Clinical DT-MRI estimation, smoothing, and fiber tracking with log-Euclidean metrics

Symmetry of the fornix using diffusion tensor imaging

PURPOSE

To: 1) Present fornix tractography in its entirety for 20 healthy individuals to assess variability. 2) Provide individual and groupwise whole tract diffusion parameter symmetry assessments prior to clinical application. 3) Compare whole tract diffusion parameter assessments with tract-based spatial statistics (TBSS).

MATERIALS AND METHODS

Diffusion tensor imaging (DTI) data were acquired on a 3T Siemens magnetic resonance imaging (MRI) system using a single-shot spin echo planar imaging (EPI) sequence. Individual fornix tractography was conducted and whole tract diffusion parameter symmetries assessed. Whole tract results were compared with asymmetry contrasts conducted with voxelwise statistical analysis of diffusion parameters using TBSS.

RESULTS

The fornix tract could be visualized in its entirety including the columns, body, crura, and fimbria. Contrary to the crus and body, there were some tractography inconsistencies of the columns and fimbria across subjects. Although whole tract diffusion parameter asymmetries were nonsignificant, fractional anisotropy (FA) values bordered on statistical significance (P = 0.052). Using TBSS, significant FA asymmetries were identified (P ≤ 0.01, corrected).

CONCLUSION

The findings demonstrate consistency of fornix tractography as well as some variability in the columns and fimbria. While parametric assessment demonstrates diffusion parameter symmetry, permutation-based TBSS analysis reveals significant FA asymmetries in the crura and fimbriae.

Dorsal and ventral pathways in language development

The dorsal and ventral information streams between inferior frontal and temporal language regions in the human brain are implemented by two fiber connections that consist of separable tracts. We compared the maturation of the two connections including their subcomponents in three different age groups: newborn infants, 7-year-old children, and adults. Our results reveal a maturational primacy of the ventral connection in the language network associating the temporal areas to the inferior frontal gyrus during early development, which is already in place at birth. Likewise, a dorsal pathway from the temporal cortex to the premotor cortex is observable at this early age. This is in contrast to the dorsal pathway to the inferior frontal gyrus which matures at later stages in development and might play a role in more complex language functions.

Image corruption detection in diffusion tensor imaging for post-processing and real-time monitoring

Due to the high sensitivity of diffusion tensor imaging (DTI) to physiological motion, clinical DTI scans often suffer a significant amount of artifacts. Tensor-fitting-based, post-processing outlier rejection is often used to reduce the influence of motion artifacts. Although it is an effective approach, when there are multiple corrupted data, this method may no longer correctly identify and reject the corrupted data. In this paper, we introduce a new criterion called "corrected Inter-Slice Intensity Discontinuity" (cISID) to detect motion-induced artifacts. We compared the performance of algorithms using cISID and other existing methods with regard to artifact detection. The experimental results show that the integration of cISID into fitting-based methods significantly improves the retrospective detection performance at post-processing analysis. The performance of the cISID criterion, if used alone, was inferior to the fitting-based methods, but cISID could effectively identify severely corrupted images with a rapid calculation time. In the second part of this paper, an outlier rejection scheme was implemented on a scanner for real-time monitoring of image quality and reacquisition of the corrupted data. The real-time monitoring, based on cISID and followed by post-processing, fitting-based outlier rejection, could provide a robust environment for routine DTI studies.

Long-term functional outcomes and correlation with regional brain connectivity by MRI diffusion tractography metrics in a near-term rabbit model of intrauterine growth restriction

BACKGROUND

Intrauterine growth restriction (IUGR) affects 5-10% of all newborns and is associated with increased risk of memory, attention and anxiety problems in late childhood and adolescence. The neurostructural correlates of long-term abnormal neurodevelopment associated with IUGR are unknown. Thus, the aim of this study was to provide a comprehensive description of the long-term functional and neurostructural correlates of abnormal neurodevelopment associated with IUGR in a near-term rabbit model (delivered at 30 days of gestation) and evaluate the development of quantitative imaging biomarkers of abnormal neurodevelopment based on diffusion magnetic resonance imaging (MRI) parameters and connectivity.

METHODOLOGY

At +70 postnatal days, 10 cases and 11 controls were functionally evaluated with the Open Field Behavioral Test which evaluates anxiety and attention and the Object Recognition Task that evaluates short-term memory and attention. Subsequently, brains were collected, fixed and a high resolution MRI was performed. Differences in diffusion parameters were analyzed by means of voxel-based and connectivity analysis measuring the number of fibers reconstructed within anxiety, attention and short-term memory networks over the total fibers.

PRINCIPAL FINDINGS

The results of the neurobehavioral and cognitive assessment showed a significant higher degree of anxiety, attention and memory problems in cases compared to controls in most of the variables explored. Voxel-based analysis (VBA) revealed significant differences between groups in multiple brain regions mainly in grey matter structures, whereas connectivity analysis demonstrated lower ratios of fibers within the networks in cases, reaching the statistical significance only in the left hemisphere for both networks. Finally, VBA and connectivity results were also correlated with functional outcome.

