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Existe na indústria o interesse em utilizar as informações da tomografia computadorizada a fim de conhecer o interior (i) dos objetos industriais fabricados ou (ii) das máquinas e seus meios de produção. Nestes casos, a tomografia tem como finalidade (a) controlar a qualidade do produto final e (b) otimizar a produção, contribuindo na fase piloto dos projetos e na análise da qualidade dos meios sem interromper a produção. O contínuo controle de qualidade dos meios de produção é a chave mestra para garantir a qualidade e a competitividade dos produtos. O Centro de Tecnologia das Radiações (CTR), do Instituto de Pesquisas Energéticas e Nucleares (IPEN/CNEN-SP) vem desenvolvendo esta tecnologia para fins de análises de processos industriais há algum tempo. Atualmente, o laboratório tem desenvolvido três gerações de tomógrafos: (i) primeira geração; (ii) terceira geração; e (iii) tomógrafo Instant Non-Scanning. Os algoritmos de reconstrução de imagens tomográficas tem uma importância relevante para o funcionamento ideal desta tecnologia. Nesta tese, foram desenvolvidos e analisados os algoritmos de reconstrução de imagens tomográficas para serem implementados aos protocolos experimentais dos tomógrafos. Os métodos de reconstrução de imagem analítico e iterativo foram desenvolvidos utilizando o software Matlab® r2013b. Os algoritmos iterativos apresentaram imagens com melhor resolução espacial comparado com as obtidas pelo método analítico. Entretanto as imagens por método analítico apresentaram menos ruídos. O tempo para obtenção de imagem pelo método iterativo é relativamente elevado, e aumenta conforme aumenta a matriz de pixels da imagem. Já o método analítico fornece imagens instantâneas. Para as reconstruções de imagens utilizando o tomógrafo Instant Non-Scanning, as imagens pelo método analítico não apresentaram qualidade de imagem satisfatória comparada aos métodos iterativos. / There is an interest in the industry to use the CT information in order to know the interior (i) of the manufactured industrial objects or (ii) the machines and their means of production. In these cases, the purpose of the tomography systems is to (a) control the quality of the final product and (b) to optimize production, contributing to the pilot phase of the projects and to analyze the quality of the means without interrupting he line production. Continuous quality assurance of the means of production is the key to ensuring product quality and competitiveness. The Radiation Technology Center of the Nuclear and Energy Research Institute (IPEN/CNEN-SP) has been developing this technology for the purpose of industrial analysis. Currently the laboratory has developed three generations of tomography systems: (i) first generation; (ii) third generation; and (iii) Instant Non-Scanning tomography. The algorithms for the reconstruction of tomographic images are of relevant importance for the optimal functioning of this technology. In this PhD thesis, the reconstruction algorithms of tomographic images were developed and analyzed to be implemented to the tomography systems developed. The analytical and iterative image reconstruction methods were developed using the software Matlab® r2013b. The iterative algorithms presented images with better spatial resolution compared to those obtained by the analytical method; however the images of the analytical method presented be less image noisy. The time to obtain the image by the iterative method is high, and increases as the image matrix increases, while the analytical method provides fast images. For images reconstructions using the Instant Non-Scanning tomography system, the images by the analytical method did not present satisfactory image quality compared to the iterative methods.

