Search

PurposeTo optimize a diffusion-prepared balanced steady-state free precession cardiac MRI (CMR) technique to perform diffusion-tensor CMR (DT-CMR) in humans on a 3 Tesla clinical scanner METHODS: A previously developed second order motion compensated (M2) diffusion-preparation scheme was significantly shortened (40%) yielding sufficient signal-to-noise ratio for DT-CMR imaging. In 20 healthy volunteers and 3 heart failure (HF) patients, DT-CMR was performed comparing no motion compensation (M0), first order motion compensation (M1), and the optimized M2. Mean diffusivity (MD), fractional anisotropy (FA), helix angle (HA), and HA transmural slope (HATS) were calculated. Reproducibility and success rate (SR) were investigated.ResultsM2-derived left ventricular (LV) MD, FA, and HATS (1.4 ± 0.2 μm2 /ms, 0.28 ± 0.06, -1.0 ± 0.2 °/%trans) were significantly (P  0.85) and SR (82%) than M1 (ICC = 0.20-0.85; SR = 37%) and M0 (ICC = 0.20-0.30; SR = 11%). M2 DT-CMR was able to yield HA maps with smooth transmural transition from endocardium to epicardium.ConclusionThe proposed M2 DT-CMR reproducibly yielded bulk motion robust estimations of mean LV MD, FA, HA, and HATS on a 3T clinical scanner. Magn Reson Med 76:1354-1363, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Diffusion tensor magnetic resonance imaging (DT-MRI) is a technique used to quantify the microstructural organization of biological tissues. Multiple images are necessary to reconstruct the tensor data and each acquisition is subject to complex thermal noise. As such, measures of tensor invariants, which characterize components of tensor shape, derived from the tensor data will be biased from their true values. Previous work has examined this bias, but over a narrow range of tensor shape. Herein, we define the mathematics for constructing a tensor from tensor invariants, which permits an intuitive and principled means for building tensors with a complete range of tensor shape and salient microstructural properties. Thereafter, we use this development to evaluate by simulation the effects of noise on characterizing tensor shape over the complete space of tensor shape for three encoding schemes with different SNR and gradient directions. We also define a new framework for determining the distribution of the true values of tensor invariants given their measures, which provides guidance about the confidence the observer should have in the measures. Finally, we present the statistics of tensor invariant estimates over the complete space of tensor shape to demonstrate how the noise sensitivity of tensor invariants varies across the space of tensor shape as well as how the imaging protocol impacts measures of tensor invariants.

We consider the inverse problem of denoising an image where each point (pixel) is an element of a target set, which we refer to as a target-valued image. The target sets considered are either (ⅰ) a closed convex set of Euclidean space or (ⅱ) a closed subset of the sphere. The energy for the denoising problem consists of an L2-fidelity term which is regularized by the Dirichlet energy. A relaxation of this energy, based on the heat kernel, is introduced and the associated minimization problem is proven to be well-posed. We introduce a diffusion generated method which can be used to efficiently find minimizers of this energy. We prove results for the stability and convergence of the method for both types of target sets. The method is demonstrated on a variety of synthetic and test problems, with associated target sets given by the semi-positive definite matrices, the cube, spheres, the orthogonal matrices, and the real projective line. [ABSTRACT FROM AUTHOR]

The highly inhomogeneous distribution of collagen fibrils may have important effects on the biphasic mechanics of articular cartilage. However, the effect of the inhomogeneity of collagen fibrils has mainly been investigated using simplified three-layered models, which may have underestimated the effect of collagen fibrils by neglecting their realistic orientation. The aim of this study was to investigate the effect of the realistic orientation of collagen fibrils on the biphasic mechanics of articular cartilage. Five biphasic material models, each of which included a different level of complexity of fibril reinforcement, were solved using two different finite element software packages (Abaqus and FEBio). Model 1 considered the realistic orientation of fibrils, which was derived from diffusion tensor magnetic resonance images. The simplified three-layered orientation was used for Model 2. Models 3–5 were three control models. The realistic collagen orientations obtained in this study were consistent with the literature. Results from the two finite element implementations were in agreement for each of the conditions modelled. The comparison between the control models confirmed some functions of collagen fibrils. The comparison between Models 1 and 2 showed that the widely-used three-layered inhomogeneous model can produce similar fluid load support to the model including the realistic fibril orientation; however, an accurate prediction of the other mechanical parameters requires the inclusion of the realistic orientation of collagen fibrils. [ABSTRACT FROM AUTHOR]