CONCLUSIONS

The rabbit model used reproduced long-term functional impairments and their neurostructural correlates of abnormal neurodevelopment associated with IUGR. The description of the pattern of microstructural changes underlying functional defects may help to develop biomarkers based in diffusion MRI and connectivity analysis.

Diffusion weighted image denoising using overcomplete local PCA

Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.

Characterizing humans on Riemannian manifolds

In surveillance applications, head and body orientation of people is of primary importance for assessing many behavioral traits. Unfortunately, in this context people are often encoded by a few, noisy pixels so that their characterization is difficult. We face this issue, proposing a computational framework which is based on an expressive descriptor, the covariance of features. Covariances have been employed for pedestrian detection purposes, actually a binary classification problem on Riemannian manifolds. In this paper, we show how to extend to the multiclassification case, presenting a novel descriptor, named weighted array of covariances, especially suited for dealing with tiny image representations. The extension requires a novel differential geometry approach in which covariances are projected on a unique tangent space where standard machine learning techniques can be applied. In particular, we adopt the Campbell-Baker-Hausdorff expansion as a means to approximate on the tangent space the genuine (geodesic) distances on the manifold in a very efficient way. We test our methodology on multiple benchmark datasets, and also propose new testing sets, getting convincing results in all the cases.

Logical foundations and fast implementation of probabilistic tractography

Although tractography can noninvasively map axonal pathways, current approaches are typically incomplete or computationally intensive. Fast, complete maps may serve as a useful clinical tool for assessing neurological disorders stemming from pathological anatomical connections such as epilepsy. We re-frame tractography in terms of logic and conditional probabilities. The formalism inherently includes global constraints and can compute connections between any two arbitrary regions of the brain. The formalism also lends itself to a fast implementation using standard partial differential equation solvers, which makes whole-brain probabilistic maps of anatomical connectivity feasible. We demonstrate results of our implementation on in vivo data and show that it outperforms Monte Carlo approaches in both computation time and identification of pathways.

Connectivity within the primary motor cortex: a DTI tractography study

PURPOSE

Because of the motor function of the precentral area, the connections of the primary motor cortex by white matter fiber bundles have been widely studied in diffusion tensor imaging (DTI). Nevertheless, the connections within the primary motor cortex have yet to be explored. We have studied the connectivity between the different regions of the precentral gyrus in a population of subjects.

METHODS

Based on T1 magnetic resonance imaging (MRI) and on individual sulco-gyral anatomy, we defined a parcellation of the right and the left precentral gyri in 20 healthy subjects (10 right-handers; 10 left-handers). This parcellation gave us the opportunity to study MRI tracks reconstructed by tractography within the precentral gyrus and to compare these connections across subjects. We also performed a classical dissection of post-mortem brain tissue to isolate this pattern of connectivity.

RESULTS

We showed MRI tracks connecting the different parts of the same precentral gyrus. This result was reproducible and was found in the left and right hemispheres of the 20 subjects. A quantitative description of the bilateral distribution of the MRI tracks was performed, based on statistical analysis and asymmetry indices, to compare asymmetry and handedness.

CONCLUSIONS

To the best of our knowledge, this pattern of connectivity has never before been detailed in the literature. Its functional meaning remains to be determined, which requires further study.

Patient-Specific Models of Cardiac Biomechanics

Patient-specific models of cardiac function have the potential to improve diagnosis and management of heart disease by integrating medical images with heterogeneous clinical measurements subject to constraints imposed by physical first principles and prior experimental knowledge. We describe new methods for creating three-dimensional patient-specific models of ventricular biomechanics in the failing heart. Three-dimensional bi-ventricular geometry is segmented from cardiac CT images at end-diastole from patients with heart failure. Human myofiber and sheet architecture is modeled using eigenvectors computed from diffusion tensor MR images from an isolated, fixed human organ-donor heart and transformed to the patient-specific geometric model using large deformation diffeomorphic mapping. Semi-automated methods were developed for optimizing the passive material properties while simultaneously computing the unloaded reference geometry of the ventricles for stress analysis. Material properties of active cardiac muscle contraction were optimized to match ventricular pressures measured by cardiac catheterization, and parameters of a lumped-parameter closed-loop model of the circulation were estimated with a circulatory adaptation algorithm making use of information derived from echocardiography. These components were then integrated to create a multi-scale model of the patient-specific heart. These methods were tested in five heart failure patients from the San Diego Veteran's Affairs Medical Center who gave informed consent. The simulation results showed good agreement with measured echocardiographic and global functional parameters such as ejection fraction and peak cavity pressures.

Anisotropic phantom measurements for quality assured use of diffusion tensor imaging in clinical practice

BACKGROUND

Diffusion-weighted magnetic resonance imaging (MRI) is being increasingly applied in clinical practice, for example in neuronavigation and in modern radiation treatment planning. Quality assurance (QA) is therefore important to avoid clinical errors.

PURPOSE

To compare four analytical programs and a neuronavigation tool to evaluate our in-house diffusion-weighted imaging protocol in order to be able to implement diffusion tensor imaging (DTI) into clinical practice.

