<p>The recognition that supportive mechanical ventilation can also damage the lung, the so called ventilation induced lung injury (VILI), has revived the more than 40 year long debate on the optimal level of PEEP to be used. It is established that the prevention of VILI improves patient outcome and that PEEP exerts protective effects by preventing unstable diseased alveoli from collapsing. Therefore, the term “open lung PEEP” (OL-PEEP) has been introduced as the end-expiratory pressure that keeps the lung open after its collapse has been eliminated by an active lung recruitment manoeuvre. The determination of such an optimal level of PEEP under clinical circumstances is difficult and remains to be investigated.</p><p>The aim of this study was to investigate the usefulness of breath by breath monitoring of dynamic compliance (Cdyn) as a clinical means to identify OL-PEEP at the bedside and to demonstrate the improvement in lung function resulting from its application.</p><p>In a porcine lung lavage model of acute lung injury PEEP at maximum Cdyn during a decremental PEEP trial after full lung recruitment was related to the onset of lung collapse and OL-PEEP could be found 2 cmH₂O above this level Ventilation at OL-PEEP was associated with improved gas exchange, efficiency of ventilation, lung mechanics and less than 5% collapse on CT scans. In addition, dead space, especially its portion related to alveolar gas changed characteristically during recruitment, PEEP titration and collapse thereby helping to identify OL-PEEP.</p><p>The beneficial effects of OL-PEEP on lung function and mechanics was demonstrated in a porcine model of VILI. OL-PEEP improved lung function and mechanics when compared to lower or higher levels prior to or after lung recruitment. By using electrical impedance tomography it could be shown that PEEPs within the range of 14 to 22 cmH₂O resulted in a similar redistribution of both ventilation and perfusion to the dorsal regions of the lung. OL-PEEP resulted in the best regional and global matching of ventilation and perfusion explaining the drastic improvements in gas exchange. Also regional compliance was greatly improved in the lower half of the lung as compared to all other situations.</p><p>In ARDS patients OL-PEEP could be identified applying the same protocol. The physiological changes described could now be reproduced and maintained during a four hours study ventilation period in real patients at four study centres.</p><p>In conclusion, the usefulness of dynamic compliance for identifying open lung PEEP during a decremental PEEP trial was demonstrated under experimental and clinical conditions. This PEEP should then be used as an essential part of any lung protective ventilation strategy. The impact of ventilating ARDS patients according to the principles described in these studies on outcome are currently being evaluated in an international randomized controlled trial.</p>

The recognition that supportive mechanical ventilation can also damage the lung, the so called ventilation induced lung injury (VILI), has revived the more than 40 year long debate on the optimal level of PEEP to be used. It is established that the prevention of VILI improves patient outcome and that PEEP exerts protective effects by preventing unstable diseased alveoli from collapsing. Therefore, the term “open lung PEEP” (OL-PEEP) has been introduced as the end-expiratory pressure that keeps the lung open after its collapse has been eliminated by an active lung recruitment manoeuvre. The determination of such an optimal level of PEEP under clinical circumstances is difficult and remains to be investigated. The aim of this study was to investigate the usefulness of breath by breath monitoring of dynamic compliance (Cdyn) as a clinical means to identify OL-PEEP at the bedside and to demonstrate the improvement in lung function resulting from its application. In a porcine lung lavage model of acute lung injury PEEP at maximum Cdyn during a decremental PEEP trial after full lung recruitment was related to the onset of lung collapse and OL-PEEP could be found 2 cmH2O above this level Ventilation at OL-PEEP was associated with improved gas exchange, efficiency of ventilation, lung mechanics and less than 5% collapse on CT scans. In addition, dead space, especially its portion related to alveolar gas changed characteristically during recruitment, PEEP titration and collapse thereby helping to identify OL-PEEP. The beneficial effects of OL-PEEP on lung function and mechanics was demonstrated in a porcine model of VILI. OL-PEEP improved lung function and mechanics when compared to lower or higher levels prior to or after lung recruitment. By using electrical impedance tomography it could be shown that PEEPs within the range of 14 to 22 cmH2O resulted in a similar redistribution of both ventilation and perfusion to the dorsal regions of the lung. OL-PEEP resulted in the best regional and global matching of ventilation and perfusion explaining the drastic improvements in gas exchange. Also regional compliance was greatly improved in the lower half of the lung as compared to all other situations. In ARDS patients OL-PEEP could be identified applying the same protocol. The physiological changes described could now be reproduced and maintained during a four hours study ventilation period in real patients at four study centres. In conclusion, the usefulness of dynamic compliance for identifying open lung PEEP during a decremental PEEP trial was demonstrated under experimental and clinical conditions. This PEEP should then be used as an essential part of any lung protective ventilation strategy. The impact of ventilating ARDS patients according to the principles described in these studies on outcome are currently being evaluated in an international randomized controlled trial.