Existe na indústria o interesse em utilizar as informações da tomografia computadorizada a fim de conhecer o interior (i) dos objetos industriais fabricados ou (ii) das máquinas e seus meios de produção. Nestes casos, a tomografia tem como finalidade (a) controlar a qualidade do produto final e (b) otimizar a produção, contribuindo na fase piloto dos projetos e na análise da qualidade dos meios sem interromper a produção. O contínuo controle de qualidade dos meios de produção é a chave mestra para garantir a qualidade e a competitividade dos produtos. O Centro de Tecnologia das Radiações (CTR), do Instituto de Pesquisas Energéticas e Nucleares (IPEN/CNEN-SP) vem desenvolvendo esta tecnologia para fins de análises de processos industriais há algum tempo. Atualmente, o laboratório tem desenvolvido três gerações de tomógrafos: (i) primeira geração; (ii) terceira geração; e (iii) tomógrafo Instant Non-Scanning. Os algoritmos de reconstrução de imagens tomográficas tem uma importância relevante para o funcionamento ideal desta tecnologia. Nesta tese, foram desenvolvidos e analisados os algoritmos de reconstrução de imagens tomográficas para serem implementados aos protocolos experimentais dos tomógrafos. Os métodos de reconstrução de imagem analítico e iterativo foram desenvolvidos utilizando o software Matlab® r2013b. Os algoritmos iterativos apresentaram imagens com melhor resolução espacial comparado com as obtidas pelo método analítico. Entretanto as imagens por método analítico apresentaram menos ruídos. O tempo para obtenção de imagem pelo método iterativo é relativamente elevado, e aumenta conforme aumenta a matriz de pixels da imagem. Já o método analítico fornece imagens instantâneas. Para as reconstruções de imagens utilizando o tomógrafo Instant Non-Scanning, as imagens pelo método analítico não apresentaram qualidade de imagem satisfatória comparada aos métodos iterativos. / There is an interest in the industry to use the CT information in order to know the interior (i) of the manufactured industrial objects or (ii) the machines and their means of production. In these cases, the purpose of the tomography systems is to (a) control the quality of the final product and (b) to optimize production, contributing to the pilot phase of the projects and to analyze the quality of the means without interrupting he line production. Continuous quality assurance of the means of production is the key to ensuring product quality and competitiveness. The Radiation Technology Center of the Nuclear and Energy Research Institute (IPEN/CNEN-SP) has been developing this technology for the purpose of industrial analysis. Currently the laboratory has developed three generations of tomography systems: (i) first generation; (ii) third generation; and (iii) Instant Non-Scanning tomography. The algorithms for the reconstruction of tomographic images are of relevant importance for the optimal functioning of this technology. In this PhD thesis, the reconstruction algorithms of tomographic images were developed and analyzed to be implemented to the tomography systems developed. The analytical and iterative image reconstruction methods were developed using the software Matlab® r2013b. The iterative algorithms presented images with better spatial resolution compared to those obtained by the analytical method; however the images of the analytical method presented be less image noisy. The time to obtain the image by the iterative method is high, and increases as the image matrix increases, while the analytical method provides fast images. For images reconstructions using the Instant Non-Scanning tomography system, the images by the analytical method did not present satisfactory image quality compared to the iterative methods.

Diffuse optical tomography (DOT) is one of the promising imaging modalities that pro- vides functional information of the soft biological tissues in-vivo, such as breast and brain tissues. The near infrared (NIR) light (600-1000 nm) is the interrogating radiation, which is typically delivered and collected using fiber bundles placed on the boundary of the tissue. The internal optical property distribution is estimated via model-based image reconstruction algorithm using these limited boundary measurements. Image reconstruction problem in DOT is known to be non-linear, ill-posed, and some times under-determined due to the multiple scattering of NIR light in the tissue. Solving this inverse problem requires regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of the regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter. A new approach that can easily incorporate any generic penalty function into the diffuse optical tomographic image reconstruction is introduced to show the utility of non-quadratic penalty functions. The penalty functions that were used include, quadratic (`2), absolute (`1), Cauchy, and Geman-McClure. The regularization parameter in each of these cases were obtained automatically using the generalized cross-validation (GCV) method. The reconstruction results were systematically compared with each other via utilization of quantitative metrics, such as relative error and Pearson correlation. The reconstruction results indicate that while quadratic penalty may be able to provide better separation between two closely spaced targets, it's contrast recovery capability is limited and the sparseness promoting penalties, such as `1, Cauchy, Geman-McClure have better utility in reconstructing high-contrast and complex-shaped targets with Geman-McClure penalty being the most optimal one. Effective usage of image guidance by incorporating the refractive index (RI) variation in computational modeling of light propagation in tissue is investigated to assess its impact on optical-property estimation. With the aid of realistic patient breast three-dimensional models, the variation in RI for different regions of tissue under investigation is shown to influence the estimation of optical properties in image-guided diffuse optical tomography (IG-DOT) using numerical simulations. It is also shown that by assuming identical RI for all regions of tissue would lead to erroneous estimation of optical properties. The a priori knowledge of the RI for the segmented regions of tissue in IG-DOT, which is difficult to obtain for the in vivo cases, leads to more accurate estimates of optical properties. Even inclusion of approximated RI values, obtained from the literature, for the regions of tissue resulted in better estimates of optical properties, with values comparable to that of having the correct knowledge of RI for different regions of tissue. Image reconstruction in IG-DOT procedure involves reduction of the number of optical parameters to be reconstructed equal to the number of distinct regions identified in the structural information provided by the traditional imaging modality. This makes the image reconstruction problem to be well-determined compared to traditional under- determined case. Still, the methods that are deployed in this case are same as the one used for traditional diffuse optical image reconstruction, which involves regularization term as well as computation of the Jacobian. A gradient-free Nelder-Mead simplex method was proposed here to perform the image reconstruction procedure and shown to be providing solutions that are closely matching with ones obtained using established methods. The proposed method also has the distinctive advantage of being more efficient due to being regularization free, involving only repeated forward calculations.