MATERIAL AND METHODS

A phantom containing crossing fibers was used for the QA. Fiber tracking and fractional anisotropy (FA) analyses were performed, and the geometrical resolution was verified using the phantom.

RESULTS

FA results were reproducible within each program and no significant differences between programs were observed. Also, no significant differences in FA values were found when comparing the results between the four software programs. Geometrical resolution of the anatomical data-set was satisfactory; however the crossing of the fibers was not accurately represented by three of the four programs.

CONCLUSION

Phantom QA is necessary before using DTI for novel procedures to identify the uncertainties associated with DTI data. It is important to remember that the results are software-dependent and that representation of the tracts may vary between software products. We therefore recommend caution with regard to the application of fiber tracking results intraoperatively when dealing with abnormal fiber tract anatomy.

Diffusion imaging quality control via entropy of principal direction distribution

Diffusion MR imaging has received increasing attention in the neuroimaging community, as it yields new insights into the microstructural organization of white matter that are not available with conventional MRI techniques. While the technology has enormous potential, diffusion MRI suffers from a unique and complex set of image quality problems, limiting the sensitivity of studies and reducing the accuracy of findings. Furthermore, the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts, reduced signal-to-noise ratio (SNR), and increased proneness to a wide variety of artifacts, including eddy-current and motion artifacts, "venetian blind" artifacts, as well as slice-wise and gradient-wise inconsistencies. Such artifacts mandate stringent Quality Control (QC) schemes in the processing of diffusion MRI data. Most existing QC procedures are conducted in the DWI domain and/or on a voxel level, but our own experiments show that these methods often do not fully detect and eliminate certain types of artifacts, often only visible when investigating groups of DWI's or a derived diffusion model, such as the most-employed diffusion tensor imaging (DTI). Here, we propose a novel regional QC measure in the DTI domain that employs the entropy of the regional distribution of the principal directions (PD). The PD entropy quantifies the scattering and spread of the principal diffusion directions and is invariant to the patient's position in the scanner. High entropy value indicates that the PDs are distributed relatively uniformly, while low entropy value indicates the presence of clusters in the PD distribution. The novel QC measure is intended to complement the existing set of QC procedures by detecting and correcting residual artifacts. Such residual artifacts cause directional bias in the measured PD and here called dominant direction artifacts. Experiments show that our automatic method can reliably detect and potentially correct such artifacts, especially the ones caused by the vibrations of the scanner table during the scan. The results further indicate the usefulness of this method for general quality assessment in DTI studies.

Effects of DTI spatial normalization on white matter tract reconstructions

Major white matter (WM) pathways in the brain can be reconstructed in vivo using tractography on diffusion tensor imaging (DTI) data. Performing tractography using the native DTI data is often considered to produce more faithful results than performing it using the spatially normalized DTI obtained using highly non-linear transformations. However, tractography in the normalized DTI is playing an increasingly important role in population analyses of the WM. In particular, the emerging tract specific analyses (TSA) can benefit from tractography in the normalized DTI for statistical parametric mapping in specific WM pathways. It is well known that the preservation of tensor orientations at the individual voxel level is enforced in tensor based registrations. However small reorientation errors at individual voxel level can accumulate and could potentially affect the tractography results adversely. To our knowledge, there has been no study investigating the effects of normalization on consistency of tractography that demands non-local preservation of tensor orientations which is not explicitly enforced in typical DTI spatial normalization routines. This study aims to evaluate and compare tract reconstructions obtained using normalized DTI against those obtained using native DTI. Although tractography results have been used to measure and influence the quality of spatial normalization, the presented study addresses a distinct question: whether non-linear spatial normalization preserves even long-range anatomical connections obtained using tractography for accurate reconstructions of pathways. Our results demonstrate that spatial normalization of DTI data does preserve tract reconstructions of major WM pathways and does not alter the variance (individual differences) of their macro and microstructural properties. This suggests one can extract quantitative and shape properties efficiently from the tractography data in the normalized DTI for performing population statistics on major WM pathways.

Longitudinal change detection: inference on the diffusion tensor along white matter pathways

Diffusion weighted magnetic resonance imaging (DW-MRI) makes it possible to probe brain connections in vivo. This paper presents a change detection framework that relies on white matter pathways with application to neuromyelitis optica (NMO). The objective is to detect local or global fiber diffusion property modifications between two longitudinal DW-MRI acquisitions of a patient. To this end, we develop two frameworks based on statistical tests on tensor eigenvalues to detect local or global changes along the white matter pathways: a pointwise test that compares tensor populations extracted in bundles cross sections and a fiberwise test that compares paired tensors along all the fiber bundles. Experiments on both synthetic and real data highlight the benefit of considering fiber based statistical tests compared to standard voxelwise strategies.

