Connectivity of the Superficial Muscles of the Human Perineum: A Diffusion Tensor Imaging-Based Global Tractography Study.

Despite the importance of pelvic floor muscles, significant controversy still exists about the true structural details of these muscles. We provide an objective analysis of the architecture and orientation of the superficial muscles of the perineum using a novel approach. Magnetic Resonance Diffusion Tensor Images (MR-DTI) were acquired in 10 healthy asymptomatic nulliparous women, and 4 healthy males. Global tractography was then used to generate the architecture of the muscles. Micro-CT imaging of a male cadaver was performed for validation of the fiber tracking results. Results show that muscles fibers of the external anal sphincter, from the right and left side, cross midline in the region of the perineal body to continue as transverse perinea and bulbospongiosus muscles of the opposite side. The morphology of the external anal sphincter resembles that of the number '8' or a "purse string". The crossing of muscle fascicles in the perineal body was supported by micro-CT imaging in the male subject. The superficial muscles of the perineum, and external anal sphincter are frequently damaged during child birth related injuries to the pelvic floor; we propose the use of MR-DTI based global tractography as a non-invasive imaging technique to assess damage to these muscles

Orientation matching for diffusion tensor image registration.

This thesis develops a registration algorithm specifically for diffusion-tensor (DT) images. The proposed approach matches the tensor orientations to find the registration transformation. Early results show that local optimisation does not find the global minimum in registration of DT-MR brain images. Therefore, a global optimisation registration technique is also implemented. This thesis proposes several new similarity measures for DT registration and provides a comparison of them along with several others previously proposed in the literature. The thesis also proposes several new performance evaluation measures to assess registration quality and develops a performance evaluation framework that uses directional coherence and landmark separation. Experiments with direct optimisation demonstrate increased local minima in tensor registration objective functions over scalar registration. Using registration with global optimisation, this thesis compares the performance of scalar-derived similarity measures with those derived from the full tensor. Results suggest that similarity measures derived from the full tensor matrix do not find a more accurate registration than those based on the derived scalar indices. Affine and higher-order polynomial registration is not reliable enough to make a firm conclusion about whether diffusion tensor orientation matching improves the accuracy of registration over registration algorithms that ignore orientation. The main problem preventing a firm conclusion is that the local minima problem persists despite the use of global optimisation, causing poor registration of the regions of interest

Unsupervised connectivity-based cortex parcellation using the information bottleneck method

In this dissertation, we embody an information-theoretic framework to compress and therefore cluster anatomical connectivity data that avoids many assumptions and drawbacks imposed by previous methods

Structural and Functional Brain Connectivity in Middle-Aged Carriers of Risk Alleles for Alzheimer\u27s Disease

Single nucleotide polymorphisms (SNPs) in APOE, COMT, BDNF, and KIBRA have been associated with age-related memory performance and executive functioning as well as risk for Alzheimer’s disease (AD). The purpose of the present investigation was to characterize differences in brain functional and structural integrity associated with these SNPs as potential endophenotypes of age-related cognitive decline. I focused my investigation on healthy, cognitively normal middle-aged adults, as disentangling the early effects of healthy versus pathological aging in this group may aid early detection and prevention of AD. The aims of the study were 1) to characterize SNP-related differences in functional connectivity within two resting state networks (RSNs; default mode network [DMN] and executive control network [ECN]) associated with memory and executive functioning, respectively; 2) to identify differences in the white matter (WM) microstructural integrity of tracts underlying these RSNs; and 3) to characterize genotype differences in the graph properties of an integrated functional-structural network. Participants (age 40-60, N = 150) underwent resting state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), and genotyping. Independent components analysis (ICA) was used to derive RSNs, while probabilistic tractography was performed to characterize tracts connecting RSN subregions. A technique known as functional-by-structural hierarchical (FSH) mapping was used to create the integrated, whole brain functional-structural network, or resting state structural connectome (rsSC). I found that BDNF risk allele carriers had lower functional connectivity within the DMN, while KIBRA risk allele carriers had poorer WM microstructural integrity in tracts underlying the DMN and ECN. In addition to these differences in the connectivity of specific RSNs, I found significant impairments in the global and local topology of the rsSC across all evaluated SNPs. Collectively, these findings suggest that integrating multiple neuroimaging modalities and using graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of age-related cognitive decline in middle age, decades before the onset of overt cognitive impairment

Modelling uncertainty in brain fibre orientation from diffusion-weighted magnetic resonance imaging.

