京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第18416号 / 情博第531号 / 新制||情||94(附属図書館) / 31274 / 京都大学大学院情報学研究科複雑系科学専攻 / (主査)教授 磯 祐介, 教授 西村 直志, 教授 木上 淳, 講師 吉川 仁 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

According to Bröstcancer förbundet, mammography is not efficient at detecting tumors in dense breast tissue or diagnosing breast cancer at its early stages. Ultrasound-optical tomography (UOT) is an imaging technique in development and has the potential for deep-tissue imaging. If ultrasound-optical tomography were implemented, it would be easier to differentiate between malignant, benign, and healthy tissue from any type of breast tissue. UOT is an imaging technique that takes advantage of high penetration depth and high spatial resolution of ultrasound imaging and optical imaging. In UOT, a laser light and an ultrasound pulse propagate through the tissue simultaneously at a frequency f$_L$ and f$_{US}$, respectively. The light will scatter while it propagates through the tissue and some of this scattered light will become frequency shifted by ultrasound pulse due to the acousto-optic effect. The tagged light will have the frequency $f_T = f_L + f_{US}$. The tagged (frequency shifted) light can be separated from the untagged light (unshifted light) using a thulium-doped lithium niobate, Tm$^{3+}$:$~$LiNbO$_3$, crystal as a filter. The crystal is kept at a temperature close to zero kelvin because then it exhibits unique characteristics, e.g. it has a narrow linewidth and long-lived hyperfine levels at this temperature. The filter is created by a method known as spectral hole burning (SHB). A laser beam is used to transfer electrons from the ground state to the excited state to create a hole at a specific wavelength. The spectral hole is created at the frequency of the tagged light and hence a narrow bandpass filter is constructed inside the crystal. The tagged light is fully transmitted through the filter while it highly attenuates untagged light. The tagged light is detected with a photodiode and processed in MATLAB after it has been transferred to an oscilloscope. This thesis aims to model and design a phantom probe that minimizes vibration and other unwanted movements or disturbances during measurements. The automated phantom holder will be used for the recording of 3D images. Another task of the thesis was to obtain the absorption spectrum of a 0.005$\%$ Tm$^{3+}$:$~$LiNbO$_3$ crystal when it is cooled down to 3$~$K to ensure that the crystal has the same absorption characteristics as predicted in literature. The absorption line at $\sim$ 800$~$nm is of interest since oxyhemoglobin and deoxyhemoglobin have similar absorption coefficients at $\sim$ 800$~$nm. Optical absorption and scattering information will help determine if the sample contains a cancerous region. The phantom probe was modeled in Solid Works and manufactured through 3D printing. In this setup, the sample holder was chosen to be translated while the ultrasound transducer was stationary to generate less blurry images. The design of the probe has to accommodate two detection schemes, reflection and transmission mode. The phantom probe was automated using a linear servo actuator since it was controlled with pulse-width modulation (PWM). It used a square signal as an input that could be generated with an Arbitrary signal generator (AWG). Using a device that operates with a signal was important because it would make it easier to integrate it into the experimental setup. The whole phantom probe was constructed in a cost-efficient way and in a way that it could be easily incorporated into the experimental setup. The absorption spectrum showed that the crystal has an absorption line at $\sim$ 794.3$~$nm. This absorption spectrum was compared to an absorption spectrum taken at 8$~$K on the same crystal and captured with a different method. Both absorption spectra had the same absorption peaks at almost the same wavelengths but they also showed few discrepancies that may depend on the temperature difference and the recording method. In this thesis, the absorption spectrum data taken was captured by sweeping the wavelength. The signal was captured with a photodiode, transferred to an oscilloscope, and then processed in MATLAB. The absorption spectrum data at 8$~$K was obtained using a Fourier transform spectrometer, resulting in data with little noise and well resolved peaks. To conclude, a functional and robust phantom probe was designed and manufactured that could withstand vibration and other undesired movements. An absorption spectrum of Tm$^{3+}$:$~$LiNbO$_3$ crystal was obtained at 3$~$K and compared to absorption taken at 8$~$K and compared to literature and previous measurements under similar conditions.