A robust variational approach for simultaneous smoothing and estimation of DTI

Estimating diffusion tensors is an essential step in many applications - such as diffusion tensor image (DTI) registration, segmentation and fiber tractography. Most of the methods proposed in the literature for this task are not simultaneously statistically robust and feature preserving techniques. In this paper, we propose a novel and robust variational framework for simultaneous smoothing and estimation of diffusion tensors from diffusion MRI. Our variational principle makes use of a recently introduced total Kullback-Leibler (tKL) divergence for DTI regularization. tKL is a statistically robust dissimilarity measure for diffusion tensors, and regularization by using tKL ensures the symmetric positive definiteness of tensors automatically. Further, the regularization is weighted by a non-local factor adapted from the conventional non-local means filters. Finally, for the data fidelity, we use the nonlinear least-squares term derived from the Stejskal-Tanner model. We present experimental results depicting the positive performance of our method in comparison to competing methods on synthetic and real data examples.

Joint T1 and brain fiber log-demons registration using currents to model geometry

We present an extension of the diffeomorphic Geometric Demons algorithm which combines the iconic registration with geometric constraints. Our algorithm works in the log-domain space, so that one can efficiently compute the deformation field of the geometry. We represent the shape of objects of interest in the space of currents which is sensitive to both location and geometric structure of objects. Currents provides a distance between geometric structures that can be defined without specifying explicit point-to-point correspondences. We demonstrate this framework by registering simultaneously T1 images and 65 fiber bundles consistently extracted in 12 subjects and compare it against non-linear T1, tensor, and multi-modal T1 + Fractional Anisotropy (FA) registration algorithms. Results show the superiority of the Log-domain Geometric Demons over their purely iconic counterparts.

Improving DTI resolution from a single clinical acquisition: a statistical approach using spatial prior

Diffusion Tensor Imaging (DTI) provides us with valuable information about the white matter fibers and their arrangement in the brain. However, clinical DTI acquisitions are often low resolution, causing partial volume effects. In this paper, we propose a new high resolution tensor estimation method. This method makes use of the spatial correlation between neighboring voxels. Unlike some super-resolution algorithms, the proposed method does not require multiple acquisitions, thus it is better suited for clinical situations. The method relies on a maximum likelihood strategy for tensor estimation to optimally account for the noise and an anisotropic regularization prior to promote smoothness in homogeneous areas while respecting the edges. To the best of our knowledge, this is the first method to produce high resolution tensor images from a single low resolution acquisition. We demonstrate the efficiency of the method on synthetic low-resolution data and real clinical data. The results show statistically significant improvements in fiber tractography.

Improved diffusion imaging through SNR-enhancing joint reconstruction

Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal-to-noise ratio represent significant hurdles for many in vivo applications. This article presents a new approach to reduce noise while largely maintaining resolution in diffusion weighted images, using a statistical reconstruction method that takes advantage of the high level of structural correlation observed in typical datasets. Compared to existing denoising methods, the proposed method performs reconstruction directly from the measured complex k-space data, allowing for gaussian noise modeling and theoretical characterizations of the resolution and signal-to-noise ratio of the reconstructed images. In addition, the proposed method is compatible with many different models of the diffusion signal (e.g., diffusion tensor modeling and q-space modeling). The joint reconstruction method can provide significant improvements in signal-to-noise ratio relative to conventional reconstruction techniques, with a relatively minor corresponding loss in image resolution. Results are shown in the context of diffusion spectrum imaging tractography and diffusion tensor imaging, illustrating the potential of this signal-to-noise ratio-enhancing joint reconstruction approach for a range of different diffusion imaging experiments.

Parametric representation of multiple white matter fascicles from cube and sphere diffusion MRI

The characterization of the complex diffusion signal arising from the brain remains an open problem. Many representations focus on characterizing the global shape of the diffusion profile at each voxel and are limited to the assessment of connectivity. In contrast, Multiple Fascicle Models (MFM) seek to represent the contribution from each white matter fascicle and may be useful in the investigation of both white matter connectivity and diffusion properties of each individual fascicle. However, the most appropriate representation of multiple fascicles remains unclear. In particular, a multiple tensor representation of multiple fascicles has frequently been reported to be numerically challenging and unstable. We provide here the first analytical demonstration that when using a diffusion MRI acquisition with only one non-zero b-value, such as in conventional single-shell HARDI acquisition, a co-linearity in model parameters makes the precise model estimation impossible. Motivated by this theoretical result, we propose the novel CUSP (CUbe and SPhere) optimal acquisition scheme to achieve multiple non-zero b-values. It combines the gradients of a single-shell HARDI with gradients in its enclosing cube, in which varying b-values can be acquired by modulation of the gradient strength, without modifying the minimum echo time. Compared to a multi-shell HARDI acquisition, our scheme has significantly increased signal-to-noise ratio. We propose a novel estimation algorithm that enables efficient, robust and accurate estimation of the parameters of a multi-tensor model. In conjunction with a CUSP acquisition, it enables full estimation of the multi-tensor model. We present an evaluation of CUSP-MFM on both synthetic phantoms and invivo data. We report qualitative and quantitative experimental evaluations which demonstrate the ability of CUSP-MFM to characterize multiple fascicles from short duration acquisitions. CUSP-MFM enables rapid and effective investigation of multiple white matter fascicles, in both normal development and in disease and injury, in research and clinical practice.

