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Bipolar disorder is characterized by extreme mood swings, including both manic and depressive episodes commonly accompanied by psychosis. Many imaging studies have investigated white matter changes in bipolar illness, and the results have suggested abnormal intra- and inter-hemispheric white matter structures, particularly in the fronto-limbic and callosal systems. However, some inconsistency remains...
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 paper we present a novel approach to extracting fibre orientation information from the diffusion signal. We start the process by expanding the diffusion signal using spherical harmonics (SH) basis. In theory, this could lead to a mapping between SH coefficients and fibre orientations. In practice, finding such a mapping is non-trivial. Instead, we simulate the diffusion signal for a limited...
Diffusion Tensor Imaging (DTI) has received increasing attention in the neuroimaging community. However, the complex Diffusion Weighted Images (DWI) acquisition protocol are prone to artifacts induced by motion and low signal-to-noise rations(SNRs). A rigorous quality control (QC) and error correction procedure is absolutely necessary for DTI data analysis. Most existing QC procedures are conducted...
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...
This paper presents a paradigm for population-based studies using geodesic connectivity maps computed from DTI (Diffusion Tensor Imaging) data. Geodesic connectivity maps provide a measure of connectivity characterization between different regions of the brain using a tensor-based measure that combines anisotropy and orientation information. A connectivity map is constructed relative to a given region...
Many biomedical applications require the enhancement of coherent flow-like curvilinear structures in images. This can be accomplished in a natural way by adopting anisotropic diffusion filtering to local texture analysis by means of the structure tensor. Here we propose a contrast independent anisotropic diffusion filtering of curvilinear structures based on a novel concept: the Phase Congruency Tensor...
Diffusion tensor imaging (DTI) has been widely used for nondestructive characterization of microstructures of myocardium or brain connectivity. It requires repeated acquisition with different diffusion gradients. The long acquisition time greatly limits the clinical application of DTI. In this paper, a novel method, named model-based method with joint sparsity constraint (MB-JSC), effectively incorporates...
Fiber tracking from diffusion tensor images is an essential step in numerous clinical applications. There is a growing demand for an accurate and efficient framework to perform quantitative analysis of white matter fiber bundles. In this paper, we propose a robust framework for fiber clustering. This framework is composed of two parts: accessible fiber representation, and a statistically robust divergence...
The unscented Kalman filter (UKF) was recently introduced in literature for simultaneous multi-tensor estimation and tractography. This UKF however was not intrinsic to the space of diffusion tensors. Lack of this key property leads to inaccuracies in the multi-tensor estimation as well as in tractography. In this paper, we propose an novel intrinsic unscented Kalman filter (IUKF) in the space of...
We propose a biquaternion formalism to model diffusion tensor magnetic resonance imaging (DT-MRI) data. Unlike previous methods that use dimensionality reduction, we are able to process the full tensor in a holistic manner while respecting the underlying manifold of the data. Using this approach, we introduce the Fourier transform and convolution for DT-MRI for the first time, which can be applied...
Robust estimation of diffusion models in presence of local artefacts that corrupt only a subset of gradient directions is essential in diffusion weighted imaging to accurately assess the brain connectivity and white-matter characteristics. In this work we investigate the estimation of diffusion tensors in the Random Sample Consensus (RANSAC) paradigm. First, we show that it enables robust estimation...
In this work, we present an automatic branch and stenoses detection method that is capable of detecting all types of plaques in Computed Tomography Angiography (CTA) modality. Our method is based on the vessel extraction algorithm we proposed in [1], and detects branches and stenoses in a very fast way. We demonstrate the performance of our branch detection method on 3 complex tubular structured synthetic...
We present a diffeomorphic diffusion tensor image (DTI) registration technique with multi-contrast images extracted from DTI and conventional structural MRI data. DTI provides microstructure information in white matter. However due to the acquisition protocols used in many clinical studies, DTI has lower SNR and spatial resolution compared to structural MRI. Complementary information can be used to...
The morphological analysis of axonal trees is an important problem in neuroscience. The first step for such an analysis is the extraction of the axon. Due to the high volume of generated image data and the tortuous nature of the axons, manual processing is not feasible. Therefore, it is necessary to develop techniques for the automatic extraction of the neuronal structures. In this paper we present...
Multi-fiber models have been introduced to leverage the accuracy of the diffusion representation in crossing fiber areas. The improved accuracy may, however, be impaired by poor processing of the multi-fiber models. In particular, interpolating multi-fiber models proves challenging, while it is a pervasive and recurrent task in many processes. The error accumulated from iterating a poor interpolation...
The Funk-Radon Transform allows to approximate the central section integral of the diffusion signal to compute probabilistic Orientation Distribution Functions (ODF) of fiber populations, provided the behavior for the b-values not acquired is known. To relax the latter demand, it has been proposed to compute instead the integral inside the disk defined in the central section by the b-value acquired...
Diffusion MRI is a tool of choice for the analysis of the brain white matter fiber pathways. When translated to clinics, the short acquisition time leads to low angular resolution diffusion (LARD) images. Fiber pathways are then inferred assuming Gaussian diffusion (a.k.a. DTI) that provides one fiber orientation per voxel. In the past decade, recent researches highlight more intricate intra-voxel...
Over decades, segmentation has remained a salient task in most medical imaging applications confronting multi-faced challenges including limited image quality. In this paper, we present a new anisotropic region growing segmentation approach for vascular or other elongated structures. A fundamental challenge during tracing vascular structures is broken continuity of structures by noise and other imaging...
This paper presents a computational approach for detection of filamentous networks in 3D tomographic electron microscopy. Due to the general heterogeneity of chemical staining, imaged signatures may appear punctate and discontinuous. Very often, there is a necessity to characterize organization and phenotypic signatures of stained structures. This is the example of polysaccharides in the plant cell...
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