Optical Tomography is an imaging modality that enhances early diagnosis of disease through use of harmless Near-Infrared rays instead of conventional x-rays. The subsequent images are used to reconstruct the object. However Optical Tomography has not been effectively utilized due to the complicated photon scattering phenomenon and ill-posed nature of the corresponding image reconstruction scheme. The major method for reconstruction of the object is based on an iterative loop that constantly minimizes the difference between the predicted model of photon scattering with acquired images. Currently the most effective method of predicting the photon scattering pattern is the solution of the Radiative Transfer Equation (RTE) using the Finite Elements Method (FEM). However, the conventional FEM uses classical C0 interpolation functions, which have shortcomings in terms of continuity of the solution over the domain as well as proper representation of geometry. Hence higher discretization is necessary to maintain accuracy of gradient-based results which may significantly increase the computational cost in each iteration. This research implements the recently developed Isogeometric Approach (IGA) and particularly IGA-based FEM to address the aforementioned issues. The IGA-based FEM has the potential to enhance adaptivity and reduce the computational cost of discretization schemes. The research in this study applies the IGA method to solve the RTE with the diffusion approximation and studies its behavior in comparison to conventional FEM. The results show comparison of the IGA-based solution with analytical and conventional FEM solutions in terms of accuracy and efficiency. While both methods show high levels of accuracy in reference to the analytical solution, the IGA results clearly excel in accuracy. Furthermore, FE solutions tend to have shorter runtimes in low accuracy results. However, in higher accuracy solutions, where it matters the most, the IGA proves to be considerably faster. / Doctor of Philosophy / CT scans can save lives by allowing medical practitioners observe inside the patient's body without use of invasive surgery. However, they use high energy, potentially harmful x-rays to penetrate the organs. Due to limits of the mathematical algorithm used to reconstruct the 3D figure of the organs from the 2D x-ray images, many such images are required. Thus, a high level of x-ray exposure is necessary, which in periodic use can be harmful. Optical Tomography is a promising alternative which replaces x-rays with harmless Near-infrared (NIR) visible light. However, NIR photons have lower energy and tend to scatter before leaving the organs. Therefore, an additional algorithm is required to predict the distribution of light photons inside the body and their resulting 2D images. This is called the forward problem of Optical Tomography. Only then, like conventional CT scans, can another algorithm, called the inverse solution, reconstruct the 3D image by diminishing the difference between the predicted and registered images. Currently Optical Tomography cannot replace x-ray CT scans for most cases, due to shortcomings in the forward and inverse algorithms to handle real life usages. One obstacle stems from the fact that the forward problem must be solved numerous times for the inverse solution to reach the correct visualization. However, the current numerical method, Finite Element Method (FEM), has limitations in generating accurate solutions fast enough using economically viable computers. This limitation is mostly caused by the FEM's use of a simpler mathematical construct that requires more computations and is limited in accurately modelling the geometry and shape. This research implements the recently developed Isogeometric Analysis (IGA) and particularly IGA-based FEM to address this issue. The IGA-based FEM uses the same mathematical construct that is used to visualize the geometry for complicated applications such as some animations and computer games. They are also less complicated to apply due to much lower need for partitioning the domain. This study applies the IGA method to solve the forward problem of diffuse Optical Tomography and compare the accuracy and speed of IGA solution to the conventional FEM solution. The comparison reveals that while both methods can reach high accuracy, the IGA solutions are relatively more accurate. Also, while low accuracy FEM solutions have shorter runtimes, in solutions with required higher accuracy levels, the IGA proves to be considerably faster.