Diffusion magnetic resonance imaging for Brainnetome: a critical review

Increasing evidence shows that the human brain is a highly self-organized system that shows attributes of small-worldness, hierarchy and modularity. The "connectome" was conceived several years ago to identify the underpinning physical connectivities of brain networks. The need for an integration of multi-spatial and -temporal approaches is becoming apparent. Therefore, the "Brainnetome" (brain-net-ome) project was proposed. Diffusion magnetic resonance imaging (dMRI) is a non-invasive way to study the anatomy of brain networks. Here, we review the principles of dMRI, its methodologies, and some of its clinical applications for the Brainnetome. Future research in this field is discussed.

Behavioral and neuroanatomical investigation of Highly Superior Autobiographical Memory (HSAM)

A single case study recently documented one woman's ability to recall accurately vast amounts of autobiographical information, spanning most of her lifetime, without the use of practiced mnemonics (Parker, Cahill, & McGaugh, 2006). The current study reports findings based on eleven participants expressing this same memory ability, now referred to as Highly Superior Autobiographical Memory (HSAM). Participants were identified and subsequently characterized based on screening for memory of public events. They were then tested for personal autobiographical memories as well as for memory assessed by laboratory memory tests. Additionally, whole-brain structural MRI scans were obtained. Results indicated that HSAM participants performed significantly better at recalling public as well as personal autobiographical events as well as the days and dates on which these events occurred. However, their performance was comparable to age- and sex-matched controls on most standard laboratory memory tests. Neuroanatomical results identified nine structures as being morphologically different from those of control participants. The study of HSAM may provide new insights into the neurobiology of autobiographical memory.

Individual functional ROI optimization via maximization of group-wise consistency of structural and functional profiles

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain.

Linking ordering in Broca's area to storage in left temporo-parietal regions: the case of sentence processing

In sentence processing, storage and ordering of the verb and its arguments (subject and object) are core tasks. Their cortical representation is a matter of ongoing debate, and it is unclear whether prefrontal activations in neuroimaging studies on sentence processing reflect the storage of arguments or their ordering. Moreover, it is unclear how storage during sentence processing relates to the neuroanatomy of storage outside the sentence processing domain. To tackle these questions, we crossed the factor "ordering" (subject-first vs. object-first German sentences) with the factor "storage" (one vs. four phrases intervene between the critical argument and the verb) in an auditory fMRI study. Ordering focally activated the left pars opercularis in Broca's area, while storage activated deep left temporo-parietal (TP) regions. Notably, left TP activation correlated with listener's digit span, while Broca's area activation did not. Furthermore, fractional anisotropy of listeners' left arcuate fasciculus/superior longitudinal fasciculus (AF/SLF) is shown to covary with the functional effect of increased storage demands at sites along the tract. Functionally, the results suggest that storage during sentence processing relies on TP regions, likely shared between sentence processing and other working memory-related tasks, while Broca's area appears as a distinct neural correlate of ordering. We conclude that the abstract notion of sentence processing can be captured by the interplay of concrete cognitive concepts such as ordering and storage.

ENTROPY BASED DTI QUALITY CONTROL VIA REGIONAL ORIENTATION DISTRIBUTION

Diffusion Tensor Imaging (DTI) has received increasing attention in the neuroimaging community. However, the complex Diffusion Weighted Images (DWI) acquisition protocol are prone to artifacts induced by motion and low signal-to-noise rations(SNRs). A rigorous quality control (QC) and error correction procedure is absolutely necessary for DTI data analysis. Most existing QC procedures are conducted in the DWI domain and/or on a voxel level, but our own experiments show that these methods often do not fully detect and eliminate certain types of artifacts. We propose a new regional, alignment-independent DTI-QC measure that is based in the DTI domain employing the entropy of the regional distribution of the principal directions. This new QC measurement is intended to complement the existing set of QC procedures by detecting and correcting residual artifacts. Experiments show that our automatic method can reliably detect and potentially correct such residual artifacts. The results indicate its usefulness for general quality assessment in DTI studies.

Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome

Intrauterine growth restriction (IUGR) due to placental insufficiency affects 5-10% of all pregnancies and it is associated with a wide range of short- and long-term neurodevelopmental disorders. Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. In recent years several studies have used magnetic resonance imaging (MRI) to demonstrate differences in brain structure in IUGR subjects, but the ability to use MRI for individual predictive purposes in IUGR is limited. Recent research suggests that MRI in vivo access to brain connectivity might have the potential to help understanding cognitive and neurodevelopment processes. Specifically, MRI based connectomics is an emerging approach to extract information from MRI data that exhaustively maps inter-regional connectivity within the brain to build a graph model of its neural circuitry known as brain network. In the present study we used diffusion MRI based connectomics to obtain structural brain networks of a prospective cohort of one year old infants (32 controls and 24 IUGR) and analyze the existence of quantifiable brain reorganization of white matter circuitry in IUGR group by means of global and regional graph theory features of brain networks. Based on global and regional analyses of the brain network topology we demonstrated brain reorganization in IUGR infants at one year of age. Specifically, IUGR infants presented decreased global and local weighted efficiency, and a pattern of altered regional graph theory features. By means of binomial logistic regression, we also demonstrated that connectivity measures were associated with abnormal performance in later neurodevelopmental outcome as measured by Bayley Scale for Infant and Toddler Development, Third edition (BSID-III) at two years of age. These findings show the potential of diffusion MRI based connectomics and graph theory based network characteristics for estimating differences in the architecture of neural circuitry and developing imaging biomarkers of poor neurodevelopment outcome in infants with prenatal diseases.

