Image Processing for Diffusion Tensor Magnetic Resonance Imaging

The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis

BACKGROUND

Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography.

METHODS

Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively.

RESULTS

We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right (p = 0.03), but not left (p = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p = .00023; p = .00023) originating in selective PFC subregions.

CONCLUSIONS

In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.

A Review of Three-Dimensional Medical Image Visualization

Importance. Medical images are essential for modern medicine and an important research subject in visualization. However, medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques that could increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Our paper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medical experts and visualization researchers.Highlights. Fundamental visualization techniques are revisited for various medical imaging modalities, from computational tomography to diffusion tensor imaging, featuring techniques that enhance spatial perception, which is critical for medical practices. The state-of-the-art of medical visualization is reviewed based on a procedure-oriented classification of medical problems for studies of individuals and populations. This paper summarizes free software tools for different modalities of medical images designed for various purposes, including visualization, analysis, and segmentation, and it provides respective Internet links.Conclusions. Visualization techniques are a useful tool for medical experts to tackle specific medical problems in their daily work. Our review provides a quick reference to such techniques given the medical problem and modalities of associated medical images. We summarize fundamental techniques and readily available visualization tools to help medical experts to better understand and utilize medical imaging data. This paper could contribute to the joint effort of the medical and visualization communities to advance precision medicine.

Exploring arterial tissue microstructural organization using non-Gaussian diffusion magnetic resonance schemes

The purpose of this study was to characterize the alterations in microstructural organization of arterial tissue using higher-order diffusion magnetic resonance schemes. Three porcine carotid artery models namely; native, collagenase treated and decellularized, were used to estimate the contribution of collagen and smooth muscle cells (SMC) on diffusion signal attenuation using gaussian and non-gaussian schemes. The samples were imaged in a 7 T preclinical scanner. High spatial and angular resolution diffusion weighted images (DWIs) were acquired using two multi-shell (max b-value = 3000 s/mm²) acquisition protocols. The processed DWIs were fitted using monoexponential, stretched-exponential, kurtosis and bi-exponential schemes. Directionally variant and invariant microstructural parametric maps of the three artery models were obtained from the diffusion schemes. The parametric maps were used to assess the sensitivity of each diffusion scheme to collagen and SMC composition in arterial microstructural environment. The inter-model comparison showed significant differences across the considered models. The bi-exponential scheme based slow diffusion compartment (Ds) was highest in the absence of collagen, compared to native and decellularized microenvironments. In intra-model comparison, kurtosis along the radial direction was the highest. Overall, the results of this study demonstrate the efficacy of higher order dMRI schemes in mapping constituent specific alterations in arterial microstructure.

Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain

Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation-diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation-diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.

The impact of position-orientation adaptive smoothing in diffusion weighted imaging-From diffusion metrics to fiber tractography

In contrast to commonly used approaches to improve data quality in diffusion weighted imaging, position-orientation adaptive smoothing (POAS) provides an edge-preserving post-processing approach. This study aims to investigate its potential and effects on image quality, diffusion metrics, and fiber tractography of the corticospinal tract in relation to non-post-processed and averaged data. 22 healthy volunteers were included in this study. For each volunteer five clinically applicable diffusion weighted imaging data sets were acquired and post-processed by standard averaging and POAS. POAS post-processing led to significantly higher signal-to-noise-ratios (p < 0.001), lower fractional anisotropy across the whole brain (p < 0.05) and reduced intra-subject variability of diffusion weighted imaging signal intensity and fractional anisotropy (p < 0.001, p = 0.006). Fiber tractography of the corticospinal tract resulted in significantly (p = 0.027, p = 0.014) larger tract volumes while fiber density was the lowest. Similarity across tractography results was highest for POAS post-processed data (p < 0.001). POAS post-processing enhances image quality, decreases the intra-subject variability of signal intensity and fractional anisotropy, increases fiber tract volume of the corticospinal tract, and leads to higher reproducibility of tractography results. Thus, POAS post-processing supports a reliable and more accurate fiber tractography of the corticospinal tract, being mandatory for the clinical use.