Musculoskeletal (dys-)function relies for a large part on muscle architecture which can be obtained using Diffusion-Tensor MRI (DT-MRI) and fiber tractography. However, reconstructed tracts often continue along the tendon or aponeurosis when using conventional methods, thus overestimating fascicle lengths. In this study, we propose a new method for semiautomatic segmentation of tendinous tissue using tract density (TD). We investigated the feasibility and repeatability of this method to quantify the mean fascicle length per muscle. Additionally, we examined whether the method facilitates measuring changes in fascicle length of lower leg muscles with different foot positions. Five healthy subjects underwent two DT-MRI scans of the right lower leg, with the foot in 15° dorsiflexion, neutral, and 30° plantarflexion positions. Repeatability of fascicle length measurements was assessed using Bland-Altman analysis. Changes in fascicle lengths between the foot positions were tested using a repeated multivariate analysis of variance (MANOVA). Bland-Altman analysis showed good agreement between repeated measurements. The coefficients of variation in neutral position were 8.3, 16.7, 11.2, and 10.4% for soleus (SOL), fibularis longus (FL), extensor digitorum longus (EDL), and tibialis anterior (TA), respectively. The plantarflexors (SOL and FL) showed significant increase in fascicle length from plantarflexion to dorsiflexion, whereas the dorsiflexors (EDL and TA) exhibited a significant decrease. The use of a tract density for semiautomatic segmentation of tendinous structures provides more accurate estimates of the mean fascicle length than traditional fiber tractography methods. The method shows moderate to good repeatability and allows for quantification of changes in fascicle lengths due to passive stretch. [ABSTRACT FROM AUTHOR]

We are interested in restoring images having values in a symmetric Hadamard manifold by minimizing a functional with a quadratic data term and a total variation-like regularizing term. To solve the convex minimization problem, we extend the Douglas-Rachford algorithm and its parallel version to symmetric Hadamard manifolds. The core of the Douglas-Rachford algorithm is reections of the functions involved in the functional to be minimized. In the Euclidean setting the reections of convex lower semicontinuous functions are nonexpansive. As a consequence, convergence results for Krasnoselski-Mann iterations imply the convergence of the Douglas-Rachford algorithm. Unfortunately, these general results do not carry over to Hadamard manifolds, where proper convex lower semicontinuous functions can have expansive reections. However, splitting our restoration functional in an appropriate way, we have only to deal with special functions--namely, several distance-like functions and an indicator function of a special convex set. We prove that the reections of certain distance-like functions on Hadamard manifolds are nonexpansive, which is an interesting result on its own. Furthermore, the reection of the involved indicator function is nonexpansive on Hadamard manifolds with constant curvature so that the Douglas-Rachford algorithm converges here. Several numerical examples demonstrate the advantageous performance of the suggested algorithm compared to other existing methods such as the cyclic proximal point algorithm and half-quadratic minimization. Numerical convergence is also observed in our experiments on the Hadamard manifold of symmetric positive definite matrices with the affine invariant metric, which does not have a constant curvature. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) acutely damages the brain; this injury can evolve into chronic neurodegeneration. While much is known about the chronic effects arising from multiple mild TBIs, far less is known about the long-term effects of a single moderate to severe TBI. We found that a single moderate closed head injury to mice induces diffuse axonal injury within 1-day post-injury (DPI). At 14 DPI, injured animals have atrophy of ipsilesional cortex, thalamus, and corpus callosum, with bilateral atrophy of the dorsal fornix. Atrophy of the ipsilesional corpus callosum is accompanied by decreased fractional anisotropy and increased mean and radial diffusivity that remains unchanged between 14 and 180 DPI. Injured animals show an increased density of phospho-tau immunoreactive (pTau+) cells in the ipsilesional cortex and thalamus, and bilaterally in corpus callosum. Between 14 and 180 DPI, atrophy occurs in the ipsilesional ventral fornix, contralesional corpus callosum, and bilateral internal capsule. Diffusion tensor MRI parameters remain unchanged in white matter regions with delayed atrophy. Between 14 and 180 DPI, pTau+ cell density increases bilaterally in corpus callosum, but decreases in cortex and thalamus. The location of pTau+ cells within the ipsilesional corpus callosum changes between 14 and 180 DPI; density of all cells increases including pTau+ or pTau− cells. >90% of the pTau+ cells are in the oligodendrocyte lineage in both gray and white matter. Density of thioflavin-S+ cells in thalamus increases by 180 DPI. These data suggest a single closed head impact produces multiple forms of chronic neurodegeneration. Gray and white matter regions proximal to the impact site undergo early atrophy. More distal white matter regions undergo chronic, progressive white matter atrophy with an increasing density of oligodendrocytes containing pTau. These data suggest a complex chronic neurodegenerative process arising from a single moderate closed head injury. • Gray and white matter atrophy begins within 14 days of a single closed head injury. • White matter atrophy progresses between 14 and 180 days post-injury, with minimal changes in diffusion tensor MRI parameters. • CHI increases the density of oligodendrocytes with perinuclear accumulation of phosphorylated tau. • Thioflavin-S+ cells increase in thalamus at 180 days post-injury. [ABSTRACT FROM AUTHOR]

We introduce a new nonsmooth variational model for the restoration of manifold-valued data which includes second order differences in the regularization term. While such models were successfully applied for real-valued images, we introduce the second order difference and the corresponding variational models for manifold data, which up to now only existed for cyclic data. The approach requires a combination of techniques from numerical analysis, convex optimization, and differential geometry. First, we establish a suitable definition of absolute second order differences for signals and images with values in a manifold. Employing this definition, we introduce a variational denoising model based on first and second order differences in the manifold setup. In order to minimize the corresponding functional, we develop an algorithm using an inexact cyclic proximal point algorithm. We propose an efficient strategy for the computation of the corresponding proximal mappings in symmetric spaces utilizing the machinery of Jacobi fields. For the n-sphere and the manifold of symmetric positive definite matrices, we demonstrate the performance of our algorithm in practice. We prove the convergence of the proposed exact and inexact variant of the cyclic proximal point algorithm in Hadamard spaces. These results which are of interest on its own include, e.g., the manifold of symmetric positive definite matrices. [ABSTRACT FROM AUTHOR]