k-space and q-space: combining ultra-high spatial and angular resolution in diffusion imaging using ZOOPPA at 7 T

There is ongoing debate whether using a higher spatial resolution (sampling k-space) or a higher angular resolution (sampling q-space angles) is the better way to improve diffusion MRI (dMRI) based tractography results in living humans. In both cases, the limiting factor is the signal-to-noise ratio (SNR), due to the restricted acquisition time. One possible way to increase the spatial resolution without sacrificing either SNR or angular resolution is to move to a higher magnetic field strength. Nevertheless, dMRI has not been the preferred application for ultra-high field strength (7 T). This is because single-shot echo-planar imaging (EPI) has been the method of choice for human in vivo dMRI. EPI faces several challenges related to the use of a high resolution at high field strength, for example, distortions and image blurring. These problems can easily compromise the expected SNR gain with field strength. In the current study, we introduce an adapted EPI sequence in conjunction with a combination of ZOOmed imaging and Partially Parallel Acquisition (ZOOPPA). We demonstrate that the method can produce high quality diffusion-weighted images with high spatial and angular resolution at 7 T. We provide examples of in vivo human dMRI with isotropic resolutions of 1 mm and 800 μm. These data sets are particularly suitable for resolving complex and subtle fiber architectures, including fiber crossings in the white matter, anisotropy in the cortex and fibers entering the cortex.

Diffusion tensor imaging of diffuse axonal injury in a rat brain trauma model

Diffusion tensor imaging (DTI) was used to study traumatic brain injury. The impact-acceleration trauma model was used in rats. Here, in addition to diffusivities (mean, axial and radial), fractional anisotropy (FA) was used, in particular, as a parameter to characterize the cerebral tissue early after trauma. DTI was implemented at 7 T using fast spiral k-space sampling and the twice-refocused spin echo radiofrequency sequence for eddy current minimization. The method was carefully validated on different phantom measurements. DTI of a trauma group (n = 5), as well as a sham group (n = 5), was performed at different time points during 6 h following traumatic brain injury. Two cerebral regions, the cortex and corpus callosum, were analyzed carefully. A significant decrease in diffusivity in the trauma group versus the sham group was observed, suggesting the predominance of cellular edema in both cerebral regions. No significant FA change was detected in the cortex. In the corpus callosum of the trauma group, the FA indices were significantly lower. A net discontinuity in fiber reconstructions in the corpus callosum was observed by fiber tracking using DTI. Histological analysis using Hoechst, myelin basic protein and Bielschowsky staining showed fiber disorganization in the corpus callosum in the brains of the trauma group. On the basis of our histology results and the characteristics of the impact-acceleration model responsible for the presence of diffuse axonal injury, the detection of low FA caused by a drastic reduction in axial diffusivity and the presence of fiber disconnections of the DTI track in the corpus callosum were considered to be related to the presence of diffuse axonal injury.

Least squares for diffusion tensor estimation revisited: propagation of uncertainty with Rician and non-Rician signals

Least Squares (LS) and its minimum variance counterpart, Weighted Least Squares (WLS), have become very popular when estimating the Diffusion Tensor (DT), to the point that they are the standard in most of the existing software for diffusion MRI. They are based on the linearization of the Stejskal-Tanner equation by means of the logarithmic compression of the diffusion signal. Due to the Rician nature of noise in traditional systems, a certain bias in the estimation is known to exist. This artifact has been made patent through some experimental set-ups, but it is not clear how the distortion translates in the reconstructed DT, and how important it is when compared to the other source of error contributing to the Mean Squared Error (MSE) in the estimate, i.e. the variance. In this paper we propose the analytical characterization of log-Rician noise and its propagation to the components of the DT through power series expansions. We conclude that even in highly noisy scenarios the bias for log-Rician signals remains moderate when compared to the corresponding variance. Yet, with the advent of Parallel Imaging (pMRI), the Rician model is not always valid. We make our analysis extensive to a number of modern acquisition techniques through the study of a more general Non Central-Chi (nc-χ) model. Since WLS techniques were initially designed bearing in mind Rician noise, it is not clear whether or not they still apply to pMRI. An important finding in our work is that the common implementation of WLS is nearly optimal when nc-χ noise is considered. Unfortunately, the bias in the estimation becomes far more important in this case, to the point that it may nearly overwhelm the variance in given situations. Furthermore, we evidence that such bias cannot be removed by increasing the number of acquired gradient directions. A number of experiments have been conducted that corroborate our analytical findings, while in vivo data have been used to test the actual relevance of the bias in the estimation.