Diffuse optical tomography is a promising imaging modality that provides functional information of the soft biological tissues, with prime imaging applications including breast and brain tissue in-vivo. This modality uses near infrared light( 600nm-900nm) as the probing media, giving an advantage of being non-ionizing imaging modality. The image reconstruction problem in diffuse optical tomography is typically posed as a least-squares problem that minimizes the difference between experimental and modeled data with respect to optical properties. This problem is non-linear and ill-posed, due to multiple scattering of the near infrared light in the biological tissues, leading to infinitely many possible solutions. The traditional methods employ a regularization term to constrain the solution space as well as stabilize the solution, with Tikhonov type regularization being the most popular one. The choice of this regularization parameter, also known as hyper parameter, dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. In this thesis, a simple back projection type image reconstruction algorithm is taken up, as they are known to provide computationally efficient solution compared to regularized solutions. In these algorithms, the hyper parameter becomes equivalent to filter factor and choice of which is typically dependent on the sampling interval used for acquiring data in each projection and the angle of projection. Determining these parameters for diffuse optical tomography is not so straightforward and requires usage of advanced computational models. In this thesis, a computationally efficient simplex Method based optimization scheme for automatically finding this filter factor is proposed and its performances is evaluated through numerical and experimental phantom data. As back projection type algorithms are approximations to traditional methods, the absolute quantitative accuracy of the reconstructed optical properties is poor .In scenarios, like dynamic imaging, where the emphasis is on recovering relative difference in the optical properties, these algorithms are effective in comparison to traditional methods, with an added advantage being highly computationally efficient. In the second part of this thesis, this hyper parameter choice for traditional Tikhonov type regularization is attempted with the help of Least-Squares QR-decompisition (LSQR) method. The established techniques that enable the automated choice of hyper parameters include Generalized Cross-Validation(GCV) and regularized Minimal Residual Method(MRM), where both of them come with higher over head of computation time, making it prohibitive to be used in the real-time. The proposed LSQR algorithm uses bidiagonalization of the system matrix to result in less computational cost. The proposed LSQR-based algorithm for automated choice of hyper parameter is compared with MRM methods and is proven to be computationally optimal technique through numerical and experimental phantom cases.