Diffusion MRI visualization

Modern diffusion magnetic resonance imaging (dMRI) acquires intricate volume datasets and biological meaning can only be found in the relationship between its different measurements. Suitable strategies for visualizing these complicated data have been key to interpretation by physicians and neuroscientists, for drawing conclusions on brain connectivity and for quality control. This article provides an overview of visualization solutions that have been proposed to date, ranging from basic grayscale and color encodings to glyph representations and renderings of fiber tractography. A particular focus is on ongoing and possible future developments in dMRI visualization, including comparative, uncertainty, interactive and dense visualizations.

Machine learning based compartment models with permeability for white matter microstructure imaging

Some microstructure parameters, such as permeability, remain elusive because mathematical models that express their relationship to the MR signal accurately are intractable. Here, we propose to use computational models learned from simulations to estimate these parameters. We demonstrate the approach in an example which estimates water residence time in brain white matter. The residence time τ_i of water inside axons is a potentially important biomarker for white matter pathologies of the human central nervous system, as myelin damage is hypothesised to affect axonal permeability, and thus τ_i. We construct a computational model using Monte Carlo simulations and machine learning (specifically here a random forest regressor) in order to learn a mapping between features derived from diffusion weighted MR signals and ground truth microstructure parameters, including τ_i. We test our numerical model using simulated and in vivo human brain data. Simulation results show that estimated parameters have strong correlations with the ground truth parameters (R²={0.88,0.95,0.82,0.99}) for volume fraction, residence time, axon radius and diffusivity respectively), and provide a marked improvement over the most widely used Kärger model (R²={0.75,0.60,0.11,0.99}). The trained model also estimates sensible microstructure parameters from in vivo human brain data acquired from healthy controls, matching values found in literature, and provides better reproducibility than the Kärger model on both the voxel and ROI level. Finally, we acquire data from two Multiple Sclerosis (MS) patients and compare to the values in healthy subjects. We find that in the splenium of corpus callosum (CC-S) the estimate of the residence time is 0.57±0.05s for the healthy subjects, while in the MS patient with a lesion in CC-S it is 0.33±0.12s in the normal appearing white matter (NAWM) and 0.19±0.11s in the lesion. In the corticospinal tracts (CST) the estimate of the residence time is 0.52±0.09s for the healthy subjects, while in the MS patient with a lesion in CST it is 0.56±0.05s in the NAWM and 0.13±0.09s in the lesion. These results agree with our expectations that the residence time in lesions would be lower than in NAWM because the loss of myelin should increase permeability. Overall, we find parameter estimates in the two MS patients consistent with expectations from the pathology of MS lesions demonstrating the clinical potential of this new technique.

POAS4SPM: a toolbox for SPM to denoise diffusion MRI data

We present an implementation of a recently developed noise reduction algorithm for dMRI data, called multi-shell position orientation adaptive smoothing (msPOAS), as a toolbox for SPM. The method intrinsically adapts to the structures of different size and shape in dMRI and hence avoids blurring typically observed in non-adaptive smoothing. We give examples for the usage of the toolbox and explain the determination of experiment-dependent parameters for an optimal performance of msPOAS.

High angular resolution diffusion imaging with stimulated echoes: compensation and correction in experiment design and analysis

Stimulated echo acquisition mode (STEAM) diffusion MRI can be advantageous over pulsed-gradient spin-echo (PGSE) for diffusion times that are long compared with T2 . It therefore has potential for biomedical diffusion imaging applications at 7T and above where T2 is short. However, gradient pulses other than the diffusion gradients in the STEAM sequence contribute much greater diffusion weighting than in PGSE and lead to a disrupted experimental design. Here, we introduce a simple compensation to the STEAM acquisition that avoids the orientational bias and disrupted experiment design that these gradient pulses can otherwise produce. The compensation is simple to implement by adjusting the gradient vectors in the diffusion pulses of the STEAM sequence, so that the net effective gradient vector including contributions from diffusion and other gradient pulses is as the experiment intends. High angular resolution diffusion imaging (HARDI) data were acquired with and without the proposed compensation. The data were processed to derive standard diffusion tensor imaging (DTI) maps, which highlight the need for the compensation. Ignoring the other gradient pulses, a bias in DTI parameters from STEAM acquisition is found, due both to confounds in the analysis and the experiment design. Retrospectively correcting the analysis with a calculation of the full B matrix can partly correct for these confounds, but an acquisition that is compensated as proposed is needed to remove the effect entirely.