Diffuse optical tomography (DOT) is an imaging technique that utilizes near-infrared (NIR) light to probe biological tissue and ultimately recover the optical parameters of the tissue. Broadly, the process for image reconstruction in DOT involves three parts: (1) the detected measurements, (2) the modeling of the medium being imaged, and (3) the algorithm that incorporates (1) and (2) to finally estimate the optical properties of the medium. These processes have long been established in the DOT field but are also known to suffer drawbacks. The measurements themselves tend to be susceptible to experimental noise that could degrade reconstructed image quality. Furthermore, depending on the DOT configuration being utilized, the total number of measurements per capture can get very large and add additional computational burden to the reconstruction algorithms. DOT algorithms are reliant on accurate modeling of the medium, which includes solving a light propagation model and/or generating a so-called sensitivity matrix. This process tends to be complex and computationally intensive and, furthermore, does not take into account real system characteristics and fluctuations. Similarly, the inverse algorithms typically utilized in DOT also often take on a high computational volume and complexity, leading to long reconstruction times, and have limited accuracy depending on the measurements, forward model, and experimental system. The purpose of this dissertation is to address and develop computational methods, especially incorporating deep learning, to improve each of these components. First, I evaluated several time-domain data features involving the Mellin and Laplace transforms to incorporate measurements that were robust to noise and sensitive at depth for reconstruction. Furthermore, I developed a method to find the optimal values to use for different imaging depths and scenarios. Second, I developed a neural network that can directly learn the forward problem and sensitivity matrix for simulated and experimental measurements, which allows the computational forward model to adapt to the system's characteristics. Finally, I employed learning-based approaches based on the previous results to solve the inverse problem to recover the optical parameters in a high-speed manner. Each of these components were validated and tested with numerical simulations, phantom experiments, and a variety of in vivo data. Altogether, the results presented in this dissertation depict how these computational approaches lead to an improvement in DOT reconstruction quality, speed, and versatility. It is the ultimate hope that these methods, algorithms, and frameworks developed as a part of this dissertation can be directly used on future data to further validate the research presented here and to further validate DOT as a valuable imaging tool across many applications.

La tomographie optique diffuse (TOD) est une modalité d’imagerie biomédicale 3D peu dispendieuse et non-invasive qui permet de reconstruire les propriétés optiques d’un tissu biologique. Le processus de reconstruction d’images en TOD est difficile à réaliser puisqu’il nécessite de résoudre un problème non-linéaire et mal posé. Les propriétés optiques sont calculées à partir des mesures de surface du milieu à l’étude. Dans ce projet, deux méthodes de reconstruction non-linéaire pour la TOD ont été développées. La première méthode utilise un modèle itératif, une approche encore en développement qu’on retrouve dans la littérature. L’approximation de la diffusion est le modèle utilisé pour résoudre le problème direct. Par ailleurs, la reconstruction d’image à été réalisée dans différents régimes, continu et temporel, avec des mesures intrinsèques et de fluorescence. Dans un premier temps, un algorithme de reconstruction en régime continu et utilisant des mesures multispectrales est développé pour reconstruire la concentration des chromophores qui se trouve dans différents types de tissus. Dans un second temps, un algorithme de reconstruction est développé pour calculer le temps de vie de différents marqueurs fluorescents à partir de mesures optiques dans le domaine temporel. Une approche innovatrice a été d’utiliser la totalité de l’information du signal temporel dans le but d’améliorer la reconstruction d’image. Par ailleurs, cet algorithme permettrait de distinguer plus de trois temps de vie, ce qui n’a pas encore été démontré en imagerie de fluorescence. La deuxième méthode qui a été développée utilise l’apprentissage machine et plus spécifiquement l’apprentissage profond. Un modèle d’apprentissage profond génératif est mis en place pour reconstruire la distribution de sources d’émissions de fluorescence à partir de mesures en régime continu. Il s’agit de la première utilisation d’un algorithme d’apprentissage profond appliqué à la reconstruction d’images en TOD de fluorescence. La validation de la méthode est réalisée avec une mire aux propriétés optiques connues dans laquelle sont inséres des marqueurs fluorescents. La robustesse de cette méthode est démontrée même dans les situations où le nombre de mesures est limité et en présence de bruit. / Abstract : Diffuse optical tomography (DOT) is a low cost and noninvasive 3D biomedical imaging technique to reconstruct the optical properties of biological tissues. Image reconstruction in DOT is inherently a difficult problem, because the inversion process is nonlinear and ill-posed. During DOT image reconstruction, the optical properties of the medium are recovered from the boundary measurements at the surface of the medium. In this work, two approaches are proposed for non-linear DOT image