La caméra Compton est un dispositif permettant d’imager les sources de rayonnement gamma. Ses avantages sont sa sensibilité (absence de collimateur mécanique) et la possibilité de reconstruire des images 3D avec un dispositif immobile. Elle également adaptée pour des sources à large spectre énergétique. Ce dispositif est un candidat prometteur en médecine nucléaire et en hadronthérapie. Ces travaux, financés par le projet européen ENVISION (European NoVel Imaging Systems for ION therapy) Coopération-FP7, portent sur le développement de méthodes de reconstruction d’images pour la caméra Compton pour la surveillance de la thérapie par ions. Celle-ci nécessite idéalement une reconstruction temps réel avec une précision millimétrique, même si le nombre de données acquises est relativement faible. Nous avons développé des méthodes analytiques et itératives. Leurs performances sont analysées dans le contexte d’acquisitions réalistes (géométrie de la caméra, nombre d’événements). Nous avons développé une méthode analytique de rétroprojection filtrée. Cette méthode est rapide mais nécessite beaucoup de données. Nous avons également développé des méthodes itératives utilisant un algorithme de maximisation de la fonction de vraisemblance. Nous avons proposé un modèle probabiliste pour l’estimation des éléments de la matrice système nécessaire à la reconstruction et nous avons développé différentes approches pour le calcul de ses éléments : l’une néglige les incertitudes de mesure sur l’énergie, l’autre les prend en compte en utilisant une distribution gaussienne. Nous avons étudié une méthode simplifiée utilisant notre modèle probabiliste. Plusieurs reconstructions sont menées à partir de données simulées, obtenues avec Geant4, mais provenant aussi de plusieurs prototypes simulés de caméra Compton proposés par l’Institut de Physique Nucléaire de Lyon (IPNL) et par le Centre de recherche de Dresde-Rossendorf en Allemagne. Les résultats sont prometteurs et des études plus poussées, à partir de données encore plus réalistes, viseront à les confirmer. / The Compton camera is a device for imaging gamma radiation sources. The advantages of the system lie in its sensitivity, due to the absence of mechanical collimator, and the possibility of imaging wide energy spectrum sources. These advantages make it a promising candidate for application in hadrontherapy. Funded by the european project ENVISION, FP7-Cooperation Work Program, this work deals with the development of image reconstruction methods for the Compton camera. We developed both analytical and iterative methods in order to reconstruct the source from cone-surface projections. Their performances are analyzed with regards to the context (geometry of the camera, number of events). We developped an analytical method using a Filtered BackProjection (FBP) formulation. This method is fast but really sensitive to the noise. We have also developped iterative methods using a List Mode-Maximum Likelihood Expectation Maximization (LM-MLEM) algorithm. We proposed a new probabilistic model for the computation of the elements of the system matrix and different approaches for the calculation of these elements neglecting or not the measurement uncertainties. We also implemented a simplified method using the probabilistic model we proposed. The novelty of the method also lies on the specific discretization of the cone-surface projections. Several studies are carried out upon the reconstructions of simulated data worked out with Geant4, but also simulated data obtained from several prototypes of Compton cameras under study at the Institut de Physique Nucléaire de Lyon (IPNL) and at the Research Center of Dresden-Rossendorf. Results are promising, and further investigations on more realistic data are to be done.

Electrical Impedance Tomography (EIT) is a non linear inverse problem in which the electrical conductivity or resistivity distribution across a closed domain of interest is reconstructed from the surface potentials measured at the domain boundary by injecting a constant sinusoidal current through an array of surface electrodes. Being a non-invasive, non-radiating, non-ionizing, portable and inexpensive methodology, EIT has been extensively studied in medical diagnosis, biomedical engineering, biotechnology, chemical engineering, industrial and process engineering, civil and material engineering, soil and rock science, electronic industry, defense field, nano-technology and many other fields of applied physics. The reconstructed image quality in EIT depends mainly on the boundary data quality and the performance of the reconstruction algorithm used. The boundary data accuracy depends on the design of the practical phantoms, current injection method and boundary data measurement process and precision. On the other hand, the reconstruction algorithm performance is highly influenced by the mathematical modeling of the system, performance of the forward solver and Jacobian computation, inverse solver and the regularization techniques. Hence, for improving the EIT system performance, it is essential to improve the design of practical phantom, instrumentation and image