FATCAT: (an efficient) Functional and Tractographic Connectivity Analysis Toolbox

We present a suite of software tools for facilitating the combination of functional magnetic resonance imaging (FMRI) and diffusion-based tractography from a network-focused point of view. The programs have been designed for investigating functionally derived gray matter networks and related structural white matter networks. The software comprises the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), now freely distributed with AFNI. This toolbox supports common file formats and has been designed to integrate as easily as possible with existing standard FMRI pipelines and diffusion software, such as AFNI, FSL, and TrackVis. The programs are efficient, run by commandline for facilitating group processing, and produce several visualizable outputs. Here, we present the programs and their underlying methods, and we also provide a test example of resting-state FMRI analysis combined with tractography. Tractography results are compared with existing methods, showing significantly reduced runtime and generally similar connectivity, but with important differences such as more circumscribed tract regions and more physiologically identifiable paths produced between several region-of-interest pairs. Currently, FATCAT uses only diffusion tensor-based tractography (one direction per voxel), but higher-order models will soon be included.

A flocking based method for brain tractography

We propose a new method to estimate axonal fiber pathways from Multiple Intra-Voxel Diffusion Orientations. Our method uses the multiple local orientation information for leading stochastic walks of particles. These stochastic particles are modeled with mass and thus they are subject to gravitational and inertial forces. As result, we obtain smooth, filtered and compact trajectory bundles. This gravitational interaction can be seen as a flocking behavior among particles that promotes better and robust axon fiber estimations because they use collective information to move. However, the stochastic walks may generate paths with low support (outliers), generally associated to incorrect brain connections. In order to eliminate the outlier pathways, we propose a filtering procedure based on principal component analysis and spectral clustering. The performance of the proposal is evaluated on Multiple Intra-Voxel Diffusion Orientations from two realistic numeric diffusion phantoms and a physical diffusion phantom. Additionally, we qualitatively demonstrate the performance on in vivo human brain data.

CT dataset anisotropy management for oral implantology planning software

PURPOSE

Measurements accomplished on most oral implantology software are often affected by some systematic effects, of which those related to the CT dataset anisotropy are the most relevant. In fact, most of these commercial systems do not manage possible anisotropy in input datasets, leaving the responsibility to users and radiologists. Therefore, in order to achieve a better knowledge of the patient's anatomy before inserting the implants, and thus reducing the risk of damaging the surrounding structures, the implementation of a complete and precise anisotropy management system is required.

METHODS

We present an anisotropy management algorithm for pre-operative planning software that is able to handle any anisotropic CT dataset, and, as a result, provides a very precise isotropic equivalent. The developed algorithm exploits two interpolation passes to correct anisotropy and is characterised by linear complexity, needing just a few seconds to accomplish the tasks. The first pass concerns the integer-filling of possible intra-slice void spaces of the original slices, having the responsibility of a correct spreading of the radiographic details along the volume height axis. The second pass, instead, reformats its input dataset under isotropic conditions exploiting a contribution-based interpolation sub-algorithm.

RESULTS

The algorithm has been evaluated by comparing the anisotropy implied systematic effects for both anisotropic and interpolation-reconstructed radiographic volumes of five different scans. The proposed system demonstrated to be able to successfully handle any dataset interslice-pixel-size ratio. Moreover, the precision achieved proved to be even better than that of another precise algorithm that we previously developed and published, validating the proposed approach as a consequence.