White matter (WM) abnormalities have been reported in bipolar disorder (BD) patients, as well as in their non-BD relatives, both children and adults. Although it is considered an emerging vulnerability marker for BD, there are no studies investigating WM alterations in pediatric unmedicated patients and young healthy offspring. In this study, we evaluated the presence of WM alterations in 18 pediatric, non medicated BD patients, as well as in 18 healthy offspring of BD type I parents and 20 healthy controls. 3T DT-MRI data were acquired and scans were processed with tract-based spatial statistics to provide measures of fractional anisotropy and diffusivity. We found no significant differences in WM microstructure between BD patients, healthy offspring and healthy controls. Previous studies that reported WM alterations investigated older subjects, either on medication (BD patients) or with psychiatric diagnoses other than BD (unaffected offspring). Our findings highlight the importance of the understanding of disease ontogeny and brain development dynamics in the search for early vulnerability markers for psychiatric disorders. [ABSTRACT FROM AUTHOR]

Objectives. Maternal prenatal stress is associated with elevated risk of adverse behavioural outcomes in offspring. This association may involve developmental disruption to limbic-prefrontal white matter circuitry, of which the uncinate fasciculus is the major tract. One potential candidate for modulating brain development is maternal prenatal stress. We provide the first prospective study of prenatal stress and white matter microstructure in children. Methods. A total of 22 healthy children (mean age 7 years) of mothers recruited in pregnancy underwent diffusion tensor magnetic resonance imaging. We examined correlations between prenatal stressful life events and white matter microstructural organisation indices (fractional anisotropy (FA) and perpendicular diffusivity (Dperp)) of the uncinate fasciculus and a 'control' tract. Results. Maternal prenatal stressful life events were correlated positively with right uncinate fasciculus FA, and negatively with right uncinate fasciculus Dperp in their child, with a similar trend with left uncinate fasciculus Dperp. Prenatal stress was not associated with control tract properties; sociodemographic/obstetric variables were not associated with FA/Dperp of either tract. Conclusions. Variation in maternal prenatal stress may be associated with differences in the development of white matter within brain networks underlying child social behaviour. [ABSTRACT FROM AUTHOR]

Abstract: General fluid-type intelligence (gF) reflects abstract reasoning and problem solving abilities, and is an important predictor for lifetime trajectories of cognition, and physical and mental health. Structural and functional neuroimaging studies have demonstrated the role of parieto-frontal gray matter, but the white matter (WM) underpinnings of gF and the contribution of individual gF components to gF–WM relationship still need to be explored. The aim of this study was to characterize, in a sample of 100 healthy middle-aged and old subjects (mean=63.8years), the relationship between gF and indices of WM structure obtained from diffusion tensor magnetic resonance imaging (DT-MRI) (fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD)). gF was estimated by principal component analysis including measures of episodic memory, reasoning, and processing speed. Tract-based spatial statistics and permutation-based inference statistics were used to test the association between gF and WM indices, while controlling for the effect of age and sex. We hypothesized a positive relationship between gF and WM structure. Based on previous studies, we further hypothesized that this relationship was heavily influenced by the processing speed component of gF. We found a robust relationship between gF and DT-MRI measures of FA, RD and MD in all major WM tracts. Higher gF score was related to higher degree of WM integrity, in middle-aged as well as old individuals. Thus, the distributed relationship between gF and indices of WM microstructure is consistent with the notion that gF reflects efficient signaling between cortical areas. Furthermore, analysis of relationships between WM measures and gF components revealed an association with information processing speed and reasoning ability, but not with episodic memory. Thus, although all subcomponents loaded high on gF factor, the speed-related components were most strongly associated with DT-MRI-derived measures. These results suggest that DT-MRI can be used to parse gF. [Copyright &y& Elsevier]

ABSTRACT A thorough review of the q-space technique is presented starting from a discussion of Fick's laws. The work presented here is primarily conceptual, theoretical, and hopefully pedagogical. We offered the notion of molecular concentration to unify Fick's laws and diffusion MRI within a coherent conceptual framework. The fundamental relationship between diffusion MRI and Fick's laws is carefully established. The conceptual and theoretical basis of the q-space technique is investigated from first principles. © 2013 Wiley Periodicals, Inc. Concepts Magn Reson Part A 42A: 116-129, 2013. [ABSTRACT FROM AUTHOR]

Purpose: Modern imaging such as diffusion tensor magnetic resonance imaging (DT-MRI) is consists of matrix valued data. The processing for DT-MRI tractography is a method to determine the architecture of axonal fibers in the central nervous system by computing the direction of the principal eigenvectors that obtained from tensor matrix. This tensor matrix is usually sensitive to noise. Hence adequate image processing methods for regularization are in demand. Our goal of this paper is extend the morphological operation to the matrix-valued data and applies it to noise reduction. Methods: We proposed three Mathematical Morphology methods for regularization and compared to matrix medians which are the Simple Median Method. Results The results of the Mathematical Morphology Method are faster and the performance is similar to those of the Simple Median. Conclusions: Mathematical Morphology methods are effective algorithm for the tensor regularization. [ABSTRACT FROM AUTHOR]

