczak, verificou-se que a escolha das janelas de fotopico e espalhamento é crucial e confirmou-se que o valor de k é altamente dependente do objeto examinado. Dada uma estimativa inicial do mapa de atenuação, o uso das condições de consistência para estimar o mapa de atenuação uniforme, consistente com os dados de emissão do modelo MCAT simulado, resultou sempre em uma mesma forma, para quaisquer valores iniciais do conjunto de parâmetros. Apesar do erro diminuir com o aumento da contagem, o melhor coeficiente de atenuação não pôde ser obtido, mesmo em altas contagens. Isto se deve a presença dos fótons espalhados, que alteraram a solução das condições de consistência, reduzindo as dimensões do mapa. Os resultados indicaram que a correção de espalhamento é o fator mais importante na reconstrução quantitativa em SPECT. Com referência aos efeitos quantitativos da correção de atenuação, não foram observadas diferenças significativas com a utilização dos mapas borrados, enquanto que a correção com mapas uniformes mostrou-se menos eficaz. / The goal of this work is to evaluate the influence of scatter and attenuation correction methods in quantitative SPECT reconstruction. The study was based on several Monte Carlo simulations, with special emphasis on the mathematical cardiac-torso phantom (MCAT). Iterative ML-EM reconstruction with modified projector-backprojector was used. To evaluate the scatter correction, energy spectra were simulated for SPECT imaging including multiple order Compton scattered photons. The dual energy window method proposed by Jaszczak was applied and scatter corrected images were compared with primary photons images. The choice of the scattering and photopeak windows and the dependence of the scatter factor k with the activity distribution were also analysed. Two approaches were adopted for obtaining the maps for attenuation correction: the estimation of the attenuation maps directly from the emission data, without transmission imaging, and the blurring of non-uniform attenuation maps, reconstructed from transmission data. The estimation of attenuation maps directly from the emission sinograms was based on the consistency conditions of attenuated Radon transform. In this case, the effects of different counting rates and various initial attenuation coefficients on the corrected images were studied. The non-uniform attenuation maps were blurred with a gaussian kernel with different variances, applied in further corrections and their effects on quantitation were examined. Analysis of energy spectra emitted from the MCAT phantom showed that scattered photons cannot be totally excluded, even when narrow acquisition windows were used. As far as the Jaszczak correction is concerned, results showed that the choice of photopeak and secondary windows is crucial and that the value of k is highly dependent on the imaged object. Given an initial estimation of the attenuation map with a constant coefficient, the use of consistency conditions to estimate the uniform map, consistent with the emission data of simulated MCAT phantom, resulted in the same shape for any set of initial parameters. In spite of the fact that the error falls with increasing counting rate, higher counts are not able to determine the best attenuation coefficient. This is due to scattered photons, which alter the solution of consistency conditions, reducing the size of estimated maps. Results indicated that the scatter correction is the most important factor inquantitative SPECT reconstruction. Furthermore, no significant differences were observed in the quantitation, when using the blurred non- uniform attenuation maps in attenuation correction, while corrections with uniform maps proved to be less efficient

Nesta tese é apresentado um novo método de reconstrução de imagens, por tomografia de transmissão, de projeções sujeitas a ruído na contagem de fótons. O método de reconstrução selecionado utiliza a técnica POCS (Projections Onto Convex Sets). A estimação das projeções originais a partir das observações ruidosas se dá por meio de quatro métodos: a) estimação utilizando o critério MAP (Maximum a Posteriori); b) filtragem nos coeficientes wavelets das projeções ruidosas; c) aplicação do filtro de Wiener pontual e d) aplicação do filtro de Goodman-Belsher. É apresentado o resultado da reconstrução após a estimação das projeções, mostrando o ISNR (Improvement Signal-to-Noise Ratio) entre as imagens reconstruídas, a partir das projeções ruidosas, com a técnica POCS, estimando as projeções e sem a realização da estimação. Foram utilizados, para reconstrução tomográfica, projeções de corpos de prova obtidos por meio de simulação e também projeções obtidas experimentalmente no minitomógrafo do CNPDIA - EMBRAPA. O uso de estimação sobre as projeções ruidosas mostrou-se eficaz para melhorar a relação sinal-ruído na imagem final, pois esse pré-processamento faz com que os conjuntos impostos pelas projeções sejam mais restritivos. Deve-se observar que a melhoria das imagens obtidas com o uso