32-channel RF coil optimized for brain and cervical spinal cord at 3 T

Diffusion and functional magnetic resonance imaging of the spinal cord remain challenging due to the small cross-sectional size of the cord and susceptibility-related distortions. Although partially addressable through parallel imaging, few highly parallel array coils have been implemented for the cervical cord. Here, we developed a 32-channel coil that fully covers the brain and c-spine and characterized its performance in comparison with a commercially available head/neck/spine array. Image and temporal signal-to-noise ratio were, respectively, increased by 2× and 1.8× in the cervical cord. Averaged g-factors at 4× acceleration were lowered by 22% in the brain and by 39% in the spinal cord, enabling 1-mm isotropic R = 4 multi-echo magnetization prepared gradient echo of the full brain and c-spine in 3:20 min. Diffusion imaging of the cord at 0.6 × 0.6 × 5 mm(3) resolution and tractography of the full brain and c-spine at 1.7-mm isotropic resolution were feasible without noticeable distortion. Improvements of this nature potentially enhance numerous basic and clinical research studies focused on spinal and supraspinal regions.

Neural language networks at birth

The ability to learn language is a human trait. In adults and children, brain imaging studies have shown that auditory language activates a bilateral frontotemporal network with a left hemispheric dominance. It is an open question whether these activations represent the complete neural basis for language present at birth. Here we demonstrate that in 2-d-old infants, the language-related neural substrate is fully active in both hemispheres with a preponderance in the right auditory cortex. Functional and structural connectivities within this neural network, however, are immature, with strong connectivities only between the two hemispheres, contrasting with the adult pattern of prevalent intrahemispheric connectivities. Thus, although the brain responds to spoken language already at birth, thereby providing a strong biological basis to acquire language, progressive maturation of intrahemispheric functional connectivity is yet to be established with language exposure as the brain develops.

Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling

The estimation of covariance matrices is a crucial step in several statistical tasks. Especially when using few samples of a high dimensional representation of shapes, the standard maximum likelihood estimation (ML) of the covariance matrix can be far from the truth, is often rank deficient, and may lead to unreliable results. In this paper, we discuss regularization by prior knowledge using maximum a posteriori (MAP) estimates. We compare ML to MAP using a number of priors and to Tikhonov regularization. We evaluate the covariance estimates on both synthetic and real data, and we analyze the estimates' influence on a missing-data reconstruction task, where high resolution vertebra and cartilage models are reconstructed from incomplete and lower dimensional representations. Our results demonstrate that our methods outperform the traditional ML method and Tikhonov regularization.

Patient-specific modeling of dyssynchronous heart failure: a case study

The development and clinical use of patient-specific models of the heart is now a feasible goal. Models have the potential to aid in diagnosis and support decision-making in clinical cardiology. Several groups are now working on developing multi-scale models of the heart for understanding therapeutic mechanisms and better predicting clinical outcomes of interventions such as cardiac resynchronization therapy. Here we describe the methodology for generating a patient-specific model of the failing heart with a myocardial infarct and left ventricular bundle branch block. We discuss some of the remaining challenges in developing reliable patient-specific models of cardiac electromechanical activity, and identify some of the main areas for focusing future research efforts. Key challenges include: efficiently generating accurate patient-specific geometric meshes and mapping regional myofiber architecture to them; modeling electrical activation patterns based on cellular alterations in human heart failure, and estimating regional tissue conductivities based on clinically available electrocardiographic recordings; estimating unloaded ventricular reference geometry and material properties for biomechanical simulations; and parameterizing systemic models of circulatory dynamics from available hemodynamic measurements.

The architecture of cross-hemispheric communication in the aging brain: linking behavior to functional and structural connectivity

Contralateral recruitment remains a controversial phenomenon in both the clinical and normative populations. To investigate the neural correlates of this phenomenon, we explored the tendency for older adults to recruit prefrontal cortex (PFC) regions contralateral to those most active in younger adults. Participants were scanned with diffusion tensor imaging and functional magnetic rresonance imaging during a lateralized word matching task (unilateral vs. bilateral). Cross-hemispheric communication was measured behaviorally as greater accuracy for bilateral than unilateral trials (bilateral processing advantage [BPA]) and at the neural level by functional and structural connectivity between contralateral PFC. Compared with the young, older adults exhibited 1) greater BPAs in the behavioral task, 2) greater compensatory activity in contralateral PFC during the bilateral condition, 3) greater functional connectivity between contralateral PFC during bilateral trials, and 4) a positive correlation between fractional anisotropy in the corpus callosum and both the BPA and the functional connectivity between contralateral PFC, indicating that older adults' ability to distribute processing across hemispheres is constrained by white matter integrity. These results clarify how older adults' ability to recruit extra regions in response to the demands of aging is mediated by existing structural architecture, and how this architecture engenders corresponding functional changes that allow subjects to meet those task demands.

Reproducibility analysis of diffusion tensor indices and fiber architecture of human calf muscles in vivo at 1.5 Tesla in neutral and plantarflexed ankle positions at rest

PURPOSE

To investigate the reproducibility of diffusion tensor imaging (DTI) -derived indices and fiber architecture of calf muscles at 1.5 Tesla (T), to establish an imaging based method to confirm ankle position, and to compare fiber architecture at different ankle positions.