This study investigates the differential contribution of gray matter (GM) atrophy and deafferentation through white matter (WM) damage in the clinical progression of Alzheimer's disease (AD). Thirty-one patients with probable AD, 23 with amnestic mild cognitive impairment (a-MCI), and 14 healthy subjects underwent MRI scanning at 3T. Voxel-based morphometry was used to assess regional GM atrophy in AD and a-MCI patients. Diffusion tensor-MRI tractography was used to reconstruct the cingulum bilaterally, and to quantify, voxel-by-voxel, its fractional anisotropy (FA) and mean diffusivity (MD) (measures of microscopic WM integrity). Atrophy of the cinguli was also assessed by means of jacobian determinants (JD) of local transformations. In AD patients, four clusters of reduced GM were found nearby the cinguli, in the posterior (PCC) and anterior cingulate cortex, and in the hippocampal/parahippocampal areas. Widespread areas of reduced FA and increased MD were found in the cinguli of both, AD and a-MCI patients. A region of macroscopic atrophy was detectable in AD patients only. Strong associations were found between local GM densities in the four identified clusters, and measures of micro- and (to a lesser extent) macroscopic damage of patients' cinguli. Linear regression analyses revealed that MD in the cinguli predicts patients' measures of episodic memory in combination with GM density of hippocampal/parahippocampal areas, and measures of global cognition in combination with GM density of the PCC. This study indicates that brain deafferentation though the cingulum is likely to play a remarkable role in progressive development of cognitive impairment in AD. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Abstract: This study proposes an advanced finite element (FE) head modeling technique through which high-resolution FE meshes adaptive to the degree of tissue anisotropy can be generated. Our adaptive meshing scheme (called wMesh) uses MRI structural information and fractional anisotropy maps derived from diffusion tensors in the FE mesh generation process, optimally reflecting electrical properties of the human brain. We examined the characteristics of the wMeshes through various qualitative and quantitative comparisons to the conventional FE regular-sized meshes that are non-adaptive to the degree of white matter anisotropy. We investigated numerical differences in the FE forward solutions that include the electrical potential and current density generated by current sources in the brain. The quantitative difference was calculated by two statistical measures of relative difference measure (RDM) and magnification factor (MAG). The results show that the wMeshes are adaptive to the anisotropic density of the WM anisotropy, and they better reflect the density and directionality of tissue conductivity anisotropy. Our comparison results between various anisotropic regular mesh and wMesh models show that there are substantial differences in the EEG forward solutions in the brain (up to RDM=0.48 and MAG=0.63 in the electrical potential, and RDM=0.65 and MAG=0.52 in the current density). Our analysis results indicate that the wMeshes produce different forward solutions that are different from the conventional regular meshes. We present some results that the wMesh head modeling approach enhances the sensitivity and accuracy of the FE solutions at the interfaces or in the regions where the anisotropic conductivities change sharply or their directional changes are complex. The fully automatic wMesh generation technique should be useful for modeling an individual-specific and high-resolution anisotropic FE head model incorporating realistic anisotropic conductivity distributions towards more accurate analysis of bioelectromagnetic problems. [Copyright &y& Elsevier]

In order to layer ventricular tissue accurately, a linear layered fill method is proposed in this paper. This method is based on the high-resolution DT-MRI imaging data of human heart and the linear relationship between the rotation angle of the muscle fibers and ventricular cell types, and it has been used to sort the ventricular cells and reconstruct the 3D structure of ventricular tissue for further simulation of heart electrophysiological activities. The pseudo ECG simulation based on the Ten Tusscher model of single ventricular cell and this 3D ventricular structure is used to test the validity of the layering method. The simulation results show that the pseudo-ECG is similar to the real ECG in morphology, the Q-T interval and QRS-wave direction. Therefore the proposed approach for the layering of ventricular tissue is valid, and can be applied to the researches on the modeling and simulation of cardiac physiology and pathology related issues. [ABSTRACT FROM AUTHOR]

In this article we consider adaptive, PDE-driven morphological operations for 3D matrix fields arising e.g. in diffusion tensor magnetic resonance imaging (DT-MRI). The anisotropic evolution is steered by a matrix constructed from a structure tensor for matrix valued data. An important novelty is an intrinsically one-dimensional directional variant of the matrix-valued upwind schemes such as the Rouy-Tourin scheme. It enables our method to complete or enhance anisotropic structures effectively. A special advantage of our approach is that upwind schemes are utilised only in their basic one-dimensional version, hence avoiding grid effects and leading to an accurate algorithm. No higher dimensional variants of the schemes themselves are required. Experiments with synthetic and real-world data substantiate the gap-closing and line-completing properties of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce a new approach for tensor field segmentation based on the definition of mixtures of Gaussians on tensors as a statistical model. Working over the well-known Geodesic Active Regions segmentation framework, this scheme presents several interesting advantages. First, it yields a more flexible model than the use of a single Gaussian distribution, which enables the method to better adapt to the complexity of the data. Second, it can work directly on tensor-valued images or, through a parallel scheme that processes independently the intensity and the local structure tensor, on scalar textured images. Two different applications have been considered to show the suitability of the proposed method for medical imaging segmentation. First, we address DT-MRI segmentation on a dataset of 32 volumes, showing a successful segmentation of the corpus callosum and favourable comparisons with related approaches in the literature. Second, the segmentation of bones from hand radiographs is studied, and a complete automatic–semiautomatic approach has been developed that makes use of anatomical prior knowledge to produce accurate segmentation results. [Copyright &y& Elsevier]