Magnetic Resonance Electrical Impedance Tomography (MREIT) reconstructs images of electrical conductivity distribution based on magnetic flux density (B) measurements. Magnetic flux density is generated by an externally applied current on the object and measured by a Magnetic Resonance Imaging (MRI) scanner. With the measured data and peripheral voltage measurements, the conductivity distribution of the object can be reconstructed. There are two types of reconstruction algorithms. First type uses current density distributions to reconstruct conductivity distribution. Object must be rotated in MRI scanner to measure three components of magnetic flux density. These types of algorithms are called J-based reconstruction algorithms. The second type of reconstruction algorithms uses only one component of magnetic flux density which is parallel to the main magnetic field of MRI scanner. This eliminates the need of subject rotation. These types of algorithms are called B-based reconstruction algorithms. In this study four of the B-based reconstruction algorithms, proposed by several research groups, are examined. The algorithms are tested by different computer models for noise-free and noisy data. For noise-free data, the algorithms work successfully. System SNR 30, 20 and 13 are used for noisy data. For noisy data the performance of algorithm is not as satisfactory as noise-free data. Twice differentiation of z component of B (Bz) is used for two of the algorithms. These algorithms are very sensitive to noise. One of the algorithms uses only one differentiation of Bz so it is immune to noise. The other algorithm uses sensitivity matrix to reconstruct conductivity distribution.

Magnetic Resonance Electrical Impedance Tomography (MREIT) reconstructs conductivity distribution with internal current density (MRCDI) and boundary voltage measurements. There are many algorithms proposed for the solution of MREIT inverse problem which can be divided into two groups: Current density (J) and magnetic flux density (B) based reconstruction algorithms. In this thesis, J-based MREIT reconstruction algorithms are implemented and optimized with modifications. These algorithms are simulated with five conductivity models which have different geometries and conductivity values. Results of simulation are discussed and reconstruction algorithms are compared according to their performances. Equipotential-Projection algorithm has lower error percentages than other algorithms for noise-free case whereas Hybrid algorithm has the best performance for noisy cases. Although J-substitution and Hybrid algorithms have relatively long reconstruction times, they produced the best images perceptually. v Integration along Cartesian Grid Lines and Integration along Equipotential Lines algorithms diverge as noise level increases. Equipotential-Projection algorithm has erroneous lines starting from corners of FOV especially for noisy cases whereas Solution as a Linear Equation System has a typical grid artifact. When performance with data of experiment 1 is considered, only Solution as a Linear Equation System algorithm partially reconstructed all elements which show that it is robust to noise. Equipotential-Projection algorithm reconstructed resistive element partially and other algorithms failed in reconstruction of conductivity distribution. Experimental results obtained with a higher conductivity contrast show that Solution as a Linear Equation System, J-Substitution and Hybrid algorithms reconstructed both phantom elements and Hybrid algorithm is superior to other algorithms in percentage error comparison.

Electrical conductivity of biological tissues is a distinctive property which differs among tissues. It also varies according to the physiological and pathological state of tissues. Furthermore, in order to solve the bioelectric field problems accurately, electrical conductivity information is essential. Magnetic Resonance Electrical Impedance Tomography (MREIT) technique is proposed to image this information with high spatial resolution. However, almost all MREIT algorithms proposed to date assumes isotropic conductivity in order to simplify the underlying mathematics. But it is known that most of the tissues in human body have anisotropic conductivity values. The aim of this study is to reconstruct anisotropic conductivity images with MREIT. In the study, five novel anisotropic conductivity reconstruction algorithms are developed and implemented. Proposed algorithms are grouped into two: current density based reconstruction algorithms (Type-I) and magnetic flux density based algorithms (Type-II). Performances of the algorithms are evaluated in several aspects and compared with each other. The technique is experimentally realized using 0.15T METU &ndash / EE MRI System and anisotropic conductivity images of test phantoms are reconstructed using all proposed algorithms.

