Publications by Year: 2012

PURPOSE: Diffusion magnetic resonance imaging (MRI) in combination with functional MRI promises a whole new vista for scientists to investigate noninvasively the structural and functional connectivity of the human brain-the human connectome, which had heretofore been out of reach. As with other imaging modalities, diffusion MRI data are inherently noisy and its acquisition time-consuming. Further, a faithful representation of the human connectome that can serve as a predictive model requires a robust and accurate data-analytic pipeline. The focus of this paper is on one of the key segments of this pipeline-in particular, the development of a sparse and optimal acquisition (SOA) design for diffusion MRI multiple-shell acquisition and beyond. METHODS: The authors propose a novel optimality criterion for sparse multiple-shell acquisition and quasimultiple-shell designs in diffusion MRI and a novel and effective semistochastic and moderately greedy combinatorial search strategy with simulated annealing to locate the optimum design or configuration. The goal of the optimality criteria is threefold: first, to maximize uniformity of the diffusion measurements in each shell, which is equivalent to maximal incoherence in angular measurements; second, to maximize coverage of the diffusion measurements around each radial line to achieve maximal incoherence in radial measurements for multiple-shell acquisition; and finally, to ensure maximum uniformity of diffusion measurement directions in the limiting case when all the shells are coincidental as in the case of a single-shell acquisition. The approach taken in evaluating the stability of various acquisition designs is based on the condition number and the A-optimal measure of the design matrix. RESULTS: Even though the number of distinct configurations for a given set of diffusion gradient directions is very large in general-e.g., in the order of 10(232) for a set of 144 diffusion gradient directions, the proposed search strategy was found to be effective in finding the optimum configuration. It was found that the square design is the most robust (i.e., with stable condition numbers and A-optimal measures under varying experimental conditions) among many other possible designs of the same sample size. Under the same performance evaluation, the square design was found to be more robust than the widely used sampling schemes similar to that of 3D radial MRI and of diffusion spectrum imaging (DSI). CONCLUSIONS: A novel optimality criterion for sparse multiple-shell acquisition and quasimultiple-shell designs in diffusion MRI and an effective search strategy for finding the best configuration have been developed. The results are very promising, interesting, and practical for diffusion MRI acquisitions.

Features of the diffusion-time dependence of the diffusion-weighted magnetic resonance imaging (MRI) signal provide a new contrast that could be altered by numerous biological processes and pathologies in tissue at microscopic length scales. An anomalous diffusion model, based on the theory of Brownian motion in fractal and disordered media, is used to characterize the temporal scaling (TS) characteristics of diffusion-related quantities, such as moments of the displacement and zero-displacement probabilities, in excised rat hippocampus specimens. To reduce the effect of noise in magnitude-valued MRI data, a novel numerical procedure was employed to yield accurate estimation of these quantities even when the signal falls below the noise floor. The power-law dependencies characterize the TS behavior in all regions of the rat hippocampus, providing unique information about its microscopic architecture. The relationship between the TS characteristics and diffusion anisotropy is investigated by examining the anisotropy of TS, and conversely, the TS of anisotropy. The findings suggest the robustness of the technique as well as the reproducibility of estimates. TS characteristics of the diffusion-weighted signals could be used as a new and useful marker of tissue microstructure.

The overall goal of this research is the design of statistical atlas models that can be created from normal subjects, but may generalize to be applicable to abnormal brains. We present a new style of joint modeling of fMRI, DTI, and structural MRI. Motivated by the fact that a white matter tract and related cortical areas are likely to displace together in the presence of a mass lesion (brain tumor), in this work we propose a rotation and translation invariant model that represents the spatial relationship between fiber tracts and anatomic and functional landmarks. This landmark distance model provides a new basis for representation of fiber tracts and can be used for detection and prediction of fiber tracts based on landmarks. Our results indicate that the measured model is consistent across normal subjects, and thus suitable for atlas building. Our experiments demonstrate that the model is robust to displacement and missing data, and can be successfully applied to a small group of patients with mass lesions.

We present what we believe to be the first investigation into unbiased multi-subject registration of whole brain diffusion tractography of the white matter. To our knowledge, this is also the first entropy-based objective function applied to fiber tract registration. To define the probability of fiber trajectories for the computation of entropy, we take advantage of a pairwise fiber distance used as the basis for a Gaussian-like kernel. By employing several values of the kernel’s scale parameter, the method is inherently multi-scale. Results of experiments using synthetic and real datasets demonstrate the potential of the method for simultaneous joint registration of tractography.