CONCLUSIONS

The proposed algorithm makes it possible to handle and correct anisotropy in input CT datasets, helping to avoid anisotropy implied systematic effects on related measurements, and consequently supporting pre-operative planning software by providing a precise and isotropic equivalent volume on which to work.

A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury

Mild traumatic brain injury (mTBI), also referred to as concussion, remains a controversial diagnosis because the brain often appears quite normal on conventional computed tomography (CT) and magnetic resonance imaging (MRI) scans. Such conventional tools, however, do not adequately depict brain injury in mTBI because they are not sensitive to detecting diffuse axonal injuries (DAI), also described as traumatic axonal injuries (TAI), the major brain injuries in mTBI. Furthermore, for the 15 to 30 % of those diagnosed with mTBI on the basis of cognitive and clinical symptoms, i.e., the "miserable minority," the cognitive and physical symptoms do not resolve following the first 3 months post-injury. Instead, they persist, and in some cases lead to long-term disability. The explanation given for these chronic symptoms, i.e., postconcussive syndrome, particularly in cases where there is no discernible radiological evidence for brain injury, has led some to posit a psychogenic origin. Such attributions are made all the easier since both posttraumatic stress disorder (PTSD) and depression are frequently co-morbid with mTBI. The challenge is thus to use neuroimaging tools that are sensitive to DAI/TAI, such as diffusion tensor imaging (DTI), in order to detect brain injuries in mTBI. Of note here, recent advances in neuroimaging techniques, such as DTI, make it possible to characterize better extant brain abnormalities in mTBI. These advances may lead to the development of biomarkers of injury, as well as to staging of reorganization and reversal of white matter changes following injury, and to the ability to track and to characterize changes in brain injury over time. Such tools will likely be used in future research to evaluate treatment efficacy, given their enhanced sensitivity to alterations in the brain. In this article we review the incidence of mTBI and the importance of characterizing this patient population using objective radiological measures. Evidence is presented for detecting brain abnormalities in mTBI based on studies that use advanced neuroimaging techniques. Taken together, these findings suggest that more sensitive neuroimaging tools improve the detection of brain abnormalities (i.e., diagnosis) in mTBI. These tools will likely also provide important information relevant to outcome (prognosis), as well as play an important role in longitudinal studies that are needed to understand the dynamic nature of brain injury in mTBI. Additionally, summary tables of MRI and DTI findings are included. We believe that the enhanced sensitivity of newer and more advanced neuroimaging techniques for identifying areas of brain damage in mTBI will be important for documenting the biological basis of postconcussive symptoms, which are likely associated with subtle brain alterations, alterations that have heretofore gone undetected due to the lack of sensitivity of earlier neuroimaging techniques. Nonetheless, it is noteworthy to point out that detecting brain abnormalities in mTBI does not mean that other disorders of a more psychogenic origin are not co-morbid with mTBI and equally important to treat. They arguably are. The controversy of psychogenic versus physiogenic, however, is not productive because the psychogenic view does not carefully consider the limitations of conventional neuroimaging techniques in detecting subtle brain injuries in mTBI, and the physiogenic view does not carefully consider the fact that PTSD and depression, and other co-morbid conditions, may be present in those suffering from mTBI. Finally, we end with a discussion of future directions in research that will lead to the improved care of patients diagnosed with mTBI.

Genetic influence on the working memory circuitry: behavior, structure, function and extensions to illness

Working memory is a highly heritable complex cognitive trait that is critical for a number of higher-level functions. However, the neural substrates of this behavioral phenotype are intricate and it is unknown through what precise biological mechanism variation in working memory is transmitted. In this review we explore different functional and structural components of the working memory circuitry, and the degree to which each of them is contributed to by genetic factors. Specifically, we consider dopaminergic function, glutamatergic function, white matter integrity and gray matter structure all of which provide potential mechanisms for the inheritance of working memory deficits. In addition to discussing the overall heritability of these measures we also address specific genes that may play a role. Each of these heritable components has the potential to uniquely contribute to the working memory deficits observed in genetic disorders, including 22q deletion syndrome, fragile X syndrome, phenylketonuria (PKU), and schizophrenia. By observing the individual contributions of disruptions in different components of the working memory circuitry to behavioral performance, we highlight the concept that there may be many routes to a working memory deficit; even though the same cognitive measure may be a valid endophenotype across different disorders, the underlying cause of, and treatment for, the deficit may differ. This has implications for our understanding of the transmission of working memory deficits in both healthy and disordered populations.