reconstruction. The first approach relies on the use of iterative model-based image reconstruction, which is still under development for DOT and that can be found in the literature. A 3D forward model is developed based on the diffusion equation, which is an approximation of the radiative transfer equation. The forward model developed can simulate light propagation in complex geometries. Additionally, the forward model is developed to deal with different types of optical data such as continuous-wave (CW) and time-domain (TD) data for both intrinsic and fluorescence signals. First, a multispectral image reconstruction algorithm is developed to reconstruct the concentration of different tissue chromophores simultaneously from a set of CW measurements at different wavelengths. A second image reconstruction algorithm is developed to reconstruct the fluorescence lifetime (FLT) of different fluorescent markers from time-domain fluorescence measurements. In this algorithm, all the information contained in full temporal curves is used along with an acceleration technique to render the algorithm of practical use. Moreover, the proposed algorithm has the potential of being able to distinguish more than 3 FLTs, which is a first in fluorescence imaging. The second approach is based on machine learning techniques, in particular deep learning models. A deep generative model is proposed to reconstruct the fluorescence distribution map from CW fluorescence measurements. It is the first time that such a model is applied for fluorescence DOT image reconstruction. The performance of the proposed algorithm is validated with an optical phantom and a fluorescent marker. The proposed algorithm recovers the fluorescence distribution even from very noisy and sparse measurements, which is a big limitation in fluorescence DOT imaging.

Biomedical optical imaging is capable of providing functional information of the soft bi-ological tissues, whose applications include imaging large tissues, such breastand brain in-vivo. Biomedical optical imaging uses near infrared light (600nm-900nm) as the probing media, givin ganaddedadvantageofbeingnon-ionizingimagingmodality. The tomographic technologies for imaging large tissues encompasses diffuse optical tomogra-phyandphotoacoustictomography. Traditional image reconstruction methods indiffuse optical tomographyemploysa �2-norm based regularization, which is known to remove high frequency no is either econstructed images and make the mappearsmooth. Hence as parsity based image reconstruction has been deployed for diffuse optical tomography, these sparserecov-ery methods utilize the �p-norm based regularization in the estimation problem with 0≤ p<1. These sparse recovery methods, along with an approximation to utilizethe �0-norm, have been used forther econstruction of diffus eopticaltomographic images.The comparison of these methods was performed by increasing the sparsityinthesolu-tion. Further a model resolution matrix based framework was proposed and shown to in-duceblurinthe�2-norm based regularization framework for diffuse optical tomography. This model-resolution matrix framework was utilized in the optical imaged econvolution framework. A basis pursuitdeconvolution based on Split AugmentedLagrangianShrink-ageAlgorithm(SALSA)algorithm was used along with the Tikhonovregularization step making the image reconstruction into a two-step procedure. This new two-step approach was found to be robust with no iseandwasabletobetterdelineatethestructureswhichwasevaluatedusingnumericalandgelatinphantom experiments. Modern diffuse optical imaging systems are multi-modalin nature, where diffuse optical imaging is combined with traditional imaging modalitiessuc has Magnetic Res-onanceImaging(MRI),or Computed Tomography(CT). Image-guided diffuse optical tomography has the advantage of reducingthetota lnumber of optical parameters beingreconstructedtothenumber of distinct tissue types identified by the traditional imaging modality, converting the optical image-reconstruction problem fromunder-determined innaturetoover-determined. In such cases, the minimum required measurements might be farless compared to those of the traditional diffuse optical imaging. An approach to choose these measurements optimally based on a data-resolution matrix is proposed, and it is shown that it drastically reduces the minimum required measurements (typicalcaseof240to6) without compromising the image reconstruction performance. In the last part of the work , a model-based image reconstruction approaches in pho-toacoustic tomography (which combines light and ultra sound) arestudied as it is know that these methods have a distinct advantage compared to traditionalanalytical methods in limited datacase. These model-based methods deployTikhonovbasedregularizationschemetoreconstruct the initial pressure from the boundary acoustic data. Again a model-resolution for these cases tend to represent the blurinduced by the regularization scheme. A method that utilizes this blurringmodelandper forms the basis pursuit econ-volution to improve the quantitative accuracy of the reconstructed photoacoustic image is proposed and shown to be superior compared to other traditional methods. Moreover, this deconvolution including the building of model-resolution matrixis achievedvia the Lanczosbidiagonalization (least-squares QR) making this approach computationally ef-ficient and deployable inreal-time. Keywords Medical imaging, biomedical optical imaging, diffuse optical tomography, photoacous-tictomography, multi-modalimaging, inverse problems,sparse recovery,computational methods inbiomedical optical imaging.