reconstruction algorithm. As the electrical impedance of biological materials is a function of tissue composition and the frequency of applied ac signal, the better assessment of impedance distribution of biological tissues needs multifrequency EIT imaging. In medical EIT, to obtain a better image quality for a complex organ or a body part, accurate domain modelling with a large 3D finite element mesh is preferred and hence, the computation speed becomes very expensive and time consuming. But, the high speed reconstruction with improved image quality at low cost is always preferred in medical EIT. In this direction, a complete multifrequency multifunction EIT (MfMf-EIT) system is developed and multifrequency impedance reconstruction is studied to improve the bioimpedance imaging. The MfMf-EIT system consists of an MfMf-EIT instrumentation (MfMf-EITI), high speed impedance image reconstruction algorithms (IIRA), a Personal Computer (PC) and a number of practical phantoms with EIT sensors or electrodes. MfMf-EIT system and high speed IIRA are studied tested and evaluated with the practical phantoms and the multifrequency impedance imaging is improved with better image quality as well as fast image reconstruction. The MfMf-EIT system is also applied to the human subjects and the impedance imaging is studied for human body imaging and the system is evaluated. MfMf-EIT instrumentation (MfMf-EITI) consists of a multifrequency multifunction constant current injector (MfMf-CCI), multifrequency multifunction data acquisition system (MfMf DAS), a programmable electrode switching module (P-ESM) and a modified signal conditioner blocks (M-SCB) or data processing unit (DPU). MfMf-CCI, MfMf-DAS, P-ESM and M-SCBs are interfaced with a LabVIEW based data acquisition program (LV-DAP) controlled by a LabVIEW based graphical user interface (LV-GUI). LV-GUI controls the current injection and data acquisition with a user friendly, fast, reliable, efficient measurement process. The data acquisition system performance is improved by the high resolution NIDAQ card providing high precision measurement and high signal to noise ratio (SNR). MfMf-EIT system is developed as a versatile data acquisition system with a lot of flexibilities in EIT parameter selection that allows studying the image reconstruction more effectively. MfMf-EIT instrumentation controls the multifrequency and multifunctioned EIT experimentation with a number of system variables such as signal frequency, current amplitude, current signal wave forms and current injection patterns. It also works with either grounded load CCI or floating load CCI and collects the boundary data either in grounded potential form or differential form. The MfMf-EITI is futher modified to a battery based MfMf-EIT (BbMfMf-EIT) system to obtain a better patient safety and also to improve the SNR of the boundary data. MfMf-EIT system is having a facility of injecting voltage signal to the objects under test for conducting the applied potential tomography (APT). All the electronic circuit blocks in MfMf-EIT instrumentation are tested, evaluated and calibrated. The frequency response, load response, Fast Fourier Transform (FFT) studies and DSO analysis are conducted for studying the electronic performance and the signal quality of all the circuit blocks. They are all evaluated with both the transformer based power supply (TBPS) and battery based power supply (BBPS). MfMf-DAS, P-ESM and LV-DAP are tested and evaluated with digital data testing module (DDTM) and practical phantoms. A MatLAB-based Virtual Phantom for 2D EIT (MatVP2DEIT) is developed to generate accurate 2D boundary data for assessing the 2D EIT inverse solvers and its image reconstruction accuracy. It is a MATLAB-based computer program which defines a phantom domain and its inhomogeneities to generate the boundary potential data by changing its geometric parameters. In MatVP2DEIT, the phantom diameter, domain discretization, inhomogeneity number, inhomogeneity geometry (shape, size and position), electrode geometry, applied current magnitude, current injection pattern, background medium conductivity, inhomogeneity conductivity all are set as the phantom variables and are chosen indipendently for simulating different phantom configurations. A constant current injection is simulated at the phantom boundary with different current injection protocols and boundary potential data are calculated. A number of boundary data sets are generated with different phantom configurations and the resistivity images are reconstructed using EIDORS (Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software). Resistivity images are evaluated with the resistivity parameters and contrast parameters estimated from the elemental resistivity profiles of the reconstructed impedance images. MfMf-EIT system is studied, tested, evaluated with a number of practical phantoms eveloped with non-biological and biological materials and the multifrequency impedance imaging is improved. A number of saline phantoms with single and multiple inhomogeneities are developed and the boundary data profiles are studied and the phantom geometry is modified. NaCl-insulator