Multidimensional image analysis of cardiac function in MRI

Cardiac morphology is a key indicator of cardiac health. Important metrics that are currently in clinical use are left-ventricle cardiac ejection fraction, cardiac muscle (myocardium) mass, myocardium thickness and myocardium thickening over the cardiac cycle. Advances in imaging technologies have led to an increase in temporal and spatial resolution. Such an increase in data presents a laborious task for medical practitioners to analyse. In this thesis, measurement of the cardiac left-ventricle function is achieved by developing novel methods for the automatic segmentation of the left-ventricle blood-pool and the left ventricle myocardium boundaries. A preliminary challenge faced in this task is the removal of noise from Magnetic Resonance Imaging (MRI) data, which is addressed by using advanced data filtering procedures. Two mechanisms for left-ventricle segmentation are employed. Firstly segmentation of the left ventricle blood-pool for the measurement of ejection fraction is undertaken in the signal intensity domain. Utilising the high discrimination between blood and tissue, a novel methodology based on a statistical partitioning method offers success in localising and segmenting the blood pool of the left ventricle. From this initialisation, the estimation of the outer wall (epi-cardium) of the left ventricle can be achieved using gradient information and prior knowledge. Secondly, a more involved method for extracting the myocardium of the leftventricle is developed, that can better perform segmentation in higher dimensions. Spatial information is incorporated in the segmentation by employing a gradient-based boundary evolution. A level-set scheme is implemented and a novel formulation for the extraction of the cardiac muscle is introduced. Two surfaces, representing the inner and the outer boundaries of the left-ventricle, are simultaneously evolved using a coupling function and supervised with a probabilistic model of expertly assisted manual segmentations

Segmentation, tracking, and kinematics of lung parenchyma and lung tumors from 4D CT with application to radiation treatment planning.

This thesis is concerned with development of techniques for efficient computerized analysis of 4-D CT data. The goal is to have a highly automated approach to segmentation of the lung boundary and lung nodules inside the lung. The determination of exact lung tumor location over space and time by image segmentation is an essential step to track thoracic malignancies. Accurate image segmentation helps clinical experts examine the anatomy and structure and determine the disease progress. Since 4-D CT provides structural and anatomical information during tidal breathing, we use the same data to also measure mechanical properties related to deformation of the lung tissue including Jacobian and strain at high resolutions and as a function of time. Radiation Treatment of patients with lung cancer can benefit from knowledge of these measures of regional ventilation. Graph-cuts techniques have been popular for image segmentation since they are able to treat highly textured data via robust global optimization, avoiding local minima in graph based optimization. The graph-cuts methods have been used to extract globally optimal boundaries from images by s/t cut, with energy function based on model-specific visual cues, and useful topological constraints. The method makes N-dimensional globally optimal segmentation possible with good computational efficiency. Even though the graph-cuts method can extract objects where there is a clear intensity difference, segmentation of organs or tumors pose a challenge. For organ segmentation, many segmentation methods using a shape prior have been proposed. However, in the case of lung tumors, the shape varies from patient to patient, and with location. In this thesis, we use a shape prior for tumors through a training step and PCA analysis based on the Active Shape Model (ASM). The method has been tested on real patient data from the Brown Cancer Center at the University of Louisville. We performed temporal B-spline deformable registration of the 4-D CT data - this yielded 3-D deformation fields between successive respiratory phases from which measures of regional lung function were determined. During the respiratory cycle, the lung volume changes and five different lobes of the lung (two in the left and three in the right lung) show different deformation yielding different strain and Jacobian maps. In this thesis, we determine the regional lung mechanics in the Lagrangian frame of reference through different respiratory phases, for example, Phase10 to 20, Phase10 to 30, Phase10 to 40, and Phase10 to 50. Single photon emission computed tomography (SPECT) lung imaging using radioactive tracers with SPECT ventilation and SPECT perfusion imaging also provides functional information. As part of an IRB-approved study therefore, we registered the max-inhale CT volume to both VSPECT and QSPECT data sets using the Demon\u27s non-rigid registration algorithm in patient subjects. Subsequently, statistical correlation between CT ventilation images (Jacobian and strain values), with both VSPECT and QSPECT was undertaken. Through statistical analysis with the Spearman\u27s rank correlation coefficient, we found that Jacobian values have the highest correlation with both VSPECT and QSPECT