Diffuse optical tomography uses near infrared (NIR) light as the probing media to recover the distributions of tissue optical properties. It has a potential to become an adjunct imaging modality for breast and brain imaging, that is capable of providing functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) tends to be non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Traditional image reconstruction methods in diffuse optical tomography employ l2 –norm based regularization, which is known to remove high frequency noises in the re-constructed images and make them appear smooth. The recovered contrast in the reconstructed image in these type of methods are typically dependent on the iterative nature of the method employed, in which the non-linear iterative technique is known to perform better in comparison to linear techniques. The usage of non-linear iterative techniques in the real-time, especially in dynamical imaging, becomes prohibitive due to the computational complexity associated with them. In the rapid dynamic diffuse optical imaging, assumption of a linear dependency in the solutions between successive frames results in a linear inverse problem. This new frame work along with the l1–norm based regularization can provide better robustness to noise and results in a better contrast recovery compared to conventional l2 –based techniques. Moreover, it is shown that the proposed l1-based technique is computationally efficient compared to its counterpart(l2 –based one). The proposed framework requires a reasonably close estimate of the actual solution for the initial frame and any suboptimal estimate leads to erroneous reconstruction results for the subsequent frames. Modern diffuse optical imaging systems are multi-modal in nature, where diffuse optical imaging is combined with traditional imaging modalities such as MRI, CT, and Ultrasound. A novel approach that can more effectively use the structural information provided by the traditional imaging modalities in these scenarios is introduced, which is based on prior image constrained- l1 minimization scheme. This method has been motivated by the recent progress in the sparse image reconstruction techniques. It is shown that the- l1 based frame work is more effective in terms of localizing the tumor region and recovering the optical property values both in numerical and gelatin phantom cases compared to the traditional methods that use structural information.

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.

Stable and computationally efficient reconstruction methodologies are developed to solve two important medical imaging problems which use near-infrared (NIR) light as the source of interrogation, namely, diffuse optical tomography (DOT) and one of its variations, ultrasound-modulated optical tomography (UMOT). Since in both these imaging modalities the system matrices are ill-conditioned owing to insufficient and noisy data, the emphasis in this work is to develop robust stochastic filtering algorithms which can handle measurement noise and also account for inaccuracies in forward models through an appropriate assignment of a process noise. However, we start with demonstration of speeding of a Gauss-Newton (GN) algorithm for DOT so that a video-rate reconstruction from data recorded on a CCD camera is rendered feasible. Towards this, a computationally efficient linear iterative scheme is proposed to invert the normal equation of a Gauss-Newton scheme in the context of recovery of absorption coefficient distribution from DOT data, which involved the singular value decomposition (SVD) of the Jacobian matrix appearing in the update equation. This has sufficiently speeded up the inversion that a video rate recovery of time evolving absorption coefficient distribution is demonstrated from experimental data. The SVD-based algorithm has made the number of operations in image reconstruction to be rather than. 2()ONN3()ONN The rest of the algorithms are based on different forms of stochastic filtering wherein we arrive at a mean-square estimate of the parameters through computing their joint probability distributions conditioned on the measurement up to the current instant. Under this, the first algorithm developed uses a Bootstrap particle filter which also uses a quasi-Newton direction within. Since keeping track of the Newton direction necessitates repetitive computation of the Jacobian, for all particle locations and for all time steps, to make the recovery computationally feasible, we devised a faster update of the Jacobian. It is demonstrated, through analytical reasoning and numerical simulations, that the proposed scheme, not only accelerates convergence but also yields substantially reduced sample variance in the estimates vis-à-vis the conventional BS filter. Both accelerated convergence and reduced sample variance in the estimates are demonstrated in DOT optical parameter recovery using simulated and experimental data. In the next