de filtragem das projeções é obtida com uma relação custo-benefício bastante baixa, pois a maior parte do custo computacional está na fase de reconstrução das imagens. / In this thesis is pesented a new method for image reconstruction, by transmission tomography, for projections under noise in the counting of photons. The selected method of reconstruction uses the POCS (Projections Onto Convex Sets) technique. The estimation of the original projections from the noisy projections observed is performed through four methods: a) estimation using the MAP (Maximum a Posteriori) criteria; b) through of filtering of the wavelets coefficients of the noisy projections; c) using the pointwise Wiener filter and d) using the Goodman-Belsher filter. We present the result of reconstruction after projection estimation, showing the ISNR (Improvement Signal-to-Noise Ratio) between the reconstructed images on noisy projections, using POCS technique after the estimated projections and without this estimation. We use, for tomographic reconstruction, test body projections obtained through simulation and also projections obtained experimentally in the minitomograph scanner of CNPDIA-EMBRAPA. The use of estimation on noisy projections demonstrated to be efficient in improving the signalnoise ratio in the final image, since this pre-processing makes the sets that projections more restrictive. We should observe that the use of projection filtering is obtained with a cost-benefit ratio rather low, since the largest part of the computational effort is in the image reconstruction phase.

This work introduces a combination of image processing and analysis methods that perform feature extraction, shape analysis and tomographic reconstruction of Electron Microscopy images. These have been implemented on images of the AIDS virus interacting with neutralizing molecules. The AIDS virus spike is the primary target of drug design as it is directly involved in infecting host cells. First, a fully automated technique is introduced that can extract sub-volumes of the AIDS virus spike and be used to build a statistical model without the need for any user supervision. Such an automatic feature extraction method can significantly enhance the overall process of shape analysis of the AIDS virus spike imaged through the electron microscope. Accurate models of the virus spike will help in the development of better drug design strategies. Secondly, a tomographic reconstruction method implemented using a shape based regularization technique is introduced. Spatial models of known features in the structure being reconstructed are integrated into the reconstruction process as regularizers. This regularization scheme is driven locally through shape information obtained from segmentation and compared with a known spatial model. This method shows reduced blurring, and an improvement in the resolution of the reconstructed volume was also measured. It performs better than popular current techniques and can be extended to other tomographic modalities. Improved Electron Tomography reconstructions will provide better structure elucidation and improved feature visualization, which can aid in solving key biological issues. / text

Medical imaging has known great advances over the past decades to become a powerful tool for the clinical practice. It has led to the tremendous growth of interventional radiology, in which medical devices are inserted and manipulated under image guidance through the vascular system to the pathology location and then used to deliver the therapy. In these minimally-invasive procedures, X-ray guidance is carried out with C-arm systems through two-dimensional real-time projective low-dose images. More recently, three-dimensional visualization via tomographic acquisition has also become available. This work tackles tomographic reconstruction in the aforementioned context. More specifically, it deals with the correction of motion artifacts that originate from the temporal variations of the contrast-enhanced vessels and thus tackles a central aspect of tomography: data (angular) sampling. The compressed sensing theory identifies conditions under which subsampled data can be recovered through the minimization of a least-square data fidelity term combined with sparse constraints. Relying on this theory, an original reconstruction framework is proposed based on iterative filtered backprojection, proximal splitting, '1-minimization and homotopy. This framework is derived for integrating several spatial and temporal penalties. Such a strategy is shown to outperform the analytical filtered backprojection algorithm that is used in the current clinical practice by reducing motion and sampling artifacts in well-identified clinical cases, with focus on cerebral and abdominal imaging. The obtained results emphasize one of the key contributions of this work that is the importance of homotopy in addition to regularization, to provide much needed image quality improvement in the suggested domain of applicability.