Construction of boundary element models in bioelectromagnetism

Multisensor electro- and magnetoencephalographic (EEG and MEG) as well as electro- and magnetocardiographic (ECG and MCG) recordings have been proved useful in noninvasively extracting information on bioelectric excitation. The anatomy of the patient needs to be taken into account, when excitation sites are localized by solving the inverse problem. In this work, a methodology has been developed to construct patient specific boundary element models for bioelectromagnetic inverse problems from magnetic resonance (MR) data volumes as well as from two orthogonal X-ray projections. The process consists of three main steps: reconstruction of 3-D geometry, triangulation of reconstructed geometry, and registration of the model with a bioelectromagnetic measurement system. The 3-D geometry is reconstructed from MR data by matching a 3-D deformable boundary element template to images. The deformation is accomplished as an energy minimization process consisting of image and model based terms. The robustness of the matching is improved by multi-resolution and global-to-local approaches as well as using oriented distance maps. A boundary element template is also used when 3-D geometry is reconstructed from X-ray projections. The deformation is first accomplished in 2-D for the contours of simulated, built from the template, and real X-ray projections. The produced 2-D vector field is back-projected and interpolated on the 3-D template surface. A marching cube triangulation is computed for the reconstructed 3-D geometry. Thereafter, a non-iterative mesh-simplification method is applied. The method is based on the Voronoi-Delaunay duality on a 3-D surface with discrete distance measures. Finally, the triangulated surfaces are registered with a bioelectromagnetic measurement utilizing markers. More than fifty boundary element models have been successfully constructed from MR images using the methods developed in this work. A simulation demonstrated the feasibility of X-ray reconstruction; some practical problems of X-ray imaging need to be solved to begin tests with real data.reviewe

Lung Imaging and Function Assessment using Non-Contrast-Enhanced Magnetic Resonance Imaging

Measurement of pulmonary ventilation and perfusion has significant clinical value for the diagnosis and monitoring of prevalent lung diseases. To this end, non-contrast-enhanced MRI techniques have emerged as a promising alternative to scintigraphical measurements, computed tomography, and contrast-enhanced MRI. Although these techniques allow the acquisition of both structural and functional information in the same scan session, they are prone to robustness issues related to imaging artifacts and post-processing techniques, limiting their clinical utilization. In this work, new acquisition and post-processing techniques were introduced for improving the robustness of non-contrast-enhanced MRI based functional lung imaging. Furthermore, pulmonary functional maps were acquired in 2-year-old congenital diaphragmatic hernia (CDH) patients to demonstrate the feasibility of non-contrast-enhanced MRI methods for functional lung imaging. In the first study, a multi-acquisition framework was developed to improve robustness against field inhomogeneity artifacts. This method was evaluated at 1.5T and 3T field strengths via acquisitions obtained from healthy volunteers. The results demonstrate that the proposed acquisition framework significantly improved ventilation map homogeneity p<0.05. In the second study, a post-processing method based on dynamic mode decomposition (DMD) was developed to accurately identify dominant spatiotemporal patterns in the acquisitions. This method was demonstrated on digital lung phantoms and in vivo acquisitions. The findings indicate that the proposed method led to a significant reduction in dispersion of estimated ventilation and perfusion map amplitudes across different number of measurements when compared with competing methods p<0.05. In the third study, the free-breathing non-contrast-enhanced dynamic acquisitions were obtained from 2-year-old patients after CDH repair, and then processed using the DMD to obtain pulmonary functional maps. Afterwards, functional differences between ipsilateral and contralateral lungs were assessed and compared with results obtained using contrast-enhanced MRI measurements. The results demonstrate that pulmonary ventilation and perfusion maps can be generated from dynamic acquisitions successfully without the need for ionizing radiation or contrast agents. Furthermore, lung perfusion parameters obtained with DMD MRI correlate very strongly with parameters obtained using dynamic contrast-enhanced MRI. In conclusion, the presented work improves the robustness and accuracy of non-contrast-enhanced functional lung imaging using MRI. Overall, the methods introduced in this work may serve as a valuable tool in the clinical adaptation of non-contrast-enhanced imaging methods and may be used for longitudinal assessments of pulmonary functional changes