demonstration a derivative free variant of the pseudo-dynamic ensemble Kalman filter (PD-EnKF) is developed for DOT wherein the size of the unknown parameter is reduced by representing of the inhomogeneities through simple geometrical shapes. Also the optical parameter fields within the inhomogeneities are approximated via an expansion based on the circular harmonics (CH) (Fourier basis functions). The EnKF is then used to recover the coefficients in the expansion with both simulated and experimentally obtained photon fluence data on phantoms with inhomogeneous inclusions. The process and measurement equations in the Pseudo-Dynamic EnKF (PD-EnKF) presently yield a parsimonious representation of the filter variables, which consist of only the Fourier coefficients and the constant scalar parameter value within the inclusion. Using fictitious, low-intensity Wiener noise processes in suitably constructed ‘measurement’ equations, the filter variables are treated as pseudo-stochastic processes so that their recovery within a stochastic filtering framework is made possible. In our numerical simulations we have considered both elliptical inclusions (two inhomogeneities) and those with more complex shapes ( such as an annular ring and a dumbbell) in 2-D objects which are cross-sections of a cylinder with background absorption and (reduced) scattering coefficient chosen as = 0.01 mm-1 and = 1.0 mm-1respectively. We also assume=0.02 mm-1 within the inhomogeneity (for the single inhomogeneity case) and=0.02 and 0.03 mm-1 (for the two inhomogeneities case). The reconstruction results by the PD-EnKF are shown to be consistently superior to those through a deterministic and explicitly regularized Gauss-Newton algorithm. We have also estimated the unknown from experimentally gathered fluence data and verified the reconstruction by matching the experimental data with the computed one. The superiority of a modified version of the PD-EnKF, which uses an ensemble square root filter, is also demonstrated in the context of UMOT by recovering the distribution of mean-squared amplitude of vibration, related to the Young’s modulus, in the ultrasound focal volume. Since the ability of a coherent light probe to pick-up the overall optical path-length change is limited to modulo an optical wavelength, the individual displacements suffered owing to the US forcing should be very small, say within a few angstroms. The sensitivity of modulation depth to changes in these small displacements could be very small, especially when the ROI is far removed from the source and detector. The contrast recovery of the unknown distribution in such cases could be seriously impaired whilst using a quasi-Newton scheme (e.g. the GN scheme) which crucially makes use of the derivative information. The derivative-free gain-based Monte Carlo filter not only remedies this deficiency, but also provides a regularization insensitive and computationally competitive alternative to the GN scheme. The inherent ability of a stochastic filter in accommodating the model error owing to a diffusion approximation of the correlation transport may be cited as an added advantage in the context of the UMOT inverse problem. Finally to speed up forward solve of the partial differential equation (PDE) modeling photon transport in the context of UMOT for which the PDE has time as a parameter, a spectral decomposition of the PDE operator is demonstrated. This allows the computation of the time dependent forward solution in terms of the eigen functions of the PDE operator which has speeded up the forward solution, which in turn has rendered the UMOT parameter recovery computationally efficient.

Respiratory system of the cystic fibrosis patients is affected by the defect in gene coding for protein transporter for chloride ions - CFTR ("Cystic fibrosis transmembrane conductance regulator"). The main complication of this disease is airways chronic inflammation, in particular caused by bacterium Pseudomonas aeruginosa. Due to asialylation of the lung surfaces the bacterial adhesion is facilitated, for example via lectin PAIIL. The ability of the chicken yolk antibodies to protect lung epithelial cells against Pseudomonas aeruginosa adhesion has been already proven. Therefore this thesis has mainly focused on the influence of the yolk antibodies specific against PAIIL on the development of infection in lungs of experimental animals. The objective was the optimization of the experimental model on which it would be possible to observe the infection development caused by luminescent bacteria strain in vivo using the optical tomography. At first the experiments have been performed on Wistar rats. Since the bacteria colonies in the rat lungs were not detectable in vivo on the available equipment, the rat experimental model showed up as not suitable. Further on only the mouse models were used. Experiments for the inhalation of the antibodies and intratracheal instillation of the bacteria suspension...