Breast cancer is the most crucial cancer type among all other cancer types. There are many imaging techniques used to screen breast carcinoma. These are mammography, ultrasound, computed tomography, magnetic resonance imaging, infrared imaging, positron emission tomography and electrical impedance tomography. However, there is no gold standard in breast carcinoma diagnosis. The object of this study is to create a hybrid system that uses thermal and electrical imaging methods together for breast cancer diagnosis. Body tissues have different electrical conductivity values depending on their state of health and types. Consequently, one can get information about the anatomy of the human body and tissue&rsquo / s health by imaging tissue conductivity distribution. Due to metabolic heat generation values and thermal characteristics that differ from tissue to tissue, thermal imaging has started to play an important role in medical diagnosis. To increase the temperature contrast in thermal images, the characteristics of the two imaging modalities can be combined. This is achieved by implementing thermal imaging applying electrical currents from the body surface within safety limits (i.e., thermal imaging in active mode). Electrical conductivity of tissues changes with frequency, so it is possible to obtain more than one thermal image for the same body. Combining these images, more detailed information about the tumor tissue can be acquired. This may increase the accuracy in diagnosis while tumor can be detected at deeper locations. Feasibility of the proposed technique is investigated with analytical and numerical simulations and experimental studies. 2-D and 3-D numerical models of the female breast are developed and feasibility work is implemented in the frequency range of 10 kHz and 800 MHz. Temporal and spatial temperature distributions are obtained at desired depths. Thermal body-phantoms are developed to simulate the healthy breast and tumor tissues in experimental studies. Thermograms of these phantoms are obtained using two different infrared cameras (microbolometer uncooled and cooled Quantum Well Infrared Photodetectors). Single and dual tumor tissues are determined using the ratio of uniform (healthy) and inhomogeneous (tumor) images. Single tumor (1 cm away from boundary) causes 55 &deg / mC temperature increase and dual tumor (2 cm away from boundary) leads to 50 &deg / mC temperature contrast. With multi-frequency current application (in the range of 10 kHz-800 MHz), the temperature contrast generated by 3.4 mm3 tumor at 9 mm depth can be detected with the state-of-the-art thermal imagers.

<p>This thesis concerns the approximation of optimally controlled partial differential equations for applications in optimal design and reconstruction. Such optimal control problems are often ill-posed and need to be regularized to obtain good approximations. We here use the theory of the corresponding Hamilton-Jacobi-Bellman equations to construct regularizations and derive error estimates for optimal design problems. The constructed Pontryagin method is a simple and general method where the first, analytical, step is to regularize the Hamiltonian. Next its stationary Hamiltonian system, a nonlinear partial differential equation, is computed efficiently with the Newton method using a sparse Jacobian. An error estimate for the difference between exact and approximate objective functions is derived, depending only on the difference of the Hamiltonian and its finite dimensional regularization along the solution path and its<em> L</em>² projection, i.e. not on the difference of the exact and approximate solutions to the Hamiltonian systems. In the thesis we present solutions to applications such as optimal design and reconstruction of conducting materials and elastic structures.</p>

This dissertation focuses on the application of tomography in the sugar milling process, specifically within the vacuum pan. The research aims to improve the efficiency and throughput of a sugar mill by producing real-time images of the boiling dynamic in the pan and hence can be used as a diagnostic tool. The real-time tomography system is a combination of ruggedized data collecting hardware, a switching circuit and software algorithms. The system described in this dissertation uses 16 electrodes and estimates images based on the distinct differences in conductivities to be found in the vacuum pan, i.e. a conductive syrup-like fluid (massecuite) and bubbles. There is a direct correlation between the bubbles produced during the boiling process and heat transfer in the pan. From this correlation one can determine how well the pan is operating. The system has been developed in order to monitor specific parts of a pan for optimal boiling. A binary reconstructed image identifies either massecuite or water vapour. Each image is reconstructed using a modified neighbourhood data collection method and a back projection algorithm. The data collection and image reconstruction take place simultaneously, making it possible to generate images in real-time. Each image frame is reconstructed at approximately 1.1 frames per second. Most of the system was developed in LabVIEW, with some added external drive electronics, and functions seamlessly. The tomography system is LAN enabled hence measurements are initiated through a remote PC on the same network and the reconstructed images are streamed to the user. The laboratory results demonstrate that it is possible to generate tomographic images from bubbles vs massecuite, tap water and deionized water in real-time. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2012.

