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Wearable Flexible Optical Imaging Systems for Diffuse Optical Spectroscopy and Tomography in Medical Diagnosis and Treatment MonitoringKim, Youngwan January 2020 (has links)
The overall goal of this thesis is the development of wearable flexible optical imaging systems (WeFOISs) that can be used for diffuse optical spectroscopy (DOS) and tomography (DOT). The advantages of WeFOISs lie in their lowcost, portability, and simple patientinterface compared to current DOS and DOT systems. A flexible form factor provides conformal attachment even in cases where the targets such as fingers and toes have a strongly curved surface. WeFOIS technology is a scalable and expandable. Each system can be designed with multiple pairs of light sources and photodetectors depending on the needs and size of the target.
The WeFOISs presented in this thesis were developed based on a modular design. The two main modules are a sensing unit and a control unit (a.k.a. sensing band and control band). Two different types of sensing units have been developed. The first type is based on inorganic optoelectronic components such as light emitting diode (LED) and siliconphotodiode (SiPD). The second type is made with organic components such as organic light emitting diodes (OLEDs), quantumdot light emitting diodes (QDLED), and organic photodiodes (OPDs). The flexible control units operate the light sources, read the intensity of the light transmitted through biological tissue, and send data to the computer. Depending on the number of pairs of light sources and photodiodes placed on the flexible sensing bands, the control units have different designs.
Furthermore, a small integrating sphere system (SISS) was developed to measure the optical properties (absorption and scattering coefficients, μa and μs) of biological tissue samples and tissuemimicking phantoms, which were to be used to calibrate the WeFOISs. The accuracy of the SISS as a function of the sample thickness was investigated by comparing a widely used inverseaddingdoubling (IAD) program and inverseMonteCarlo (IMC) simulations.
Finally, the performance of the different WeFOISs were tested in various preclinical and clinical applications, including epilepsy monitoring in rat brains, monitoring of peripheral artery disease (PAD) in human feet, diagnosing systemic lupus erythematosus (SLE) in human fingers, and characterizing tumors in breast cancer patients. The results demonstrated the potentials of the WeFOISs for monitoring of symptoms of various diseases and for applications in point of care.

52 
Stability Analysis of the CIP Scheme and its Applications in Fundamental Study of the Diffused Optical Tomography / CIPスキームの安定性解析とその拡散光トモグラフィへの基礎研究への応用についてTanaka, Daiki 24 March 2014 (has links)
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

53 
Improving the experimental setup for ultrasoundoptical tomography imagingDahir Ahmed, Ibtisam January 2023 (has links)
According to Bröstcancer förbundet, mammography is not efficient at detecting tumors in dense breast tissue or diagnosing breast cancer at its early stages. Ultrasoundoptical tomography (UOT) is an imaging technique in development and has the potential for deeptissue imaging. If ultrasoundoptical 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 acoustooptic 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 thuliumdoped 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 longlived 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 pulsewidth 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 costefficient 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.

54 
Modelbased and machine learning techniques for nonlinear image reconstruction in diffuse optical tomography / Techniques basées sur des modèles et apprentissage machine pour la reconstruction d’image nonlinéaire en tomographie optique diffuseEttehadi, Seyedrohollah January 2017 (has links)
La tomographie optique diffuse (TOD) est une modalité d’imagerie biomédicale 3D peu
dispendieuse et noninvasive 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 nonlinéaire 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 nonlinéaire 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
illposed. 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 nonlinear DOT image reconstruction. The first approach
relies on the use of iterative modelbased 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 continuouswave (CW) and timedomain (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 timedomain 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.

