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The human brain with all its faculties and intricacies has fascinated many generations of researchers [1] and will likely be the final frontier of science. Understanding the principles underlying the brain's higher-order cognitive functions is indeed a major challenge and will profoundly impact our views on what defines a human being. On a more down-to-earth level, knowledge of the structure, function,...
Morphology of bones, teeth, and some particular structures are widely used for categorizing species and studying their evolution. In this paper, we used groupwise registration to provide a representative image from the set of the image samples that represents its typical morphology. We also provided perturbation map which indicates the deviation of each point through the ensemble. These images support...
Progress in our understanding of brain functions relies on our capability to explore the human cortical surface at a fine scale (typically 1.5 mm isotropic at 3T). For this purpose, high accuracy is required for all processing steps from image acquisition to data analysis. For group studies, the high intersubject variability of the human cortices hampers their precise registration. Based on the hypothesis...
Detecting cancerous lesion is a major clinical application in emission tomography. In a previous work, we have shown that penalized maximum likelihood image reconstruction can improve lesion detection at a fixed location by designing a shift-invariant quadratic penalty function. Here we extend this work to detection of tumors at unknown positions. We present a method to design a shift-variant quadratic...
Fiber tracking techniques are essential for representing and visualizing the cardiac fiber architecture information encoded in diffusion-tensor imaging (DTI) data. We propose a neighborhood-based probabilistic fiber tracking method for cardiac DTI which accounts for spatial correlation and data uncertainty. The method consists in tracking fiber paths by sampling step directions from a normalized weighted...
In this study, we propose a methodology to estimate 3D+time maps of left ventricular fibre strain from human structural and dynamic MRI data. A finite element model integrates fibre principal direction throughout the left ventricle from an ex vivo human diffusion tensor MRI acquisition and motion from tagged MRI. This combination enables the estimation of fibre strain and its variation throughout...
In this paper, we explore the theory of tensor invariants as a mathematical framework for computing new biomarkers for HARDI. We present and explain the integrity basis, basic invariants and principal invariants of 2nd & 4th order tensors to expand on a recently proposed paper on 4th order tensor invariants. We present the mathematical results and compute the basic and principal invariants on...
Advances in fast 2D MRI have led to its growing clinical use in un-sedated fetal brain studies, as a tool for challenging neurodevelopmental cases. The availability of this 2D data has motivated new engineering developments that combine fast multi-slice MRI scans with computer vision techniques to provide a route to full 3D fetal brain image formation in a significant fraction of imaging studies....
Extracting and labeling sulcal curves on the human cerebral cortex is important for many neuroscience studies, however manually annotating the sulcal curves is a time-consuming task. In this paper, we present an automatic sulcal curve extraction method by registering a set of dense landmark points representing the sulcal curves to the subject cortical surface. A Markov random field is used to model...
Insight into brain development and organization can be gained by computing correlations between structural and functional measures in parcellated cortex. Partial correlations can often reduce ambiguity in correlation data by identifying those pairs of regions whose similarity cannot be explained by the influence of other regions with which they may both interact. Consequently a graph with edges indicating...
Accurate 3D models of the human brain vessels can greatly help to diagnose serious diseases. Such models can be constructed by segmentation of 3D MRA images, especially the recently introduced high resolution 7T MRA. We propose a new two-step approach for fully automatic segmentation of 7T MRA images of the human cerebrovascular system. First, a 3D model-based approach is applied to segment thick...
Accurate inter-subject registration of magnetic resonance (MR) images of the human brain is required to allow meaningful comparisons across groups of subjects. Some anatomical structures can be very difficult to match and this can result in intensity based registration approaches inferring complex and implausible mappings in some regions. In this work, we propose a generic probabilistic framework...
Human brain connectivity is disrupted in a wide range of disorders — from Alzheimer's disease to autism — but little is known about which specific genes affect it. Here we conducted a genome-wide association for connectivity matrices that capture information on the density of fiber connections between 70 brain regions. We scanned a large twin cohort (N=366) with 4-Tesla high angular resolution diffusion...
We present a shape-space approach for analyzing genetic influences on the shapes of the sulcal folding patterns on the cortex. Sulci are represented as continuously parameterized functions in a shape space, and shape differences between sulci are obtained via geodesics between them. The resulting statistical shape analysis framework is used not only to construct populations averages, but also used...
Eggs from the small, intestinal pig whipworm Trichuris suis constitute the active pharmaceutical ingredient in a novel type of medicine for chronic autoimmune diseases like Crohn's disease. The pharmaceutical potency of such an egg suspension can be assessed by microscopic inspection, as only eggs containing a viable, infective larva provoke the wanted immune reaction. Thus, a precise and objective...
We have developed techniques to automatically generate personalised biomechanical models of patients' hearts based on 3D cardiac images. We demonstrate this approach using multi-slice computed tomography images. Unsupervised segmentation was performed using non-rigid image registration with a segmented image. A finite element model was automatically fitted to the segmented data of the left ventricle...
We propose a fully automatic method to segment the dentate nucleus using diffusion weighted images (DWI). Initialization of the dentate nucleus is produced by combining the information from tractography results on the diffusion tensor images (reconstructed from DWI) and b0 images. A geometric de-formable model (GDM) with generalized gradient vector flow (GGVF) is then applied on the b0 image to generate...
Regularized image reconstruction methods are of increasing interest for X-ray CT imaging, but are hampered by the long computation times of iterative algorithms. We recently developed a variable splitting-based alternating direction method of multipliers (ADMM) that provides superior convergence speeds for statistical X-ray CT reconstruction compared to conventional methods. ADMM however demands storing...
Using a data-driven analysis of diffusion MRI called connectivity based cortical parcellation, we were able to segment the human inferior parietal lobule into spatially contiguous regions that closely match the human cytoarchitectonic atlas. This supports the cortical field hypothesis in that connectivity profiles appear to define the same field boundaries as histo-logic features. Here we present...
Diffusion-weighted images are increasingly being employed in conjunction with T2-weighted images for accurate diagnosis of cervical cancer. However, these images suffer from eddy-current related and magnetic field inhomogeneity induced distortions. This paper presents an investigation into the reverse gradient technique to correct distortions in diffusion-weighted echo-planar images of human cervix...
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