Background. Subjects at 'ultra high risk' (UHR) for developing psychosis have differences in white matter (WM) compared with healthy controls. WM integrity has not yet been investigated in UHR subjects in relation to the development of subsequent psychosis. Hence, we investigated a prospective cohort of UHR subjects comparing whole brain fractional anisotropy (FA) of those later developing psychosis (UHR-P) to those who did not (UHR-NP). Method. We recruited 37 subjects fulfilling UHR criteria and 10 healthy controls. Baseline 3 Tesla magnetic resonance imaging (MRI) scans and Positive and Negative Syndrome Scale (PANSS) ratings were obtained. UHR subjects were assessed at 9, 18 and 24 months for development of frank psychosis. We compared baseline FA of UHR-P to controls and UHR-NP subjects. Furthermore, we related clinical data to MRI outcome in the patient population. Results. Of the 37 UHR subjects, 10 had transition to psychosis. UHR-P subjects showed significantly lower FA values than control subjects in medial frontal lobes bilaterally. UHR-P subjects had lower FA values than UHR-NP subjects, lateral to the right putamen and in the left superior temporal lobe. UHR-P subjects showed higher FA values, compared with UHR-NP, in the left medial temporal lobe. In UHR-P, positive PANSS negatively correlated to FA in the left middle temporal lobe. In the total UHR group positive PANSS negatively correlated to FA in the right superior temporal lobe. Conclusions. UHR subjects who later develop psychosis have differences in WM integrity, compared with UHR subjects who do not develop psychosis and to healthy controls, in brain areas associated with schizophrenia. [ABSTRACT FROM AUTHOR]

Purpose: Brain tissue segmentation based on diffusion tensor magnetic resonance imaging (DT-MRI) data has been attempted by previous researchers. Due to inherent low spatial resolution of DT-MRI data, conventional methods suffered from partial volume averaging among the different types of tissues, which may result in inaccurate segmentation results. The purpose was to develop a new brain tissue segmentation method for DT-MRI data in which effect of the partial volume averaging is taken into account. Methods: The method estimates the partial volume fractions of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) within each voxel using a maximum a posteriori probability principle, based on five DT parameters (three eigenvalues, apparent diffusion coefficient, and fractional anisotropy). The authors evaluated the performance of the proposed method quantitatively by using digital phantom data. Moreover, the authors applied the method to real DT-MRI data of the human brain, and compared the results with those of a conventional segmentation method. Results: In the digital phantom experiments, the root mean square error in term of partial volume fraction with the method for WM, GM, and CSF were 0.137, 0.049, and 0.085, respectively. The volume overlap measures between the segmentation results and the ground truth of the digital phantom were more than 0.9 in all three tissue types, while those between the results by the conventional method and the ground truth ranged between 0.550 and 0.854. In visual comparisons for real DT-MRI, WM/GM/CSF regions estimated by the method were more similar to the corresponding regions depicted in the structural image than those estimated by the conventional method. Conclusions: The results of the digital phantom experiment and real DT-MRI data demonstrated that the method improved accuracy in estimation and segmentation of brain tissue on DT-MRI data over the conventional method. This method may be useful in evaluating the cortical and subcortical diffusivity in neurological diseases. [ABSTRACT FROM AUTHOR]

Abstract: Objective: The goal of this study was to experimentally investigate the influence of the anisotropy of white matter (WM) conductivity on EEG source localization. Methods: Visual evoked potentials (VEP) and fMRI data were recorded from three human subjects presented with identical visual stimuli. A finite element method was used to solve the EEG forward problems based on both anisotropic and isotropic head models, and single-dipole source localization was subsequently performed to localize the source underlying the N75 VEP component. Results: The averaged distances of the localized N75 dipole locations in V1 between the isotropic and anisotropic head models ranged from 0 to 6.22±2.83mm. The distances between the localized dipole positions and the centers of the fMRI V1 activations were slightly smaller when using an anisotropic model (7.49±1.35–15.70±8.60mm) than when using an isotropic model (7.65±1.30–15.31±9.18mm). Conclusions: Anisotropic models incorporating realistic WM anisotropic conductivity distributions do not substantially improve the accuracy of EEG dipole localization in the primary visual cortex using experimental data obtained using visual stimulation. Significance: The present study represents the first attempt using a human experimental approach to assess the effects of WM anisotropy on EEG source analysis. [Copyright &y& Elsevier]

The failure of the remyelination processes in multiple sclerosis contributes to the formation of chronic demyelinated plaques that lead to severe neurological deficits. Long-term cuprizone treatment of C57BL/6 mice resulted in pronounced white matter pathology characterized by oligodendrocyte depletion, irreversible demyelination and persistent functional deficits after cuprizone withdrawal. The use of a combination of in vivo diffusion tensor magnetic resonance imaging (DT-MRI) and histological analyses allowed for an accurate longitudinal assessment of demyelination. Injection of triiodothyronine (T3 ) hormone over a 3 week interval after cuprizone withdrawal progressively restored the normal DT-MRI phenotype accompanied by an improvement of clinical signs and remyelination. The effects of T3 were not restricted to the later stages of remyelination but increased the expression of sonic hedgehog and the numbers of Olig2+ and PSA-NCAM+ precursors and proliferative cells. Our findings establish a role for T3 as an inducer of oligodendrocyte progenitor cells in adult mouse brain following chronic demyelination. [ABSTRACT FROM AUTHOR]