phantoms and the NaCl-vegetable phantoms with different inhomogeneity configurations are developed and the multifrequency EIT reconstruction is studied with different current patterns, different current amplitudes and different frequencies using EIDORS as well as the developed IIRAs developed in MATLAB to evaluate the phantoms and MfMf-EIT system. Real tissue phantoms are developed with different chicken tissue backgrounds and high resistive inhomogeneities and the resistivity image reconstruction is studied using MfMf-EIT system. Chicken tissue phantoms are developed with chicken muscle tissue (CMTP) paste or chicken tissue blocks (CMTB) as the background mediums and chicken fat tissue, chicken bone, air hole and nylon cylinders are used as the inhomogeneity to obtained different phantom configurations. Resistivity imaging of all the real tissue phantoms is reconstructed in EIDORS and developed IIRAs with different current patterns, different frequencies and the images are evaluated by the image parameters to assess the phantoms as well as the MfMf-EIT system. Gold electrode phantoms are developed with thin film based flexible gold electrode arrays for improved bioimpedance and biomedical imaging. The thin film based gold electrode arrays of high geometric precision are developed on flexible FR4 sheet using electro-deposition process and used as the EIT sensors. The NaCl phantoms and real tissue phantoms are developed with gold electrode arrays and studied with MfMf-EIT system and and the resiulsts are compared with identical stainless steel electrode phantoms. NaCl phantoms are developed with 0.9% NaCl solution with single and multiple insulator or vegetable tissues as inhomogeneity. Gold electrode real tissue phantoms are also developed with chicken muscle tissues and fat tissues or other high resistive objects. The EIT images are reconstructed for the gold electrode NaCl phantoms and the gold electrode real tissue phantoms with different phantom geometries, different inhomogeneity configurations and different current patterns and the results are compared with identical SS electrode phantoms. High speed IIRAs called High Speed Model Based Iterative Image Reconstruction (HSMoBIIR) algorithms are developed in MATLAB for impedance image reconstruction in Electrical Impedance Tomography (EIT) by implementing high speed Jacobian calculation techniques using “Broyden’s Method (BM)” and “Adjoint Broyden’s Method (ABM)”. Gauss Newton method based EIT inverse solvers repeatitively evaluate the Jacobian (J) which consumes a lot of computation time for reconstruction, whereas, the HSMoBIIR with Broyden’s Methods (BM)-based accelerated Jacobian Matrix Calculators (JMCs) provides the high speed schemes for Jacobian (J) computation which is integrated with conjugate gradient scheme (CGS) for fast impedance reconstruction. The Broyden’s method based HSMoBIIR (BM-HSMoBIIR) and Adjoint Broyden’s method based HSMoBIIR (ABM-HSMoBIIR) algorithm are developed for high speed improved impedance imaging using BM based JMC (BM-JMC) and ABM-based JMC (ABM-JMC) respectively. Broyden’s Method based HSMoBIIR algorithms make explicit use of secant and adjoint information that can be obtained from the forward solution of the EIT governing equation and hence both the BM-HSMoBIIR and ABM-HSMoBIIR algorithms reduce the computational time remarkably by approximating the system Jacobian (J) successively through low-rank updates. The impedance image reconstruction is studied with BM-HSMoBIIR and ABM-HSMoBIIR algorithms using the simulated and practical phantom data and results are compared with a Gauss-Newton method based MoBIIR (GNMoBIIR) algorithm. The GNMoBIIR algorithm is developed with a Finite Element Method (FEM) based flexible forward solver (FFS) and Gauss-Newton method based inverse solver (GNIS) working with a modified Newton-Raphson iterative technique (NRIT). FFS solves the forward problem (FP) to obtain the computer estimated boundary potential data (Vc) data and NRIT based GNIS solve the inverse problem (IP) and the conductivity update vector [Δσ] is calculated by conjugate gradient search by comparing Vc measured boundary potential data (Vm) and using the Jacobian (J) matrix computed by the adjoint method. The conductivity reconstruction is studied with GNMoBIIR, BM-HSMoBIIR and ABM-HSMoBIIR algorithms using simulated data a practical phantom data and the results are compared. The reconstruction time, projection error norm (EV) and the solution error norm (Eσ) produced in HSMoBIIR algorithms are calculated and compared with GNMoBIIR algorithm. Results show that both the BM-HSMoBIIR and ABM-HSMoBIIR algorithms successfully reconstructs the conductivity distribution of the domain under test with its proper inhomogeneity and background conductivities for simulation as well as experimental studies. Simulated and practical phantom studies demonstrate that both the BM-HSMoBIIR and ABM-HSMoBIIR algorithms accelerate the impedance reconstruction by more than five times. It is also observed that EV and Eσ are reduced in both the HSMoBIIR algorithms and hence the image quality is improved. Noise analysis and convergence studies show