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

Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range

Carotid plaque vulnerability assessment by microscopic morphology analysis, ultrasound and 3D model reconstruction

This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University.Research suggests that plaque morphology plays a crucial role in determining plaque vulnerability. However the relationship between plaque morphology and rupture is still not clearly understood due to the limited information of plaque morphology. The aim of this study is to improve our understanding of the relationship between plaque morphology and rupture, and to use this to predict the risk of plaque rupture from the morphology at the molecular level. This can enable the identification of culprit lesions in clinical situations for assessing plaque rupture risk. Histological assessments were carried out on 18 carotid plaque specimens. The 3-D collagen, lipid and macrophage distributions along the entire length of the plaque were analysed in both ruptured and non-ruptured symptomatic plaques. In addition, plaque morphology on the rupture sites were examined and compared with the surrounding regions. It was found that ruptured plaques had thinner fibrous caps and larger lipid cores compared to non-ruptured plaques. Also, ruptured plaques had lower collagen content compared to non-ruptured plaques, and higher collagen contents upstream compared to downstream region from the plaque throat. At the rupture site there was lower collagen content, and a larger lipid core thickness behind a thin fibrous cap compared with the mean for the longitudinally adjacent and circumferential regions. Macrophage cells were located nearer to the boundary of the luminal wall in ruptured plaques. For both groups, the area occupied by macrophages is greater at the upstream shoulder of the plaque. There is a positive correlation between macrophage area and lipid core area, a negative correlation between macrophage area and collagen content, and between lipid core size and collagen content for both plaque groups. 3D reconstruction of ex-vivo specimens of carotid plaques were carried out by a combined analysis of US imaging and histology. To reconstruct accurate 3D plaque morphology, the non-linear tissue distortion in histological images caused by specimen preparation was corrected by a finite element (FE) based deformable registration procedure. This study shows that it is possible to generate a 3D patient specific plaque model using this method. In addition, the study also quantitatively assesses the tissue distortion caused by histological procedures. It shows that at least 30% tissue shrinkage is expected for plaque tissues. The histology analysis result was also used to evaluate ultrasound (US) tissue characterization accuracy. An ex-vivo 2D ultrasound scan set-up was used to obtain serial transverse images through an atherosclerotic plaque. The different plaque component region obtained from ultrasound images was compared with the associated histology result and photograph of the sections. Plaque tissue characterisation using ex-vivo US can be performed qualitatively, whereas lipid core assessment from ultrasound scan can be semi-quantitative. This finding combined with the negative correlation between lipid core size and collagen content, suggests the ability of US to indirectly quantify plaque collagen content. This study may serve as a platform for future studies on improving ultrasound tissue characterization, and may also potentially be used in risk assessment of plaque rupture

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention

Evolution and interaction of damage modes in fabric-reinforced composites under dynamic flexural loading

In this paper, an experimental study is performed to characterise the behaviour of fabric-reinforced composites used in sports products under large-deflection bending in Izod-type impact tests. X-ray micro computed tomography (micro-CT) is used to investigate various damage modes in the impacted CFRP specimens. It revealed that matrix cracking, delaminations, tow debonding, and fibre fracture were the prominent damage modes. Three-dimensional finite-element models are developed to study the onset, progression and interaction of some damage modes such as delamination and fabric fracture observed with micro-CT. A damage modelling technique based on a cohesive-zone method, which is more efficient than continuum damage mechanics approach, is proposed for analysis of interaction of damage modes. The developed numerical models are capable to simulate the damage mechanisms and their interaction observed in the tests. In this study, the pattern of damage formation observed in specimens was front-to-back, unlike bottom-to-top one in drop weight impact tests. The effect of boundary conditions on the dynamic response and damage evolution of composite laminates is also investigated. © 2013 Elsevier Ltd