Experimental and imaging studies in monkeys have outlined various long association fiber pathways within the fronto-temporo-parietal region. In the present study, the trajectory of the extreme capsule (EmC) fibers has been delineated in five human subjects using DT-MRI tractography. The EmC seems to be a long association fiber pathway, which courses between the inferior frontal region and the superior temporal gyrus extending into the inferior parietal lobule. Comparison of EmC fibers with the adjacent association fiber pathway, the middle longitudinal fascicle (MdLF), in the same subjects reveals that EmC is located in a medial and rostral position relative to MdLF flanking in part the medial wall of the insula. The EmC can also be differentiated from other neighboring fiber pathways such as the external capsule, uncinate fascicle, arcuate fascicle, superior longitudinal fascicles II and III, and the inferior longitudinal fascicle. Given the location of EmC within the language zone, specifically Broca’s area in the frontal lobe, and Wernicke’s area in the temporal lobe and inferior parietal lobule, it is suggested that the extreme capsule could have a role in language function. [ABSTRACT FROM AUTHOR]

Large datasets typically contain coarse features comprised of finer sub-features. Even it the shapes of the small structures are evident in a 3D display, the aggregate shapes they suggest may not be easily inferred. From previous studies in shape perception, the evidence has not been clear whether physically-based illumination confers any advantage over local illumination for understanding scenes that arise in visualization of large data sets that contain features at two distinct scales. In this paper we show that physically- based illumination can improve the perception for some static scenes of complex 3D geometry from flow fields. We perform human- subjects experiments to quantify the effect of physically-based illumination on participant performance for two tasks: selecting the closer of two streamtubes from a field of tubes, and identifying the shape of the domain of a flow field over different densities of tubes. We find that physically-based illumination influences participant performance as strongly as perspective projection, suggesting that physically-based illumination is indeed a strong cue to the layout of complex scenes. We also find that increasing the density of tubes for the shape identification task improved participant performance under physically-based illumination but not under the traditional hardware-accelerated illumination model. [ABSTRACT FROM AUTHOR]

We present a method for clustering diffusion-tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. Our approach is guided by assumptions about the proximity of fibers comprising discrete white-matter bundles and proceeds as follows: We first employ a sampling and culling strategy for generating DTI integral curves and then constrain the curves so that they terminate in gray matter. This approach seems likely to retain anatomically plausible fibers. We then employ a clustering method based on a proximity measure calculated between every pair of curves. We interactively selected a proximity threshold to achieve visually optimal clustering in models from four DTI data sets. An expert rater then assigned a confidence rating about bundle presence and accuracy for each of the 12 target fiber bundles of varying calibers and types (i.e. commissural, association, and projection) in each data set. The interactive clustering and evaluation information was incorporated to create a fiber-bundle template. We then used the template to cluster and label the fiber bundles automatically in new data sets. According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles, although fiber bundles with smaller calibers and those that are not highly directionally coherent are identified with lower confidence. This work has the potential to provide an automatic and robust way to find and study neural fiber bundles within DTI. [ABSTRACT FROM AUTHOR]

Wherever anisotropic behavior in physical measurements or models is encountered matrices provide adequate means to describe this anisotropy. Prominent examples are the diffusion tensor magnetic resonance imaging in medical imaging or the stress tensor in civil engineering. As most measured data these matrix-valued data are also polluted by noise and require restoration. The restoration of scalar images corrupted by noise via minimization of an energy functional is a well-established technique that offers many advantages. A convenient way to achieve this minimization is second-order cone programming (SOCP). The goal of this article is to transfer this method to the matrix-valued setting. It is shown how SOCP can be applied to minimize various energy functionals defined for matrix fields. These functionals couple the different matrix channels taking into account the relations between them. Furthermore, new functionals for the regularization of matrix data are proposed and the corresponding Euler–Lagrange equations are derived by means of matrix differential calculus. Numerical experiments substantiate the usefulness of the proposed methods for the restoration of matrix fields. [ABSTRACT FROM AUTHOR]

Diffusion tensor magnetic resonance imaging, is a image acquisition method, that provides matrix- valued data, so-called matrix fields. Hence image processing tools for the filtering and analysis of these data types are in demand. In this article, we propose a generic framework that allows us to find the matrix-valued counterparts of the Perona–Malik PDEs with various diffusivity functions. To this end we extend the notion of derivatives and associated differential operators to matrix fields of symmetric matrices by adopting an operator-algebraic point of view. In order to solve these novel matrix-valued PDEs successfully we develop truly matrix-valued analogs to numerical solution schemes of the scalar setting. Numerical experiments performed on both synthetic and real world data substantiate the effectiveness of our novel matrix-valued Perona–Malik diffusion filters. [ABSTRACT FROM AUTHOR]

Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) is an imaging modality that is gaining importance in clinical applications. However, in a clinical environment, data have to be acquired rapidly, often at the expense of the image quality. This often results in DTI datasets that are not suitable for complex postprocessing like fiber tracking. We propose a new variational framework to improve the estimation of DT-MRI in this clinical context. Most of the existing estimation methods rely on a log-Gaussian noise (Gaussian noise on the image logarithms), or a Gaussian noise, that do not reflect the Rician nature of the noise in MR images with a low signal-to-noise ratio (SNR). With these methods, the Rician noise induces a shrinking effect: the tensor volume is underestimated when other noise models are used for the estimation. In this paper, we propose a maximum likelihood strategy that fully exploits the assumption of a Rician noise. To further reduce the influence of the noise, we optimally exploit the spatial correlation by coupling the estimation with an anisotropic prior previously proposed on the spatial regularity of the tensor field itself, which results in a maximum a posteriori estimation. Optimizing such a nonlinear criterion requires adapted tools for tensor computing. We show that Riemannian metrics for tensors, and more specifically the log-Euclidean metrics, are a good candidate and that this criterion can be efficiently optimized. Experiments on synthetic data show that our method correctly handles the shrinking effect even with very low SNR, and that the positive definiteness of tensors is always ensured. Results on real clinical data demonstrate the truthfulness of the proposed approach and show promising improvements of fiber tracking in the brain and the spinal cord. [ABSTRACT FROM AUTHOR]

Abstract: Recent anatomical studies have found that cortical neurons are mainly preserved during the aging process while myelin damage and even axonal loss is prominent throughout the forebrain. We used diffusion tensor imaging (DT-MRI) to evaluate the hypothesis that during the process of normal aging, white matter changes preferentially affect the integrity of long corticocortical association fiber tracts, specifically the superior longitudinal fasciculus II and the cingulum bundle. This would disrupt communication between the frontal lobes and other forebrain regions leading to cognitive impairments. We analyzed DT-MRI datasets from seven young and seven elderly behaviorally characterized rhesus monkeys, creating fractional anisotropy (FA) maps of the brain. Significant age-related reductions in mean FA values were found for the superior longitudinal fasciculus II and the cingulum bundle, as well as the anterior corpus callosum. Comparison of these FA reductions with behavioral measures demonstrated a statistically significant linear relationship between regional FA and performance on a test of executive function. These findings support the hypothesis that alterations to the integrity of these long association pathways connecting the frontal lobe with other forebrain regions contribute to cognitive impairments in normal aging. To our knowledge this is the first investigation reporting such alterations in the aging monkey. [Copyright &y& Elsevier]

Abstract: Since the existence of the occipitofrontal fascicle (OFF) in humans has remained controversial, we utilized diffusion tensor imaging (DT-MRI)-based segmentation and tractography to investigate its trajectory in vivo in the human. We found that the OFF is distinct from the subcallosal fasciculus or Muratoff''s bundle (MB) and extends from the dorsal and medial parts of the occipital lobe as well as the dorsal, medial and inferior parietal lobules to the dorsal and medial part of the prefrontal and premotor regions. In most of its course, it remains parallel to the corpus callosum, the caudate nucleus and the lateral ventricle. In the coronal plane, the OFF is discerned in the core of the white matter medial to the corona radiata and the superior longitudinal fascicle II (SLF II) and lateral to MB and the corpus callosum. The volumetric measurements of the stem portion of the OFF indicate that the OFF is smaller than the SLF II and the cingulum bundle. Since DT-MRI allows the visualization of OFF fibers leading to the projection areas but not to the origin or termination of these fibers, this has been extrapolated from the experimental data in non-human primates. The OFF may have a role in visual spatial processing along with SLF II. [Copyright &y& Elsevier]

Abstract: Objective: The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the pre-operative MR data and the intra-operative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) to create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI. Materials and methods: Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3 T prior to surgery. SPGR and T2w images were acquired with a 0.5 T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of-the-art systems based only on rigid registration. Results: Alignment between pre-operative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after non-rigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p <0.001). Conclusions: We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative fMRI with intra-operative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard real-time constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon. [Copyright &y& Elsevier]

Abstract: Positive semidefinite matrix fields are becoming increasingly important in digital imaging. One reason for this tendency is the introduction of diffusion tensor magnetic resonance imaging (DT-MRI). In order to perform shape analysis, enhancement or segmentation of such tensor/matrix fields, appropriate image processing tools must be developed. This paper extends fundamental morphological operations to the matrix-valued setting. We start by presenting novel definitions for the supremum and infimum of a set of matrices since these notions lie at the heart of the morphological operations. In contrast to naive approaches like the component-wise maximum or minimum of the matrix channels, our approach is based on the Loewner ordering for symmetric matrices. The notions of supremum and infimum deduced from this partial ordering satisfy desirable properties such as rotation invariance, preservation of positive semidefiniteness, and continuous dependence on the input data. We introduce erosion, dilation, opening, closing, top hats, morphological derivatives, shock filters, and mid-range filters for positive semidefinite matrix-valued images. These morphological operations incorporate information simultaneously from all matrix channels rather than treating them independently. Experiments on DT-MRI images with ball- and rod-shaped structuring elements illustrate the properties and performance of our morphological operators for matrix-valued data. [Copyright &y& Elsevier]