Diffusion MRI measures micron scale displacement of water molecules, providing unique insight into microstructural tissue architecture. However, current practical image resolution is in the millimeter scale, and thus diffusivities from many tissue compartments are averaged in each voxel, reducing the sensitivity and specificity of the measurement to subtle pathologies. Recent studies have pointed out that eliminating the contribution of extracellular water increases the sensitivity of the diffusion measures to tissue architecture. Moreover, in brain imaging, estimation of the extracellular volume appears to indicate pathological processes such as atrophy, edema and neuroinflammation. Here we study the free-water method, which assumes a bi-tensor model. We add low b-value shells to a regular DTI acquisition and present methods to improve the estimation of the model parameters using the extra information. In addition, we define a Laplace-Beltrami regularization operator that further stabilizes the multi-shell estimation.

Diffusion MRI is used extensively to investigate changes in white matter microstructure related to brain development and pathology. Ageing, however, is also associated with significant white and grey matter loss which in turn can lead to cerebrospinal fluid (CSF) based partial volume artefacts in diffusion MRI metrics. This is especially problematic in regions prone to CSF contamination, such as the fornix and the genu of corpus callosum, structures that pass through or close to the ventricles respectively. The aim of this study was to model the effects of CSF contamination on diffusion MRI metrics, and to evaluate different post-acquisition strategies to correct for CSF-contamination: Controlling for whole brain volume and correcting on a voxel-wise basis using the Free Water Elimination (FWE) approach. Using the fornix as an exemplar of a structure prone to CSF-contamination, corrections were applied to tract-specific and voxel-based [tract based spatial statistics (TBSS)] analyses of empirical DT-MRI data from 39 older adults (53-93 years of age). In addition to significant age-related decreases in whole brain volume and fornix tissue volume fraction, age was also associated with a reduction in mean fractional anisotropy and increase in diffusivity metrics in the fornix. The experimental data agreed with the simulations in that diffusivity metrics (mean diffusivity, axial and radial diffusivity) were more prone to partial volume CSF-contamination errors than fractional anisotropy. After FWE-based voxel-by-voxel partial volume corrections, the significant positive correlations between age and diffusivity metrics, in particular with axial diffusivity, disappeared whereas the correlation with anisotropy remained. In contrast, correcting for whole brain volume had little effect in removing these spurious correlations. Our study highlights the importance of correcting for CSF-contamination partial volume effects in the structures of interest on a voxel-by-voxel basis prior to drawing inferences about underlying changes in white matter structures and have implications for the interpretation of many recent diffusion MRI results in ageing and disease.

The involvement of brain structures in specific cognitive functions is not straightforward. In order to characterize the brain micro-structural correlates of cognitive domains, 52 healthy subjects, age 25-82 years, completed a computerized neuropsychological battery and were scanned using magnetic resonance diffusion tensor imaging. Factor analysis of 44 different cognitive scores was performed, isolating three cognitive domains-executive function, information processing speed and memory. Partial correlation was conducted between DTI parameters and each of the three cognitive domains controlling for age and motor function. Regions showing significant correlations with cognitive domains are domain-specific and are consistent with previous knowledge. While executive function was correlated with diffusion tensor imaging (DTI) parameters in frontal white matter and in the superior longitudinal fasciculus, information processing speed was correlated with DTI parameters in the cingulum, corona radiata, inferior longitudinal fasciculus, parietal white matter and in the thalamus. Memory performance was correlated with DTI measures in temporal and frontal gray matter and white matter regions, including the cingulate cortex and the parahippocampus. Thus, inter-subject variability in cognitive performance and tissue morphology, as expressed by diffusion tensor magnetic resonance imaging, can be used to relate tissue microstructure with cognitive performance and to provide information to corroborate other functional localization techniques.

The accurate characterization of pore morphology is of great interest in a wide range of scientific disciplines. Conventional single-pulsed field gradient (s-PFG) diffusion MR can yield compartmental size and shape only when compartments are coherently ordered using q-space approaches that necessitate strong gradients. However, double-PFG (d-PFG) methodology can provide novel microstructural information even when specimens are characterized by polydispersity in size and shape, and even when anisotropic compartments are randomly oriented. In this study, for the first time, we show that angular d-PFG experiments can be used to accurately measure cellular size and shape anisotropy of fixed yeast cells employing relatively weak gradients. The cell size, as measured by light microscopy, was found to be 5.32 ± 0.83 µm, whereas the results from noninvasive angular d-PFG experiments yielded a cell size of 5.46 ± 0.45 µm. Moreover, the low compartment shape anisotropy of the cells could be inferred from experiments conducted at long mixing times. Finally, similar experiments were conducted in a phantom comprising anisotropic compartments that were randomly oriented, showing that angular d-PFG MR provides novel information on compartment eccentricity that could not be accessed using conventional methods. Angular d-PFG methodology seems to be promising for the accurate estimation of compartment size and compartment shape anisotropy in heterogeneous systems in general, and biological cells and tissues in particular.

We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and functional connectivity and present an intuitive extension to population studies. We employ the EM algorithm to estimate the model parameters by maximizing the data likelihood. The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe in schizophrenia. We further establish that our model learns predictive differences between the control and clinical populations, and that combining the two modalities yields better results than considering each one in isolation.

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.