Diffusion-weighted magnetic resonance imaging (DW-MRI) permits in-vivo measurements of water diffusion, from which we can infer the orientation of white matter fibres in the brain. We show that by ordering the measurements, we can improve the reproducibility of the fibre-orientation estimate from partially-completed DW-MRI scans, without altering the complete data set. Tractography methods reconstruct entire fibre pathways from the local fibre-orientation estimates. Because the local fibre-orientation measurements are subject to uncertainty, the reconstructed fibre pathways are best described with a probabilistic algorithm. One way to estimate the connection probabilities is by defining a probability density function (PDF) in each voxel, and sampling from the PDF in a Monte-Carlo fashion. We propose new models of the PDF based on standard spherical statistical methods. The models improve previous work by closely modelling the dispersion of repeated noisy estimates of the fibre orientation. We compare a simple PDF (the Watson PDF) that models circular cluster of axes to a more general PDF (the Bingham PDF) that models circular or elliptical clusters of axes. We also propose models of the PDF in regions of crossing fibres, where there are two distinct fibre populations in the voxel. We validate the PDFs by comparing them to the uncertainty in fibre orientation calculated from bootstrap resampling of a repeated brain MR acquisition. We find mat the Bingham PDF produces connection probabilities that are closer to the bootstrap results man the Watson PDF. We use the new PDF models to perform a connectivity-based segmentation of the corpus callosum in eight different subjects. The results are similar to those of previous studies on corpus callosum connectivity, despite the use of finer cortical labelling, suggesting that the dominant connections from the corpus callosum project to the superior frontal gyrus, the superior parietal gyrus and the occipital gyrus

Global brain connectivity analysis by diffusion MR tractography:algorithms, validation and applications

The human cerebral cortex consists of approximately 1010 neurons that are organized into a complex network of local circuits and long-range connections. During the past years there has been an increasing interest from the neuro-scientific community towards the study of this network, referred to as the human connectome. Due to its ability to probe the tissue microstructure in vivo and non invasively, diffusion MRI has revealed to be a helpful tool for the analysis of brain axonal pathways at the millimeter scale. Whereas the neuronal level remains unreachable, diffusion MRI enables the mapping of a low-resolution estimate of the human connectome, which should give a new breath to the study of normal or pathologic neuroanatomy. After a short introduction on diffusion MRI and tractography, the process by which fiber tracts are reconstructed from the diffusion images, we present a methodology allowing the creation of normalized whole-brain structural connection matrices derived from tractography and representing the human connectome. Based on the developed framework we then investigate the potential of front propagation algorithms in tractography. We compare their performance with classical tractography approaches on several well-known associative fiber pathways, and we discuss their advantages and limitations. Several solutions are proposed in order to evaluate and validate the connectome-related methodology. We develop a method to estimate the respective contributions of diffusion contrast versus other effects to a tractography result. Using this methodology, we show that whereas we can have a strong confidence in mid- and long-range connections, short-range connectivity has to be interpreted with care. Next, we demonstrate the strong relationship between the structural connectivity obtained from diffusion MR tractography and the functional connectivity measured with functional MRI. Then, we compare the performance of several diffusion MRI techniques through connectome-based measurements. We find that diffusion spectrum imaging is more sensitive and therefore enhances the results of tractography. Finally, we present two network-oriented applications. We use the human connectome to reveal the small-world architecture of the brain, a very efficient network topology in terms of wiring and power supply. We identify the cortical areas that belong to the core of structural connectivity. We show that these regions also belong to the default mode network, a set of dynamically coupled brain regions that are found to be more highly activated at rest. As a conclusion, we emphasize the potential of human connectome mapping for clinical applications and pathological studies