Abstract: Tensors are nowadays an increasing research domain in different areas, especially in image processing, motivated for example by diffusion tensor magnetic resonance imaging (DT-MRI). Up to now, algorithms and tools developed to deal with tensors were founded on the assumption of a matrix vector space with the constraint of remaining symmetric positive definite matrices. On the contrary, our approach is grounded on the theoretically well-founded differential geometrical properties of the space of multivariate normal distributions, where it is possible to define an affine-invariant Riemannian metric and express statistics on the manifold of symmetric positive definite matrices. In this paper, we focus on the contribution of these tools to the anisotropic filtering and regularization of tensor fields. To validate our approach we present promising results on both synthetic and real DT-MRI data. [Copyright &y& Elsevier]

Abstract: We present a method for segmenting white matter as well as the gray matter structures from diffusion tensor magnetic resonance images (DT-MRI). The segmentation is done evolving a set of coupled level set functions. The zero level set of each level set function forms a surface in 3D that is driven by the region-based force including all tensors belonging to a certain region. The region-based force is defined by using a very sensitive similarity measure between DT. We apply our method for segmenting the thalamus and its nuclei. This technical paper proposes several new strategies for level set methods to segment efficiently complex objects as present in DT-MRI. First of all, we present a very sensitive similarity measure that distinguishes very subtle differences between regions within, for example, the thalamus. Secondly, we present a new way of selecting the most representative tensor for group of tensors for these kinds of applications. We argue for the importance to use the tensor minimizing the variation within the group of tensors instead of the mean tensor as suggested in other papers on tensor segmentation. The third important point is the necessity of using several coupled level sets to define the background. Methods differentiating only between foreground and background will fail when applied to complex structures such as the brain. It is crucial for a region-based approach to consider all the surrounding structures for a correct definition of the forces driving the segmentation. [Copyright &y& Elsevier]

Abstract: We propose a novel spectral decomposition of a 4th-order covariance tensor, Σ. Just as the variability of vector (i.e., a 1st-order tensor)-valued random variable is characterized by a covariance matrix (i.e., a 2nd-order tensor), S, the variability of a 2nd-order tensor-valued random variable, D, is characterized by a 4th-order covariance tensor, Σ. Accordingly, just as the spectral decomposition of S is a linear combination of its eigenvalues and the outer product of its corresponding (1st-order tensors) eigenvectors, the spectral decomposition of Σ is a linear combination of its eigenvalues and the outer product of its corresponding 2nd-order eigentensors. Analogously, these eigenvalues and 2nd-order eigentensors can be used as features with which to represent and visualize variability in tensor-valued data. Here we suggest a framework to visualize the angular structure of Σ, and then use it to assess and characterize the variability of synthetic diffusion tensor magnetic resonance imaging (DTI) data. The spectral decomposition suggests a hierarchy of symmetries with which to classify the statistical anisotropy inherent in tensor data. We also present maximum likelihood estimates of the sample mean and covariance tensors associated with D, and derive formulae for the expected value of the mean and variance of the projection of D along a particular direction (i.e., the apparent diffusion coefficient or ADC). These findings would be difficult, if not impossible, to glean if we treated 2nd-order tensor random variables as vector-valued random variables, which is conventionally done in multi-variate statistical analysis. [Copyright &y& Elsevier]

Matrix-valued data sets arise in a number of applications including diffusion tensor magnetic resonance imaging (DT-MRI) and physical measurements of anisotropic behaviour. Consequently, there arises the need to filter and segment such tensor fields. In order to detect edge-like structures in tensor fields, we first generalise Di Zenzo’s concept of a structure tensor for vector-valued images to tensor-valued data. This structure tensor allows us to extend scalar-valued mean curvature motion and self-snakes to the tensor setting. We present both two-dimensional and three-dimensional formulations, and we prove that these filters maintain positive semidefiniteness if the initial matrix data are positive semidefinite. We give an interpretation of tensorial mean curvature motion as a process for which the corresponding curve evolution of each generalised level line is the gradient descent of its total length. Moreover, we propose a geodesic active contour model for segmenting tensor fields and interpret it as a minimiser of a suitable energy functional with a metric induced by the tensor image. Since tensorial active contours incorporate information from all channels, they give a contour representation that is highly robust under noise. Experiments on three-dimensional DT-MRI data and an indefinite tensor field from fluid dynamics show that the proposed methods inherit the essential properties of their scalar-valued counterparts. [ABSTRACT FROM AUTHOR]

We present a regularized method for solving an inverse problem in Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data. In the case of brain images, DT-MR imagery technique produces a tensor field that indicates the local orientation of nerve bundles. Now days, the spatial resolution of this technique is limited by the partial volume effect produced in voxels that contain fiber crossings or bifurcations. In this paper, we proposed a method for recovering the intra-voxel information and inferring the brain connectivity. We assume that the observed tensor is a linear combination of a given tensor basis, therefore, the aim of our approach is the computation of the unknown coefficients of this linear combination. By regularizing the problem, we introduce the needed prior information about the piecewise smoothness of nerve bundles orientation. As a result, we recover a multi-tensor field. Moreover, for estimating the nerve bundles trajectory, we propose a method based on stochastic walks of particles through the computed multi-tensor field. The performance of the method is demonstrated by experiments in both synthetic and real data. [ABSTRACT FROM AUTHOR]