A study of information-theoretic metaheuristics applied to functional neuroimaging datasets

This dissertation presents a new metaheuristic related to a two-dimensional ensemble empirical mode decomposition (2DEEMD). It is based on Green’s functions and is called Green’s Function in Tension - Bidimensional Empirical Mode Decomposition (GiT-BEMD). It is employed for decomposing and extracting hidden information of images. A natural image (face image) as well as images with artificial textures have been used to test and validate the proposed approach. Images are selected to demonstrate efficiency and performance of the GiT-BEMD algorithm in extracting textures on various spatial scales from the different images. In addition, a comparison of the performance of the new algorithm GiT-BEMD with a canonical BEEMD is discussed. Then, GiT-BEMD as well as canonical bidimensional EEMD (BEEMD) are applied to an fMRI study of a contour integration task. Thus, it explores the potential of employing GiT-BEMD to extract such textures, so-called bidimensional intrinsic mode functions (BIMFs), of functional biomedical images. Because of the enormous computational load and the artifacts accompanying the extracted textures when using a canonical BEEMD, GiT-BEMD is developed to cope with such challenges. It is seen that the computational cost is decreased dramatically, and the quality of the extracted textures is enhanced considerably. Consequently, GiT-BEMD achieves a higher quality of the estimated BIMFs as can be seen from a direct comparison of the results obtained with different variants of BEEMD and GiT-BEMD. Moreover, results generated by 2DBEEMD, especially in case of GiT-BEMD, distinctly show a superior precision in spatial localization of activity blobs when compared with a canonical general linear model (GLM) analysis employing statistical parametric mapping (SPM). Furthermore, to identify most informative textures, i.e. BIMFs, a support vector machine (SVM) as well as a random forest (RF) classifier is employed. Classification performance demonstrates the potential of the extracted BIMFs in supporting decision making of the classifier. With GiT-BEMD, the classification performance improved significantly which might also be a consequence of a clearer structure for these modes compared to the ones obtained with canonical BEEMD. Altogether, there is strong believe that the newly proposed metaheuristic GiT-BEMD offers a highly competitive alternative to existing BEMD algorithms and represents a promising technique for blindly decomposing images and extracting textures thereof which may be used for further analysis

Estimation of Dose Distribution for Lu-177 Therapies in Nuclear Medicine

In nuclear medicine, two frequent applications of 177-Lu therapy exist: DOTATOC therapy for patients with a neuroendocrine tumor and PSMA thearpy for prostate cancer. During the therapy a pharmaceutical is injected intravenously, which attaches to tumor cells due to its molecular composition. Since the pharmaceutical contains a radioactive 177Lu isotope, tumor cells are destroyed through irradiation. Afterwards the substance is excreted via the kidneys. Since the latter are very sensitive to high energy radiation, it is necessary to compute exactly how much radioactivity can be administered to the patient without endangering healthy organs. This calculation is called dosimetry and currently is made according to the state of the art MIRD method. At the beginning of this work, an error assessment of the established method is presented, which has determined an overall error of 25% in the renal dose value. The presented study improves and personalizes the MIRD method in several respects and reduces individual error estimates considerably. In order to be able to estimate of the amount of activity, first a test dose is injected to the patient. Subsequently, after 4h, 24h, 48h and 72h SPECT images are taken. From these images the activity at each voxel can be obtained a specified time points, i. e. the physical decline and physiological metabolization of the pharmaceutical can be followed in time. To calculate the amount of decay in each voxel from the four SPECT registrations, a time activity curve must be integrated. In this work, a statistical method was developed to estimate the time dependent activity and then integrate a voxel-by-voxel time-activity curve. This procedure results in a decay map for all available 26 patients (13 PSMA/13 DOTATOC). After the decay map has been estimated, a full Monte Carlo simulation has been carried out on the basis of these decay maps to determine a related dose distribution. The simulation results are taken as reference (“Gold Standard”) and compared with methods for an approximate but faster estimation of the dose distribution. Recently, a convolution with Dose Voxel Kernels (DVK) has been established as a standard dose estimation method (Soft Tissue Scaling STS). Thereby a radioactive Lutetium isotope is placed in a cube consisting of soft tissue. Then radiation interactions are simulated for a number of 10^10 decays. The resulting Dose Voxel Kernel is then convolved with the estimated decay map. The result is a dose distribution, which, however, does not take into account any tissue density differences. To take tissue inhomogeneities into account, three methods are described in the literature, namely Center Scaling (CS), Density Scaling (DS), and Percentage Scaling (PS). However, their application did not improve the results of the STS method as is demonstrated in this study. Consequently, a neural network was trained finally to estimate DVKs adapted to the respective individual tissue density distribution. During the convolution process, it uses for each voxel an adapted DVK that was deduced from the corresponding tissue density kernel. This method outperformed the MIRD method, which resulted in an uncertainty of the renal dose between -42.37-10.22% an achieve a reduction in the uncertainty to a range between -26.00%-7.93%. These dose deviations were calculated for 26 patients and relate to the mean renal dose compared with the respective result of the Monte Carlo simulation. In order to improve the estimates of dose distribution even further, a 3D 2D neural network was trained in the second part of the work. This network predicts the dose distribution of an entire patient. In combination with an Empirical Mode Decomposition, this method achieved deviations of only -12.21%-2.13% . The mean deviation of the dose estimates is in the range of the statistical error of the Monte Carlo simulation. In the third part of the work, a neural network was used to automatically segment the kidney, spleen and tumors. Compared to an established segmentation algorithm, the method developed in this work can segment tumors because it uses not only the CT image as input, but also the SPECT image

Context-aware Facial Inpainting with GANs

Facial inpainting is a difficult problem due to the complex structural patterns of a face image. Using irregular hole masks to generate contextualised features in a face image is becoming increasingly important in image inpainting. Existing methods generate images using deep learning models, but aberrations persist. The reason for this is that key operations are required for feature information dissemination, such as feature extraction mechanisms, feature propagation, and feature regularizers, are frequently overlooked or ignored during the design stage. A comprehensive review is conducted to examine existing methods and identify the research gaps that serve as the foundation for this thesis. The aim of this thesis is to develop novel facial inpainting algorithms with the capability of extracting contextualised features. First, Symmetric Skip Connection Wasserstein GAN (SWGAN) is proposed to inpaint high-resolution face images that are perceptually consistent with the rest of the image. Second, a perceptual adversarial Network (RMNet) is proposed to include feature extraction and feature propagation mechanisms that target missing regions while preserving visible ones. Third, a foreground-guided facial inpainting method is proposed with occlusion reasoning capability, which guides the model toward learning contextualised feature extraction and propagation while maintaining fidelity. Fourth, V-LinkNet is pro-posed that takes into account of the critical operations for information dissemination. Additionally, a standard protocol is introduced to prevent potential biases in performance evaluation of facial inpainting algorithms. The experimental results show V-LinkNet achieved the best results with SSIM of 0.96 on the standard protocol. In conclusion, generating facial images with contextualised features is important to achieve realistic results in inpainted regions. Additionally, it is critical to consider the standard procedure while comparing different approaches. Finally, this thesis outlines the new insights and future directions of image inpainting

Aeronautical engineering: A cumulative index to a continuing bibliography (supplement 235)

This publication is a cummulative index to the abstracts contained in Supplements 223 through 234 of Aeronautical Engineering: A Continuing Bibliography. The bibliographic series is compiled through the cooperative efforts of the American Institute of Aeronautics and Astronautics (AIAA) and the National Aeronautics and Space Administration (NASA). Seven indexes are included -- subject, personal author, corporate source, foreign technology, contract number, report number and accession number