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

Developmental malformation of the corpus callosum: a review of typical callosal development and examples of developmental disorders with callosal involvement

This review provides an overview of the involvement of the corpus callosum (CC) in a variety of developmental disorders that are currently defined exclusively by genetics, developmental insult, and/or behavior. I begin with a general review of CC development, connectivity, and function, followed by discussion of the research methods typically utilized to study the callosum. The bulk of the review concentrates on specific developmental disorders, beginning with agenesis of the corpus callosum (AgCC)—the only condition diagnosed exclusively by callosal anatomy. This is followed by a review of several genetic disorders that commonly result in social impairments and/or psychopathology similar to AgCC (neurofibromatosis-1, Turner syndrome, 22q11.2 deletion syndrome, Williams yndrome, and fragile X) and two forms of prenatal injury (premature birth, fetal alcohol syndrome) known to impact callosal development. Finally, I examine callosal involvement in several common developmental disorders defined exclusively by behavioral patterns (developmental language delay, dyslexia, attention-deficit hyperactive disorder, autism spectrum disorders, and Tourette syndrome)

Watershed-based Segmentation of the Midsagittal Section of the Corpus Callosum in Diffusion MRI

Abstract -The corpus callosum (CC) is one of the most important white matter structures of the brain, interconnecting the two cerebral hemispheres. The CC is related to several diseases including dyslexia, autism, multiple sclerosis and lupus, which make its study even more important. We propose here a new approach for fully automatic segmentation of the midsagittal section of CC in magnetic resonance diffusion tensor images, including the automatic determination of the midsagittal slice of the brain . It uses the watershed transform and is performed on the fractional anisotropy map weighted by the projection of the principal eigenvector in the left-right direction. Experiments with real diffusion MRI data showed that the proposed method is able to quickly segment the CC and to the determinate the midsagittal slice without any user intervention. Since it is simple, fast a nd does not require parameter settings, the proposed method is well suited for clinical applications

Early cognitive neuropsychological profiles and development of reading skills

The present thesis sought to investigate the precise relationship between the cognitive and psychophysiological profiles of developing readers, of established readers and of failed readers. Phonological processing tasks and visuospatial tasks were used to assess relevant auditory and visual cognitive skills; handedness and EEG measures were used to provide indices of cortical organisation and activation. A 21/2 year longitudinal investigation of some 150 pre-readers provided evidence of mutually facilitative relationships between and within specific types of phonological skill and phonological memory. Early significance of visual skills was subsequently superseded by the importance of these phonological skills. The acquisition of early reading skills was associated with a shift towards increased dextrality as measured by hand skill and hand preference; this relationship was not evident in subsequent stages. Cross-sectional studies comparing dyslexic children with chronological- and reading-age matched controls extended these findings. The dyslexic readers displayed impaired phonological processing and phonological memory skills relative to chronological-age matched competent readers; similarities were observed between dyslexics and reading-age matched controls. Visual perceptual skills failed to differentiate between the chronological-age matched competent and impaired readers, although both out-performed younger control readers. ERP measures consistently demonstrated diffuse patterns of bilateral activation in dyslexic readers as opposed to asymmetric activity lateralised to the left hemisphere in control readers. Between group comparisons of inter-hemispheric activity revealed greater levels of right-hemisphere involvement in the dyslexic samples; between group comparisons of intra-hemispheric activity revealed evidence of greater involvement of fronto-central regions in the dyslexic samples. It is proposed that these data provide supportive evidence for the central involvement of phonological processing skills in the development of reading, underpinned by the normal development of asymmetric patterns of cortical lateralisation. Children where this development is delayed or deficient will display the reading difficulties characteristic of developmental dyslexia

A non-invasive diagnostic system for early assessment of acute renal transplant rejection.