55 
Development of Next Generation Image Reconstruction Algorithms for Diffuse Optical and Photoacoustic TomographyJaya Prakash, * January 2014 (has links) (PDF)
Biomedical optical imaging is capable of providing functional information of the soft biological tissues, whose applications include imaging large tissues, such breastand brain invivo. Biomedical optical imaging uses near infrared light (600nm900nm) as the probing media, givin ganaddedadvantageofbeingnonionizingimagingmodality. The tomographic technologies for imaging large tissues encompasses diﬀuse optical tomographyandphotoacoustictomography.
Traditional image reconstruction methods indiﬀuse optical tomographyemploysa
�2norm 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 sparserecovery methods utilize the �pnorm based regularization in the estimation problem with 0≤ p<1. These sparse recovery methods, along with an approximation to utilizethe �0norm, have been used forther econstruction of diﬀus eopticaltomographic images.The comparison of these methods was performed by increasing the sparsityinthesolution.
Further a model resolution matrix based framework was proposed and shown to induceblurinthe�2norm based regularization framework for diﬀuse optical tomography. This modelresolution matrix framework was utilized in the optical imaged econvolution framework. A basis pursuitdeconvolution based on Split AugmentedLagrangianShrinkageAlgorithm(SALSA)algorithm was used along with the Tikhonovregularization step making the image reconstruction into a twostep procedure. This new twostep approach was found to be robust with no iseandwasabletobetterdelineatethestructureswhichwasevaluatedusingnumericalandgelatinphantom experiments.
Modern diﬀuse optical imaging systems are multimodalin nature, where diﬀuse optical imaging is combined with traditional imaging modalitiessuc has Magnetic ResonanceImaging(MRI),or Computed Tomography(CT). Imageguided 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 imagereconstruction problem fromunderdetermined innaturetooverdetermined. 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 dataresolution 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 modelbased image reconstruction approaches in photoacoustic 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 modelbased methods deployTikhonovbasedregularizationschemetoreconstruct the initial pressure from the boundary acoustic data. Again a modelresolution for these cases tend to represent the blurinduced by the regularization scheme. A method that utilizes this blurringmodelandper forms the basis pursuit econvolution 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 modelresolution matrixis achievedvia the Lanczosbidiagonalization (leastsquares QR) making this approach computationally efﬁcient and deployable inrealtime.
Keywords
Medical imaging, biomedical optical imaging, diﬀuse optical tomography, photoacoustictomography, multimodalimaging, inverse problems,sparse recovery,computational methods inbiomedical optical imaging.

56 
Development of Novel Reconstruction Methods Based on l1Minimization for Near Infrared Diffuse Optical TomographyShaw, Calbvin B January 2012 (has links) (PDF)
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 nonlinear and illposed, 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 reconstructed 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 nonlinear iterative technique is known to perform better in comparison to linear techniques. The usage of nonlinear iterative techniques in the realtime, 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 l1based 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 multimodal 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.

57 
Development of Efficient Computational Methods for Better Estimation of Optical Properties in Diffuse Optical TomographyRavi Prasad, K J January 2013 (has links) (PDF)
Diffuse optical tomography (DOT) is one of the promising imaging modalities that pro
vides functional information of the soft biological tissues invivo, such as breast and brain tissues. The near infrared (NIR) light (6001000 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 modelbased image reconstruction algorithm using these limited boundary measurements.
Image reconstruction problem in DOT is known to be nonlinear, illposed, and some times underdetermined due to the multiple scattering of NIR light in the tissue. Solving this inverse problem requires regularization to obtain meaningful results, with Tikhonovtype 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 crossvalidation method. The results obtained using numerical and gelatin phantom data indicate that the MRMbased 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 nonquadratic penalty functions. The penalty functions that were used include, quadratic (`2), absolute (`1), Cauchy, and GemanMcClure. The regularization parameter in each of these cases were obtained automatically using the generalized crossvalidation (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, GemanMcClure have better utility in reconstructing highcontrast and complexshaped targets with GemanMcClure 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 opticalproperty estimation. With the aid of realistic patient breast threedimensional models, the variation in RI for different regions of tissue under investigation is shown to influence the estimation of optical properties in imageguided diffuse optical tomography (IGDOT) 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 IGDOT, 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 IGDOT 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 welldetermined 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 gradientfree NelderMead 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.

