Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Hydrodynamic Characterization of Physicochemical Process in Stirred Tanks and Agglomeration Reactors

A short review of the state of the art in experimental and computational fluid dynamics (CFD) characterization of micro-hydrodynamics and physicochemical processes in stirred tanks and agglomeration reactors is presented. Results of experimental and computational studies focusing on classical mixing tanks as well as other innovative reactors with various industrial applications are briefly reviewed. The hydrodynamic characterization techniques as well as the influence of the fluid dynamics on the efficiency of the physicochemical processes have been highlighted including some of the limitations of the reported modeling approach and solution strategy. Finally, the need for specialized CFD codes tailored to the specific needs of fluid-particle reactor design and optimization is advocated to advance research in this field

HIGH-FREQUENCY MOTION RESIDUALS IN MULTIBEAM ECHOSOUNDER DATA: ANALYSIS AND ESTIMATION

Advances in multibeam sonar mapping and data visualization have increasingly brought to light the subtle integration errors remaining in bathymetric datasets. Traditional field calibration procedures, such as the patch test, just account for static orientation bias and sonar-to-position latency. This, however, ignores the generally subtler integration problems that generate time-varying depth errors. Such dynamic depth errors are the result of an unknown offset in one or more of orientation, space, sound speed or time between the sonar and ancillary sensors. Such errors are systematic, and thus should be predictable, based on their relationship between the input data and integrated output. A first attempt at addressing this problem utilized correlations between motion and temporally smoothed, ping-averaged residuals. The known limitations of that approach, however, included only being able to estimate the dominant integration error, imperfectly accounting for irregularly spaced sounding distribution and only working in shallow water. This thesis presents a new and improved means of considering the dynamics of the integration error signatures which can address multiple issues simultaneously, better account for along-track sounding distribution, and is not restricted to shallow water geometry. The motion-driven signatures of six common errors are simultaneously identified. This is achieved through individually considering each sounding’s input-error relationship along extended sections of a single swath corridor. Such an approach provides a means of underway system optimization using nothing more than the bathymetry of typical seafloors acquired during transit. Initial results of the new algorithm are presented using data generated from a simulator, with known inputs and integration errors, to test the efficacy of the method. Results indicate that successful estimation requires conditions of significant vessel motion over periods of a few tens of seconds as well as smooth, gently rolling bathymetry along the equivalent spatial extent covered by the moving survey platform

Hydrodynamic CFD modeling of a pharmaceutical reactor vessel provided with a retreat-blade impeller under different baffling conditions

In the pharmaceutical industry, glass-lined reactors and vessels are often utilized to carry out a variety of different unit operations. Within these systems, both the vessel and impellers are typically glass-lined in order to provide superior corrosion resistance, prevent product contamination, and enhance cleanability. This approach, in turn, often requires the use of different, and sometimes sub-optimal, baffling conditions, which affect the hydrodynamics of the vessels and the reactor performance. Computational Fluid Dynamics (CFD) is a computational tool that employs numerical methods and algorithms to discretize and numerically solve partial differential equations (PDEs) representing mass, energy, and momentum conservation equations for the purpose of analyzing fluid flow problems. In recent years, CFD has been used successfully to model hydrodynamically complex systems such as stirred mixing systems. A variety of computational approaches and models are implemented in the CFD code to do so, including single reference frame (SRF), multiple reference frame (MRF), and sliding mesh (SM) models, also possibly combined with Volume of Fluid (VOF) models. In this study, a scaled-down version of a pharmaceutical glass-lined reactor vessel equipped with a retreat curve impeller (RCI) and a torispherical bottom is modeled using the CFD COMSOL software under a variety of setups, including variations in impeller speed, impeller clearance, and baffling conditions. Several modeling approaches are used. The CFD simulations result in the prediction of the power dissipated by the impeller and therefore the impeller Power Number. These predictions are then compared with the experimental results obtained in previous work by this group. In the fully baffled system, the values of the Power Numbers predicted by the simulations under turbulent conditions using MRF modeling are in close agreement with the experimental results across all tested impeller rotational speeds. In the partially baffled system, the results obtained with MRF modeling are very consistent with the experimental results. However, even better agreement is obtained when using the much more computationally expensive SM modeling technique. Finally, the simpler SRF approach proves to be very appropriate to model the unbaffled system, and good agreement between the simulation predictions and the experimental results is obtained, but only if the surface deformation of the liquid-air interface typically observed in unbaffled systems is small. It can be concluded that the computational method used to simulate the hydrodynamic behavior of a pharmaceutical reactor vessel generates predictions that are in close agreement with experimental results, thus validating the CFD approach used to model this system

Advanced Algorithms for 3D Medical Image Data Fusion in Specific Medical Problems

Fúze obrazu je dnes jednou z nejběžnějších avšak stále velmi diskutovanou oblastí v lékařském zobrazování a hraje důležitou roli ve všech oblastech lékařské péče jako je diagnóza, léčba a chirurgie. V této dizertační práci jsou představeny tři projekty, které jsou velmi úzce spojeny s oblastí fúze medicínských dat. První projekt pojednává o 3D CT subtrakční angiografii dolních končetin. V práci je využito kombinace kontrastních a nekontrastních dat pro získání kompletního cévního stromu. Druhý projekt se zabývá fúzí DTI a T1 váhovaných MRI dat mozku. Cílem tohoto projektu je zkombinovat stukturální a funkční informace, které umožňují zlepšit znalosti konektivity v mozkové tkáni. Třetí projekt se zabývá metastázemi v CT časových datech páteře. Tento projekt je zaměřen na studium vývoje metastáz uvnitř obratlů ve fúzované časové řadě snímků. Tato dizertační práce představuje novou metodologii pro klasifikaci těchto metastáz. Všechny projekty zmíněné v této dizertační práci byly řešeny v rámci pracovní skupiny zabývající se analýzou lékařských dat, kterou vedl pan Prof. Jiří Jan. Tato dizertační práce obsahuje registrační část prvního a klasifikační část třetího projektu. Druhý projekt je představen kompletně. Další část prvního a třetího projektu, obsahující specifické předzpracování dat, jsou obsaženy v disertační práci mého kolegy Ing. Romana Petera.Image fusion is one of today´s most common and still challenging tasks in medical imaging and it plays crucial role in all areas of medical care such as diagnosis, treatment and surgery. Three projects crucially dependent on image fusion are introduced in this thesis. The first project deals with the 3D CT subtraction angiography of lower limbs. It combines pre-contrast and contrast enhanced data to extract the blood vessel tree. The second project fuses the DTI and T1-weighted MRI brain data. The aim of this project is to combine the brain structural and functional information that purvey improved knowledge about intrinsic brain connectivity. The third project deals with the time series of CT spine data where the metastases occur. In this project the progression of metastases within the vertebrae is studied based on fusion of the successive elements of the image series. This thesis introduces new methodology of classifying metastatic tissue. All the projects mentioned in this thesis have been solved by the medical image analysis group led by Prof. Jiří Jan. This dissertation concerns primarily the registration part of the first project and the classification part of the third project. The second project is described completely. The other parts of the first and third project, including the specific preprocessing of the data, are introduced in detail in the dissertation thesis of my colleague Roman Peter, M.Sc.