A geometric flow-based approach for diffusion tensor image segmentation

Diffusion tensor magnetic resonance imaging (DT-MRI, shortened as DTI) produces, from a set of diffusion-weighted magnetic resonance images, tensor-valued images where each voxel is assigned a 3x3 symmetric, positive-definite matrix. This tensor is simply the covariance matrix of a local Gaussian process with zero mean, modelling the average motion of water molecules. We propose a three-dimensional geometric flow-based model to segment the main core of cerebral white matter fibre tracts from DTI. The segmentation is carried out with a front propagation algorithm. The front is a three-dimensional surface that evolves along its normal direction with speed that is proportional to a linear combination of two measures: a similarity measure and a consistency measure. The similarity measure computes the similarity of the diffusion tensors at a voxel and its neighbouring voxels along the normal to the front; the consistency measure is able to speed up the propagation at locations where the evolving front is more consistent with the diffusion tensor field, to remove noise effect to some extent, and thus to improve results. We validate the proposed model and compare it with some other methods using synthetic and human brain DTI data; experimental results indicate that the proposed model improves the accuracy and efficiency in segmentation.

Can we differentiate true white matter fibers from pseudofibers inside a brain abscess cavity using geometrical diffusion tensor imaging metrics?

High fractional anisotropy (FA) usually reflects the orientation and integrity of white matter (WM) fibers. Other regions of increased FA have been described, such as brain abscesses, developing cortex, and areas of hemorrhage. It may not be possible to differentiate true fibers from the pseudofibers found inside an abscess cavity on the basis of FA and mean diffusivity (MD). The aim of this study was to differentiate true WM fibers from pseudo WM tracts inside the abscess cavity using geometrical diffusion tensor imaging metrics [linear anisotropy (CL), planar anisotropy (CP), and spherical anisotropy (CS)]. Diffusion tensor imaging was performed in 42 patients with brain abscess and 10 age/sex-matched controls. Automated segmentation using Java-based software divided the abscess cavity into two sub-regions with FA < 0.20 and FA > or = 0.20. Quantitation was carried out on the sub-regions of the abscess cavity with FA > or = 0.20. In healthy controls, regions of interest were placed on the corpus callosum, posterior limb of the internal capsule, and periventricular and subcortical WM. Significantly increased CP values were observed inside the abscess cavity compared with various normal WM regions. Significantly increased FA and CL values were observed in the abscess cavity compared with subcortical WM only. However decreased FA and CL values were observed in the cavity compared with the corpus callosum, posterior limb of the internal capsule, and periventricular WM. The 95% confidence intervals of means for the abscess cavity were well separated from those for WM in the case of CL and CP; however, they overlapped in the case of FA, MD, and CS. High CP with low CL inside the abscess cavity suggests that the shape of the diffusion tensor is predominantly planar, whereas it is linear in WM tracts. These geometrical indices may have advantages over FA for differentiating true from pseudo WM tracts inside the abscess cavity.

Automatic tractography segmentation using a high-dimensional white matter atlas

We propose a new white matter atlas creation method that learns a model of the common white matter structures present in a group of subjects. We demonstrate that our atlas creation method, which is based on group spectral clustering of tractography, discovers structures corresponding to expected white matter anatomy such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, and corona radiata. The white matter clusters are augmented with expert anatomical labels and stored in a new type of atlas that we call a high-dimensional white matter atlas. We then show how to perform automatic segmentation of tractography from novel subjects by extending the spectral clustering solution, stored in the atlas, using the Nystrom method. We present results regarding the stability of our method and parameter choices. Finally we give results from an atlas creation and automatic segmentation experiment. We demonstrate that our automatic tractography segmentation identifies corresponding white matter regions across hemispheres and across subjects, enabling group comparison of white matter anatomy.