that both the BM-HSMoBIIR and ABM-HSMoBIIR algorithms works faster and better in noisy conditions compared to GNMoBIIR. In low noise conditions, BM-HSMoBIIR is faster than to ABM-HSMoBIIR algorithm. But, in higher noisy environment, the ABM-HSMoBIIR is found faster and better than BM-HSMoBIIR. Two novel regularization methods called Projection Error Propagation-based Regularization (PEPR) and Block Matrix based Multiple Regularization (BMMR) are proposed to improve the image quality in Electrical Impedance Tomography (EIT). PEPR method defines the regularization parameter as a function of the projection error contributed by the mismatch (difference) between the data obtained from the experimental measurements (Vm) and calculated data (Vc). The regularization parameter in the reconstruction algorithm gets modified automatically according to the noise level in measured data and ill-posedness of the Hessian matrix. The L-2 norm of the projection error is calculated using the voltage difference and it is used to find the regularization parameter in each iteration in the reconstruction algorithm. In BMMR method, the response matrix (JTJ) obtained from the Jacobian matrix (J) has been partitioned into several sub-block matrices and the highest eigenvalue of each sub-block matrices has been chosen as regularization parameter for the nodes contained by that sub-block. The BMMR method preserved the local physiological information through the multiple regularization process which is then integrated to the ill-posed inverse problem to make the regularization more effective and optimum for all over the domain. Impedance imaging with simulated data and the practical phantom data is studied with PEPR and BMMR techniques in GNMoBIIR and EIDORS and the reconstructed images are compared with the single step regularization (STR) and Modified Levenberg Regularization (LMR). The projection error and the solution error norms are estimated in the reconstructions processes with PEPR and the BMMR methods and the results are compared with the errors estimated in STR and modified LMR techniques. Reconstructed images obtained with PEPR and BMMR are also studied with image parameters and contrast parameters and the reconstruction performance with PEPR and BMMR are evaluated by comparing the results with STR and modified LMR. PEPR and BMMR techniques are successfully implemented in the GNMoBIIR and EIDORS algorithms to improve the impedance image reconstruction by regularizing the solution domain in EIT reconstruction process. As the multifrequency EIT is always preferred in biological object imaging for better assessments of the frequency dependent bioimpedance response, multifrequency impedance imaging is studied with MfMf-EIT system developed for biomedical applications. MfMf-EIT system is studied, tested and evaluated with practical phantoms suitably developed for multifrequency impedance imaging within a wide range of frequency. Different biological materials are studied with electrical impedance spectroscopy (EIS) and a number of practical biological phantoms suitable for multifrequency EIT imaging are developed. The MfMf-EIT system is studied, tested and evaluated at different frequency levels with different current patterns using a number of NaCl phantoms with single, multiple and hybrid vegetable tissue phantoms as well as with chicken tissue phantoms. BbMfMf-EIT system is also studied and evaluated with the multifrequency EIT imaging using the developed biological phantoms. The developed MfMf-EIT system is applied on human body for impedance imaging of human anatomy. Impedance imaging of human leg and thigh is studied to visualize the muscle and bone tissues using different current patterns and different relative electrode positions. Ag/AgCl electrodes are attached to the leg and thigh using ECG gel and the boundary data are collected with MfMf-EIT EIT system by injecting a 1 mA and 50 kHz sinusoidal constant current with neighbouring and opposite current injection patterns. Impedance images of the femur bone of the human thigh and the tibia and fibula bones of the human leg along with the muscle tissue backgrounds are reconstructed in EIDORS and GNMoBIIR algorithms. Reconstructed resistivity profiles of bone and muscles are compared with the resistivity data profiles reported in the published literature. Impedance imaging of leg and thigh is studied with MfMf-EIT system for different current patterns, relative electrode positions and the images are evaluated to assess the system reliability. Battery based MfMf-EIT system (BbMfMf-EIT) is also studied for human leg and thigh imaging and it is observed that MfMf-EIT system and BbMfMf-EIT system are suitable for impedance imaging of human body imaging though the BbMfMf-EIT system increases the patiet safety. Therefore, the developed MfMf-EIT and BbMfMf-EIT systems are found quite suitable to improve the bio-impedance imaging in medical, biomedical and clinical applications as well as to study the anatomical and physiological status of the human body to diagnose, detect and monitor the tumors, lesions and a number of diseases or anatomical abnormalities in human subjects.