Reasoning with Uncertainty in Deep Learning for Safer Medical Image Computing

Deep learning is now ubiquitous in the research field of medical image computing. As such technologies progress towards clinical translation, the question of safety becomes critical. Once deployed, machine learning systems unavoidably face situations where the correct decision or prediction is ambiguous. However, the current methods disproportionately rely on deterministic algorithms, lacking a mechanism to represent and manipulate uncertainty. In safety-critical applications such as medical imaging, reasoning under uncertainty is crucial for developing a reliable decision making system. Probabilistic machine learning provides a natural framework to quantify the degree of uncertainty over different variables of interest, be it the prediction, the model parameters and structures, or the underlying data (images and labels). Probability distributions are used to represent all the uncertain unobserved quantities in a model and how they relate to the data, and probability theory is used as a language to compute and manipulate these distributions. In this thesis, we explore probabilistic modelling as a framework to integrate uncertainty information into deep learning models, and demonstrate its utility in various high-dimensional medical imaging applications. In the process, we make several fundamental enhancements to current methods. We categorise our contributions into three groups according to the types of uncertainties being modelled: (i) predictive; (ii) structural and (iii) human uncertainty. Firstly, we discuss the importance of quantifying predictive uncertainty and understanding its sources for developing a risk-averse and transparent medical image enhancement application. We demonstrate how a measure of predictive uncertainty can be used as a proxy for the predictive accuracy in the absence of ground-truths. Furthermore, assuming the structure of the model is flexible enough for the task, we introduce a way to decompose the predictive uncertainty into its orthogonal sources i.e. aleatoric and parameter uncertainty. We show the potential utility of such decoupling in providing a quantitative “explanations” into the model performance. Secondly, we introduce our recent attempts at learning model structures directly from data. One work proposes a method based on variational inference to learn a posterior distribution over connectivity structures within a neural network architecture for multi-task learning, and share some preliminary results in the MR-only radiotherapy planning application. Another work explores how the training algorithm of decision trees could be extended to grow the architecture of a neural network to adapt to the given availability of data and the complexity of the task. Lastly, we develop methods to model the “measurement noise” (e.g., biases and skill levels) of human annotators, and integrate this information into the learning process of the neural network classifier. In particular, we show that explicitly modelling the uncertainty involved in the annotation process not only leads to an improvement in robustness to label noise, but also yields useful insights into the patterns of errors that characterise individual experts

Magnetic Resonance Imaging of the Brain in Moving Subjects. Application of Fetal, Neonatal and Adult Brain Studies

Imaging in the presence of subject motion has been an ongoing challenge for magnetic resonance imaging (MRI). Motion makes MRI data inconsistent, causing artifacts in conventional anatomical imaging as well as invalidating diffusion tensor imaging (DTI) reconstruction. In this thesis some of the important issues regarding the acquisition and reconstruction of anatomical and DTI imaging of moving subjects are addressed; methods to achieve high resolution and high signalto- noise ratio (SNR) volume data are proposed. An approach has been developed that uses multiple overlapped dynamic single shot slice by slice imaging combined with retrospective alignment and data fusion to produce self consistent 3D volume images under subject motion. We term this method as snapshot MRI with volume reconstruction or SVR. The SVR method has been performed successfully for brain studies on subjects that cannot stay still, and in some cases were moving substantially during scanning. For example, awake neonates, deliberately moved adults and, especially, on fetuses, for which no conventional high resolution 3D method is currently available. Fine structure of the in-utero fetal brain is clearly revealed for the first time with substantially improved SNR. The SVR method has been extended to correct motion artifacts from conventional multi-slice sequences when the subject drifts in position during data acquisition. Besides anatomical imaging, the SVR method has also been further extended to DTI reconstruction when there is subject motion. This has been validated successfully from an adult who was deliberately moving and then applied to inutero fetal brain imaging, which no conventional high resolution 3D method is currently available. Excellent fetal brain 3D apparent diffusion coefficient (ADC) maps in high resolution have been achieved for the first time as well as promising fractional Anisotropy (FA) maps. Pilot clinical studies using SVR reconstructed data to study fetal brain development in-utero have been performed. Growth curves for the normally developing fetal brain have been devised by the quantification of cerebral and cerebellar volumes as well as some one dimensional measurements. A Verhulst model is proposed to describe these growth curves, and this approach has achieved a correlation over 0.99 between the fitted model and actual data