Diffusion tensor imaging: structural adaptive smoothing

Diffusion Tensor Imaging (DTI) data is characterized by a high noise level. Thus, estimation errors of quantities like anisotropy indices or the main diffusion direction used for fiber tracking are relatively large and may significantly confound the accuracy of DTI in clinical or neuroscience applications. Besides pulse sequence optimization, noise reduction by smoothing the data can be pursued as a complementary approach to increase the accuracy of DTI. Here, we suggest an anisotropic structural adaptive smoothing procedure, which is based on the Propagation-Separation method and preserves the structures seen in DTI and their different sizes and shapes. It is applied to artificial phantom data and a brain scan. We show that this method significantly improves the quality of the estimate of the diffusion tensor, by means of both bias and variance reduction, and hence enables one either to reduce the number of scans or to enhance the input for subsequent analysis such as fiber tracking.

[Microstructure of the lung: diffusion measurement of hyperpolarized 3Helium]

Imaging methods to study the lung are traditionally based on x-ray or on radioactive contrast agents. Conventional magnetic resonance imaging (MRI) has only limited applications for lung imaging because of the low tissue density of protons concentration of hydrogen atoms, which are usually the basis for the imaging. The introduction of hyperpolarized noble gases as a contrast agent in MRI has opened new possibilities for lung diagnosis. The present paper describes this new technique. Diffusion-weighted MRI for assessment of the lung microstructure is presented here as an example of the new possibilities of functional imaging. Studies to determine the sensitivity of the diffusion measurement and regarding the correlation with traditionally established methods are also presented, along with results of the measurement of the reproducibility determined in a clinical pilot study on healthy volunteers and patients. Furthermore, a pilot measurement of the 3He diffusion tensor in the lung is presented.

Diffusion Tensor Magnetic Resonance Imaging in Multiple Sclerosis

Multiple sclerosis (MS), a demyelinating disease, occurs principally in the white matter (WM) of the central nervous system. Conventional magnetic resonance imaging (MRI) is sensitive to some, but not all, brain changes associated with MS. Diffusion-weighted imaging (DWI) provides information about water diffusion in tissue and diffusion tensor MRI (DT-MRI) about fiber direction, allowing for the identification of WM abnormalities that are not apparent on conventional MRI images. These techniques can quantitatively characterize the local microstructure of tissues. MS-associated disease processes lead to regions characterized by an increased amount of water diffusion and a decrease in the anisotropy of diffusion direction. These changes have been found to produce different patterns in MS patients presenting different courses of the disease. Changes in water diffusion may allow examination of the type, appearance, enhancement, and location of lesions not readily visible by other means. Ongoing studies of MS are integrating conventional MRI and DT-MRI measures with connectivity-based regional assessment, aiming to provide a better understanding of the nature and the location of WM lesions. This integration and the development of novel image-processing and visualization techniques may improve the understanding of WM architecture and its disruption in MS. This article presents a brief history of DWI, its basic principles and applications in the study of MS, a review of the properties and applications of DT-MRI, and their use in the study of MS. In addition, this article illustrates the methodology for the analysis of DT-MRI in ongoing studies of MS.

A Hamilton-Jacobi-Bellman approach to high angular resolution diffusion tractography

This paper describes a new framework for white matter tractography in high angular resolution diffusion data. A direction-dependent local cost is defined based on the diffusion data for every direction on the unit sphere. Minimum cost curves are determined by solving the Hamilton-Jacobi-Bellman using an efficient algorithm. Classical costs based on the diffusion tensor field can be seen as a special case. While the minimum cost (or equivalently the travel time of a particle moving along the curve) and the anisotropic front propagation frameworks are related, front speed is related to particle speed through a Legendre transformation which can severely impact anisotropy information for front propagation techniques. Implementation details and results on high angular diffusion data show that this method can successfully take advantage of the increased angular resolution in high b-value diffusion weighted data despite lower signal to noise ratio.