Magnetic resonance imaging (MRI) is a high resolution medical imaging technique based on distinguishing tissues according to their nuclear magnetic properties. Magnetic resonance electrical impedance tomography (MREIT) is a conductivity imaging technique which reconstructs images of electrical properties, based on their effect on induced magnetic flux density due to externally applied current flow. Both of these techniques are of interest for novel research and development. Simulators help researchers observe the accuracy and the results of the study. In this study a user friendly complete MRI/MREIT simulator is designed. This simulator is the combination of improved version of MRI simulator (implemented by V. E. Arpinar, H. Yigitler), a forward solver, to observe the current injection effect, the improved version of user interface that is designed on LabVIEW graphical programming environment (designed by M. Ozsut), and equi-potential projection (EPP) reconstruction algorithm (proposed by M. S. Ozdemir, M. Eyuboglu, O. Ozbek). All of these individual parts are improved and gathered in LabVIEW environment in order to work in synchrony. In addition to that, regional image reconstruction technique (proposed by H. Altunel, M. Eyuboglu) is also included in the simulator. The simulator is run for various inputs and system specifications. It is observed that the simulation results are consistent with the expected results for MRI, MREIT and conventional/regional MREIT reconstruction. Four different models are designed and results are obtained using these models. The accuracy of the results usually differs with the input parameters and model geometry. Validating numerically the accuracy of the forward solution part using Biot-Savart and Ampere&#039 / s laws, the consistency of the forward problem solution part is obtained at a percentage of 95%. In the MREIT part, magnetic flux density distribution taken from forward solver part is added to the main magnetic flux density used in the MRI part. Consistency of the magnetic flux density distribution given to the simulator as input and the output taken from the MREIT part of the simulator is found as 99%. In addition to conventional EPP algorithm, regional MREIT reconstruction algorithm is applied for various noise levels. It is observed that, as the noise level increases, regional MREIT reconstruction algorithm gives relatively much better results compared to conventional MREIT reconstruction algorithm. Errors obtained by applying conventional reconstruction and regional reconstruction are compared for each inhomogeneity individually. Therefore, accuracies of the different current patterns depending on the inhomogeneities are observed as well.

abstract: Among electrical properties of living tissues, the differentiation of tissues or organs provided by electrical conductivity is superior. The pathological condition of living tissues is inferred from the spatial distribution of conductivity. Magnetic Resonance Electrical Impedance Tomography (MREIT) is a relatively new non-invasive conductivity imaging technique. The majority of conductivity reconstruction algorithms are suitable for isotropic conductivity distributions. However, tissues such as cardiac muscle and white matter in the brain are highly anisotropic. Until recently, the conductivity distributions of anisotropic samples were solved using isotropic conductivity reconstruction algorithms. First and second spatial derivatives of conductivity (∇σ and ∇2σ ) are integrated to obtain the conductivity distribution. Existing algorithms estimate a scalar conductivity instead of a tensor in anisotropic samples. Accurate determination of the spatial distribution of a conductivity tensor in an anisotropic sample necessitates the development of anisotropic conductivity tensor image reconstruction techniques. Therefore, experimental studies investigating the effect of ∇2σ on degree of anisotropy is necessary. The purpose of the thesis is to compare the influence of ∇2σ on the degree of anisotropy under two different orthogonal current injection pairs. The anisotropic property of tissues such as white matter is investigated by constructing stable TX-151 gel layer phantoms with varying degrees of anisotropy. MREIT and Diffusion Magnetic Resonance Imaging (DWI) experiments were conducted to probe the conductivity and diffusion properties of phantoms. MREIT involved current injection synchronized to a spin-echo pulse sequence. Similarities and differences in the divergence of the vector field of ∇σ (∇2σ) among anisotropic samples subjected to two different current injection pairs were studied. DWI of anisotropic phantoms involved the application of diffusion-weighted magnetic field gradients with a spin-echo pulse sequence. Eigenvalues and eigenvectors of diffusion tensors were compared to characterize diffusion properties of anisotropic phantoms. The orientation of current injection electrode pair and degree of anisotropy influence the spatial distribution of ∇2σ. Anisotropy in conductivity is preserved in ∇2σ subjected to non-symmetric electric fields. Non-symmetry in electric field is observed in current injections parallel and perpendicular to the orientation of gel layers. The principal eigenvalue and eigenvector in the phantom with maximum anisotropy display diffusion anisotropy. / Dissertation/Thesis / Masters Thesis Bioengineering 2015