MATERIALS AND METHODS

Six subjects were imaged at 1.5T with the foot in neutral and plantarflexed positions. DTI indices were calculated in four muscle compartments (medial and lateral gastrocnemius [MG, LG], superficial and deep anterior tibialis [AT-S, AT-D]). Two subjects were scanned on 3 days to calculate the coefficient of variability (CV) and the repeatability coefficient (RC).

RESULTS

DTI indices were close to the values obtained in earlier 3T and 1.5T studies. Fractional anisotropy decreased significantly in the MG and increased significantly in the AT-S and AT-D compartments while fiber orientation with respect to the magnet Z-axis increased significantly in the MG and decreased significantly in the AT-S compartment with plantarflexion. The CV and RC for the DTI indices and fiber orientations were comparable to 3T studies. Fiber lengths and orientation angles in the MG matched corresponding measures from ultrasound studies.

CONCLUSION

DTI at 1.5T provides reproducible measures of diffusion indices and fiber architecture of calf muscle at different muscle lengths.

Wallerian degeneration after spinal cord lesions in cats detected with diffusion tensor imaging

One goal of in vivo neuroimaging is the detection of neurodegenerative processes and anatomical reorganizations after spinal cord (SC) injury. Non-invasive examination of white matter fibers in the living SC can be conducted using magnetic resonance diffusion-weighted imaging. However, this technique is challenging at the spinal level due to the small cross-sectional size of the cord and the presence of physiological motion and susceptibility artifacts. In this study, we acquired in vivo high angular resolution diffusion imaging (HARDI) data at 3T in cats submitted to partial SC injury. Cats were imaged before, 3 and 21 days after injury. Spatial resolution was enhanced to 1.5 × 1.5 × 1 mm(3) using super-resolution technique and distortions were corrected using the reversed gradient method. Tractography-derived regions of interest were generated in the dorsal, ventral, right and left quadrants, to evaluate diffusion tensor imaging (DTI) and Q-Ball imaging metrics with regards to their sensitivity in detecting primary and secondary lesions. A three-way ANOVA tested the effect of session (intact, D3, D21), cross-sectional region (left, right, dorsal and ventral) and rostrocaudal location. Significant effect of session was found for FA (P<0.001), GFA (P<0.05) and radial diffusivity (P<0.001). Post-hoc paired T-test corrected for multiple comparisons showed significant changes at the lesion epicenter (P<0.005). More interestingly, significant changes were also found several centimeters from the lesion epicenter at both 3 and 21 days. This decrease was specific to the type of fibers, i.e., rostrally to the lesion on the dorsal aspect of the cord and caudally to the lesion ipsilaterally, suggesting the detection of Wallerian degeneration.

Recent advances in diffusion MRI modeling: Angular and radial reconstruction

Recent advances in diffusion magnetic resonance image (dMRI) modeling have led to the development of several state of the art methods for reconstructing the diffusion signal. These methods allow for distinct features to be computed, which in turn reflect properties of fibrous tissue in the brain and in other organs. A practical consideration is that to choose among these approaches requires very specialized knowledge. In order to bridge the gap between theory and practice in dMRI reconstruction and analysis we present a detailed review of the dMRI modeling literature. We place an emphasis on the mathematical and algorithmic underpinnings of the subject, categorizing existing methods according to how they treat the angular and radial sampling of the diffusion signal. We describe the features that can be computed with each method and discuss its advantages and limitations. We also provide a detailed bibliography to guide the reader.

The neural architecture of the language comprehension network: converging evidence from lesion and connectivity analyses

While traditional models of language comprehension have focused on the left posterior temporal cortex as the neurological basis for language comprehension, lesion and functional imaging studies indicate the involvement of an extensive network of cortical regions. However, the full extent of this network and the white matter pathways that contribute to it remain to be characterized. In an earlier voxel-based lesion-symptom mapping analysis of data from aphasic patients (Dronkers et al., 2004), several brain regions in the left hemisphere were found to be critical for language comprehension: the left posterior middle temporal gyrus, the anterior part of Brodmann's area 22 in the superior temporal gyrus (anterior STG/BA22), the posterior superior temporal sulcus (STS) extending into Brodmann's area 39 (STS/BA39), the orbital part of the inferior frontal gyrus (BA47), and the middle frontal gyrus (BA46). Here, we investigated the white matter pathways associated with these regions using diffusion tensor imaging from healthy subjects. We also used resting-state functional magnetic resonance imaging data to assess the functional connectivity profiles of these regions. Fiber tractography and functional connectivity analyses indicated that the left MTG, anterior STG/BA22, STS/BA39, and BA47 are part of a richly interconnected network that extends to additional frontal, parietal, and temporal regions in the two hemispheres. The inferior occipito-frontal fasciculus, the arcuate fasciculus, and the middle and inferior longitudinal fasciculi, as well as transcallosal projections via the tapetum were found to be the most prominent white matter pathways bridging the regions important for language comprehension. The left MTG showed a particularly extensive structural and functional connectivity pattern which is consistent with the severity of the impairments associated with MTG lesions and which suggests a central role for this region in language comprehension.