DT-MRI denoising and neuronal fiber tracking

Diffusion tensor imaging can provide the fundamental information required for viewing structural connectivity. However, robust and accurate acquisition and processing algorithms are needed to accurately map the nerve connectivity. In this paper, we present a novel algorithm for extracting and visualizing the fiber tracts in the CNS, specifically in the brain. The automatic fiber tract mapping problem will be solved in two phases, namely a data smoothing phase and a fiber tract mapping phase. In the former, smoothing of the diffusion-weighted data (prior to tensor calculation) is achieved via a weighted TV-norm minimization, which strives to smooth while retaining all relevant detail. For the fiber tract mapping, a smooth 3D vector field indicating the dominant anisotropic direction at each spatial location is computed from the smoothed data. Neuronal fibers are then traced by calculating the integral curves of this vector field. Results are expressed using three modes of visualization: (1) Line integral convolution produces an oriented texture which shows fiber pathways in a planar slice of the data. (2) A streamtube map is generated to present a 3D view of fiber tracts. Additional information, such as degree of anisotropy, can be encoded in the tube radius, or by using color. (3) A particle system form of visualization is also presented. This mode of display allows for interactive exploration of fiber connectivity with no additional preprocessing.

An error analysis of white matter tractography methods: synthetic diffusion tensor field simulations

White matter tractography using diffusion tensor MR images is a promising method for estimating the pathways of white matter tracts in the human brain. The success of this method ultimately depends upon the accuracy of the white matter tractography algorithms. In this study, a Monte Carlo simulation was used to investigate the impact of SNR, tensor anisotropy, and diffusion tensor encoding directions on the accuracy of six tractography algorithms. The accuracy was assessed in straight and curved tracts and tract geometries with divergence properties. In general, the tract dispersion increased with distance and decreased with SNR and anisotropy. The tract orientation with respect to the encoding scheme also influenced tract dispersion. Divergent tract geometries increased tract dispersion, whereas convergent tract geometries reduced dispersion. Analytic models of tract dispersion were constructed as a function of the tract distance, SNR, eigenvalues of the tracts, voxel size, and the relationship between the tract direction and the diffusion tensor encoding directions. In certain cases, the mean tract trajectory was found to deviate from the ideal pathway for curved trajectories. Analytical models of mean displacement were constructed as a function of the curvature, tract distance, step size, and tensor eigenvalues. These models may be used in future studies to assess the level of confidence associated with a tractography result.

Nonrigid registration of 3D tensor medical data

New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MRI, require general representations for the development of automated algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data using local matching. The paper extends the usual concept of similarity between two pieces of data to be matched, commonly used with scalar (intensity) data, to the general tensor case. Our approach to registration is based on a multiresolution scheme, where the deformation field estimated in a coarser level is propagated to provide an initial deformation in the next finer one. In each level, local matching of areas with a high degree of local structure and subsequent interpolation are performed. Consequently, we provide an algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations. The interpolation step is carried out by means of the Kriging estimator, which provides a novel framework for the interpolation of sparse vector fields in medical applications. The feasibility of the approach is illustrated by results on synthetic and clinical data.

Towards inference of human brain connectivity from MR diffusion tensor data

This paper describes a method to infer the connectivity induced by white matter fibers in the living human brain. This method stems from magnetic resonance tensor imaging (DTI), a technique which gives access to fiber orientations. Given typical DTI spatial resolution, connectivity is addressed at the level of fascicles made up by a bunch of parallel fibers. We propose first an algorithm dedicated to fascicle tracking in a direction map inferred from diffusion data. This algorithm takes into account fan-shaped fascicle forks usual in actual white matter organization. Then, we propose a method of inferring a regularized direction map from diffusion data in order to improve the robustness of the tracking. The regularization stems from an analogy between white matter organization and spaghetti plates. Finally, we propose a study of the tracking behavior according to the weight given to the regularization and some examples of the tracking results with in vivo human brain data.