58 
Development Of Deterministic And Stochastic Algorithms For Inverse Problems Of Optical TomographyGupta, Saurabh 07 1900 (has links) (PDF)
Stable and computationally efficient reconstruction methodologies are developed to solve two important medical imaging problems which use nearinfrared (NIR) light as the source of interrogation, namely, diffuse optical tomography (DOT) and one of its variations, ultrasoundmodulated optical tomography (UMOT). Since in both these imaging modalities the system matrices are illconditioned 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 GaussNewton (GN) algorithm for DOT so that a videorate 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 GaussNewton 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 SVDbased 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 meansquare 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 quasiNewton 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 visàvis 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 pseudodynamic ensemble Kalman filter (PDEnKF) 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 PseudoDynamic EnKF (PDEnKF) 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, lowintensity Wiener noise processes in suitably constructed ‘measurement’ equations, the filter variables are treated as pseudostochastic 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 2D objects which are crosssections of a cylinder with background absorption and (reduced) scattering coefficient chosen as = 0.01 mm1 and = 1.0 mm1respectively. We also assume=0.02 mm1 within the inhomogeneity (for the single inhomogeneity case) and=0.02 and 0.03 mm1 (for the two inhomogeneities case). The reconstruction results by the PDEnKF are shown to be consistently superior to those through a deterministic and explicitly regularized GaussNewton 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 PDEnKF, which uses an ensemble square root filter, is also demonstrated in the context of UMOT by recovering the distribution of meansquared amplitude of vibration, related to the Young’s modulus, in the ultrasound focal volume. Since the ability of a coherent light probe to pickup the overall optical pathlength 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 quasiNewton scheme (e.g. the GN scheme) which crucially makes use of the derivative information. The derivativefree gainbased 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.

59 
Vývoj experimentálního modelu pro testování žloutkových protilátek jako prostředku profylaxe bakteriálních infekcí / Development of an experimental model for yolk antibody prophylaxis of bacterial infectionsHadrabová, Jana January 2015 (has links)
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...

60 
Retrieving Information from Scattered Photons in Medical ImagingJha, Abhinav K. January 2013 (has links)
In many medical imaging modalities, as photons travel from the emission source to the detector, they are scattered by the biological tissue. Often this scatter is viewed as a phenomenon that degrades image quality, and most research is focused on designing methods for either discarding the scattered photons or correcting for scatter. However, the scattered photons also carry information about the tissue that they pass through, which can perhaps be extracted. In this research, we investigate methods to retrieve information from the scattered photons in two specific medical imaging modalities: diffuse optical tomography (DOT) and single photon emission computed tomography (SPECT). To model the scattering of photons in biological tissue, we investigate using the Neumannseries form of the radiative transport equation (RTE). Since the scattering phenomenon are different in DOT and SPECT, the models are individually designed for each modality. In the DOT study, we use the developed photonpropagation model to investigate signal detectability in tissue. To study this detectability, we demonstrate the application of a surrogate figure of merit, based on Fisher information, which approximates the Bayesian ideal observer performance. In the SPECT study, our aim is to determine if only the SPECT emission data acquired in listmode (LM) format, including the scatteredphoton data, can be used to compute the tissueattenuation map. We first propose a pathbased formalism to process scattered photon data, and follow it with deriving expressions for the Fisher information that help determine the information content of LM data. We then derive a maximumlikelihood expectationmaximization algorithm that can jointly reconstruct the activity and attenuation map using LM SPECT emission data. While the DOT study can provide a boost in transition of DOT to clinical imaging, the SPECT study will provide insights on whether it is worth exposing the patient to extra Xray radiation dose in order to obtain an attenuation map. Finally, although the RTE can be used to model light propagation in tissues, it is computationally intensive and therefore time consuming. To increase the speed of computation in the DOT study, we develop software to implement the RTE on parallel computing architectures, specifically the NVIDIA graphics processing units (GPUs).

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