Early diagnosis of acute renal transplant rejection (ARTR) is of immense importance for appropriate therapeutic treatment administration. Although the current diagnostic technique is based on renal biopsy, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. In this thesis, a computer-aided diagnostic (CAD) system for early detection of ARTR from 4D (3D + b-value) diffusion-weighted (DW) MRI data is developed. The CAD process starts from a 3D B-spline-based data alignment (to handle local deviations due to breathing and heart beat) and kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The latter is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and for on-going visual kidney-background appearances. A cumulative empirical distribution of apparent diffusion coefficient (ADC) at different b-values of the segmented DW-MRI is considered a discriminatory transplant status feature. Finally, a classifier based on deep learning of a non-negative constrained stacked auto-encoder is employed to distinguish between rejected and non-rejected renal transplants. In the “leave-one-subject-out” experiments on 53 subjects, 98% of the subjects were correctly classified (namely, 36 out of 37 rejected transplants and 16 out of 16 nonrejected ones). Additionally, a four-fold cross-validation experiment was performed, and an average accuracy of 96% was obtained. These experimental results hold promise of the proposed CAD system as a reliable non-invasive diagnostic tool

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

Reading Impairments In Children With Neurofibromatosis Type 1: Profiling and Treatment

Reading difficulties are reported in the majority of children with neurofibromatosis type 1 (NF1) and have significant functional impacts. Primary aims of this thesis were to i) examine the reading profile of children with NF1, from preschool to school-age, and ii) investigate the effectiveness of a reading intervention. Three studies were conducted. Study 1 examined the preliteracy abilities of young children (5-6 years) with NF1 compared to controls. Results indicated that the performance of children with NF1 was significantly poorer than controls in all preliteracy domains. Study 2 investigated the reading profile of school-age children (7-12 years) compared to controls. Results revealed that the performance of children with NF1 was significantly poorer than controls on all literacy measures. Study 3 examined the efficacy of a computerised phonics training program for school-age children with NF1 and reading difficulties. Following treatment, children demonstrated significant improvement on literacy outcomes. Collectively these studies contribute to our understanding of the reading profile of children with NF1. Findings show that weaknesses in phonological processing and letter-sound knowledge are common in children from preschool to school-age indicating these deficits may underlie the reading difficulties experienced by children with NF1. Further these weaknesses can be detected as early as preschool age. Findings also indicate that reading deficits in children with NF1 can be successfully treated by teaching letter-sound knowledge. These findings can be used to inform clinical practice. The implementation of preliteracy screening for all young children and early intervention, may result in a greater success rate of remediation, decrease the likelihood of later literacy difficulties and contribute to significant improvements in the quality of life for children with NF1

Functional and structural connectivity of reading networks in the adult brain

Language processing draws upon many distributed regions in the brain. Reading in particular is a skill that emerges from the interaction between brain regions involved in phonological and orthographical processing. This project examined the reading network in adults (18-35 years old) with and without developmental dyslexia. Each participant was assessed on a comprehensive battery of standardised neuropsychological tests, which assessed IQ, reading accuracy and comprehension, spelling, phonological processing, working memory, grammatical understanding, motor coordination, and expressive and receptive language skills. In addition, each participant underwent a non-invasive MRI scan, during which structural and functional images were acquired. More specifically, T1-weighted and diffusion-weighted images were acquired to assess structural networks in the brain, whereas a simple reading task and resting-state fMRI were acquired to assess the functional networks involved in reading. Individuals with dyslexia were found to show reduced activation and reduced connectivity in regions typically associated with skilled reading. Moreover, results suggested that they rely on more effortful processing and attentional mechanisms instead to compensate for their reading difficulties. All in all, results indicated that individuals with developmental dyslexia had abnormal functional and structural brain networks related to reading performance, as well as other functions, such as working memory. These findings suggest that for successful reading remediation, it is important to focus on the integration of phonology with orthography, as well as with working memory. Literacy problems such as developmental dyslexia are thus better characterised as a complex disorder with multiple deficits rather than by a single phonological deficit