Quantitative Evaluation of Pulmonary Emphysema Using Magnetic Resonance Imaging and x-ray Computed Tomography

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality affecting at least 600 million people worldwide. The most widely used clinical measurements of lung function such as spirometry and plethysmography are generally accepted for diagnosis and monitoring of the disease. However, these tests provide only global measures of lung function and they are insensitive to early disease changes. Imaging tools that are currently available have the potential to provide regional information about lung structure and function but at present are mainly used for qualitative assessment of disease and disease progression. In this thesis, we focused on the application of quantitative measurements of lung structure derived from 1H magnetic resonance imaging (MRI) and high resolution computed tomography (CT) in subjects diagnosed with COPD by a physician. Our results showed that significant and moderately strong relationship exists between 1H signal intensity (SI) and 3He apparent diffusion coefficient (ADC), as well as between 1H SI and CT measurements of emphysema. This suggests that these imaging methods may be quantifying the same tissue changes in COPD, and that pulmonary 1H SI may be used effectively to monitor emphysema as a complement to CT and noble gas MRI. Additionally, our results showed that objective multi-threshold analysis of CT images for emphysema scoring that takes into account the frequency distribution of each Hounsfield unit (HU) threshold was effective in correctly classifying the patient into COPD and healthy subgroups. Finally, we found a significant correlation between whole lung average subjective and objective emphysema scores with high inter-observer agreement. It is concluded that 1H MRI and high resolution CT can be used to quantitatively evaluate lung tissue alterations in COPD subjects

Zeitabhängige, multimodale Modellierung und Analyse von Herzdaten

Kardiovaskuläre Erkrankungen stellen in den westlichen Industrienationen eine der Haupttodesursachen dar. Für die Diagnostik steht inzwischen mit der Computer-Tomographie ein leistungsfähiges bildgebendes Verfahren zur Verfügung. Im Rahmen dieser Arbeit wurden Verfahren entwickelt, um dem Radiologen durch eine weitgehend automatische und umfassende Analyse von 4D-CTA-Daten und der automatischen Berechnung wichtiger diagnostischer Parameter zu unterstützen

Attenuation correction of myocardial perfusion scintigraphy images without transmission scanning

Attenuation correction is essential for reliable interpretation of emission tomography; however the use of transmission measurements to generate attenuation maps is limited by availability of equipment and potential mismatches between the transmission and emission measurements. This work investigates the possibility of estimating an attenuation map using measured scatter data without a transmission scan. A scatter model has been developed that predicts the distribution of photons which have been scattered once. The scatter model has been used as the basis of a maximum likelihood gradient ascent method (SMLGA) to estimate an attenuation map from measured scatter data. The SMLGA algorithm has been combined with an existing algorithm using photopeak data to estimate an attenuation map (MLAA) in order to obtain a more accurate attenuation map than using either algorithm alone. Iterations of the SMLGA-MLAA algorithm are alternated with iterations of the MLEM algorithm to estimate the activity distribution. Initial tests of the algorithm were performed in 2 dimensions using idealised data before extension to 3 dimensions. The basic algorithm has been tested in 3 dimensions using projection data simulated using a Monte Carlo simulator with software phantoms. All soft tissues within the body have similar attenuation characteristics and so only a small number of different values are normally present. A Level-Set technique to restrict the attenuation map to a piecewise constant function has therefore been investigated as a potential way to improve the quality of the reconstructed attenuation map. The basic SMLGA-MLAA algorithm contains a number of assumptions; the effect of these has been investigated and the model extended to include the effect of photons which are scattered more than once and scatter correction of the photopeak. The effect of different phantom shapes and activity distributions has been assessed and the final algorithm tested using data acquired using a physical phantom