Over the last ten years, terahertz (THz or T-ray) biomedical imaging has become a modality of interest due to its ability to simultaneously acquire both image and spectral information. Terahertz imaging systems are being commercialized, with increasing trials performed in a biomedical setting. Advanced digital image processing algorithms are greatly need to assist screening, diagnosis, and treatment. Pattern recognition algorithms play a critical role in the accurate and automatic process of detecting abnormalities when applied to biomedical imaging. This goal requires classification of meaningful physical contrast and identification of information in images, for example, distinguishing between different biological tissues or materials. T-ray tomographic imaging and detection technology contributes especially to our ability to discriminate opaque objects with clear boundaries and makes possible significant potential applications in both in vivo and ex vivo environments. The Thesis consists of a number of Chapters, which can be grouped in to three parts. The first part provides a review of the state-of-the-art regarding THz sources and detectors, THz imaging modes, and THz imaging analysis. Pattern recognition forms the second part of this Thesis, which is represented via combining several basic operations: wavelet transforms and wavelet based signal filtering, feature extraction and selection, along with classification schemes for THz applications. Signal filtering in this Thesis is achieved via wavelet based de-noising. The ultrafast pulses generated terahertz time-domain spectroscopy (THz-TDS), which is demonstrated to justify their decomposition in the wavelet domain as it can provide better de-noising performance. Feature extraction and selection of the terahertz measurements rely on observed changes in pulse amplitude and phase, as well as scattering characteristics of several different types of powder samples under study. Additionally, three signal processing algorithms are adopted for the evaluation of the complex insertion loss function of such samples as lactose, mandelic acid, and dl-mandelic acid: (i) standard evaluation by ratioing the sample with the background spectra, (ii) a subspace identification algorithm, and (iii) a novel wavelet packet identification procedure. These system identification algorithms enable THz measurements to be transformed to features for THz pattern recognition. Meanwhile, a novel feature extraction method involving the use of Auto Regressive (AR) and Auto Regressive Moving Average (ARMA)models on the wavelet transforms of measured T-ray pulse responses of ex vivo osteosarcoma cells as well as other biomedical materials is presented. Classification schemes are carried out via simple and robust schemes, such as the linear Mahalanobis distance classifier, and the non-linear Support Vector Machine (SVM) classifier. In particular, SVMs are used as a learning scheme to achieve the identification of two classes of RNA samples and multiple classes of powered materials. Coherent terahertz detection hardware—THz time-domain spectroscopy (THz-TDS)—is used to obtain all the data for validation of these classification schemes. The past decade has witnessed the tremendous development of terahertz instruments for detecting, storing, analysing, and displaying images. Terahertz time-domain spectroscopy (THz-TDS) is a broadband technique that generates and detects THz radiation in a synchronous and coherent manner. By contrast, the newly developed THz quantum cascade laser is a narrow-band radiation source that provides potential for realising compact systems; they produce image data with higher average power levels. The third part of this Thesis discusses methods to improve the capability of both broad and narrow-band terahertz imaging, driven by computer-aided analytical techniques. A wavelet based reconstruction algorithm for terahertz computed tomography is represented to show how this algorithm can be used to rapidly reconstruct the region of interest (ROI) with a reduction in the measurements of terahertz responses, compared with a standard filtered back-projection technique. These reconstruction algorithms are applied to the analysis of acquired experimental data and to locally recover the two dimensional (2D) and three-dimensional (3D) structures of several optically opaque objects. Moreover, a segmentation technique based on two dimensional wavelet transforms is investigated for the identification of different materials from the reconstructed CT image. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1352839 / Thesis (Ph.D.) - University of Adelaide, School of Electrical and Electronic Engineering, 2009

Nesta tese é apresentado um novo método de reconstrução de imagens, por tomografia de transmissão, de projeções sujeitas a ruído na contagem de fótons. O método de reconstrução selecionado utiliza a técnica POCS (Projections Onto Convex Sets). A estimação das projeções originais a partir das observações ruidosas se dá por meio de quatro métodos: a) estimação utilizando o critério MAP (Maximum a Posteriori); b) filtragem nos coeficientes wavelets das projeções ruidosas; c) aplicação do filtro de Wiener pontual e d) aplicação do filtro de Goodman-Belsher. É apresentado o resultado da reconstrução após a estimação das projeções, mostrando o ISNR (Improvement Signal-to-Noise Ratio) entre as imagens reconstruídas, a partir das projeções ruidosas, com a técnica POCS, estimando as projeções e sem a realização da estimação. Foram utilizados, para reconstrução tomográfica, projeções de corpos de prova obtidos por meio de simulação e também projeções obtidas experimentalmente no minitomógrafo do CNPDIA - EMBRAPA. O uso de estimação sobre as projeções ruidosas mostrou-se eficaz para melhorar a relação sinal-ruído na imagem final, pois esse pré-processamento faz com que os conjuntos impostos pelas projeções sejam mais restritivos. Deve-se observar que a melhoria das imagens obtidas com o uso de filtragem das projeções é obtida com uma relação custo-benefício bastante baixa, pois a maior parte do custo computacional está na fase de reconstrução das imagens. / In this thesis is pesented a new method for image reconstruction, by transmission tomography, for projections under noise in the counting of photons. The selected method of reconstruction uses the POCS (Projections Onto Convex Sets) technique. The estimation of the original projections from the noisy projections observed is performed through four methods: a) estimation using the MAP (Maximum a Posteriori) criteria; b) through of filtering of the wavelets coefficients of the noisy projections; c) using the pointwise Wiener filter and d) using the Goodman-Belsher filter. We present the result of reconstruction after projection estimation, showing the ISNR (Improvement Signal-to-Noise Ratio) between the reconstructed images on noisy projections, using POCS technique after the estimated projections and without this estimation. We use, for tomographic reconstruction, test body projections obtained through simulation and also projections obtained experimentally in the minitomograph scanner of CNPDIA-EMBRAPA. The use of estimation on noisy projections demonstrated to be efficient in improving the signalnoise ratio in the final image, since this pre-processing makes the sets that projections more restrictive. We should observe that the use of projection filtering is obtained with a cost-benefit ratio rather low, since the largest part of the computational effort is in the image reconstruction phase.

O objetivo deste trabalho foi avaliar a influência das correções de atenuação e espalhamento na reconstrução quantitativa em SPECT. O estudo foi baseado em diversas simulações de Monte Carlo, com ênfase especial no modelo torso-cardíaco matemático (MCAT). Para a reconstrução, foi utilizado o algoritmo iterativo ML-EM com projetor-retroprojetor modificado pelo mapa de atenuação. Para avaliar a correção de espalhamento, foram simulados os espectros energéticos, com múltiplas ordens de espalhamento Compton. O método da dupla janela de energia (Jaszczak) foi aplicado, devido a sua simplicidade, e as imagens corrigidas foram comparadas com as de fótons primários. Foram analisadas as escolhas das janelas do fotopico e espalhamento, além da dependência do fator de espalhamento k com a distribuição de atividades do objeto. Duas abordagens foram adotadas para a obtenção dos mapas de atenuação: a estimativa do mapa uniforme diretamente dos dados de emissão, sem o uso de imagens de transmissão; e o borramento de mapas não-uniformes, reconstruídos a partir das projeções por transmissão. A estimativa do mapa de atenuação diretamente dos sinogramas de emissão baseou-se nas condições de consistência da transformada de Radon atenuada. Neste caso, foram estudados os efeitos de diferentes contagens e vários coeficientes de atenuação iniciais sobre as imagens corrigidas. Os mapas de atenuação não-uniformes foram borrados com um \"kernei\" gaussiano, aplicados nas correções e os efeitos na quantificação foram analisados. Os espectros energéticos emitidos pelo modelo MCAT mostraram que os fótons espalhados não poderiam ser excluídos a contento, mesmo que fossem utilizadas janelas de aquisição estreitas sobre o fotopico. Em relação a correção de Jaszczak, verificou-se que a escolha das janelas de fotopico e espalhamento é crucial e confirmou-se que o valor de k é altamente dependente do objeto examinado. Dada uma estimativa inicial do mapa de atenuação, o uso das condições de consistência para estimar o mapa de atenuação uniforme, consistente com os dados de emissão do modelo MCAT simulado, resultou sempre em uma mesma forma, para quaisquer valores iniciais do conjunto de parâmetros. Apesar do erro diminuir com o aumento da contagem, o melhor coeficiente de atenuação não pôde ser obtido, mesmo em altas contagens. Isto se deve a presença dos fótons espalhados, que alteraram a solução das condições de consistência, reduzindo as dimensões do mapa. Os resultados indicaram que a correção de espalhamento é o fator mais importante na reconstrução quantitativa em SPECT. Com referência aos efeitos quantitativos da correção de atenuação, não foram observadas diferenças significativas com a utilização dos mapas borrados, enquanto que a correção com mapas uniformes mostrou-se menos eficaz. / The goal of this work is to evaluate the influence of scatter and attenuation correction methods in quantitative SPECT reconstruction. The study was based on several Monte Carlo simulations, with special emphasis on the mathematical cardiac-torso phantom (MCAT). Iterative ML-EM reconstruction with modified projector-backprojector was used. To evaluate the scatter correction, energy spectra were simulated for SPECT imaging including multiple order Compton scattered photons. The dual energy window method proposed by Jaszczak was applied and scatter corrected images were compared with primary photons images. The choice of the scattering and photopeak windows and the dependence of the scatter factor k with the activity distribution were also analysed. Two approaches were adopted for obtaining the maps for attenuation correction: the estimation of the attenuation maps directly from the emission data, without transmission imaging, and the blurring of non-uniform attenuation maps, reconstructed from transmission data. The estimation of attenuation maps directly from the emission sinograms was based on the consistency conditions of attenuated Radon transform. In this case, the effects of different counting rates and various initial attenuation coefficients on the corrected images were studied. The non-uniform attenuation maps were blurred with a gaussian kernel with different variances, applied in further corrections and their effects on quantitation were examined. Analysis of energy spectra emitted from the MCAT phantom showed that scattered photons cannot be totally excluded, even when narrow acquisition windows were used. As far as the Jaszczak correction is concerned, results showed that the choice of photopeak and secondary windows is crucial and that the value of k is highly dependent on the imaged object. Given an initial estimation of the attenuation map with a constant coefficient, the use of consistency conditions to estimate the uniform map, consistent with the emission data of simulated MCAT phantom, resulted in the same shape for any set of initial parameters. In spite of the fact that the error falls with increasing counting rate, higher counts are not able to determine the best attenuation coefficient. This is due to scattered photons, which alter the solution of consistency conditions, reducing the size of estimated maps. Results indicated that the scatter correction is the most important factor inquantitative SPECT reconstruction. Furthermore, no significant differences were observed in the quantitation, when using the blurred non- uniform attenuation maps in attenuation correction, while corrections with uniform maps proved to be less efficient

<p>Multiphase fields find wide applications in the fields of combustion, sprays, turbomachinery, heating and cooling systems, blasts, energetic materials, and several more areas of engineering interest. As the efficiency and performance of these systems depend heavily on the underlying multiphase field, studying their intricate structural features becomes important. The current study follows the development of a three-dimensional Wide-Angle Relay Plenoptic (WARP) imaging system with two image quadruplers for the tomographic imaging of multiphase fields. 3D printed targets were used to simulate both semi-transparent as well as opaque particle fields to emulate multiphase systems. Tomographic reconstruction of the targets was performed using the iterative MART reconstruction algorithm in a commercial image processing software. Reconstructions were performed at different angular separations between the cameras as well as for varied separation distance between the object and the imaging system. Quantitative analysis of the reconstruction quality of the developed system was performed to study the effectiveness and accuracy of this system in imaging multiphase fields. The effect of varying different system parameters on reconstruction quality has been studied to evaluate the best system configuration for imaging multiphase fields.</p>