Publications by Year: 2010

2010
Thomas BT Yeo, Mert R Sabuncu, Tom Vercauteren, Nicholas Ayache, Bruce Fischl, and Polina Golland. 2010. “Spherical demons: fast diffeomorphic landmark-free surface registration.” IEEE Trans Med Imaging, 29, 3, Pp. 650-68.Abstract
We present the Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizors for the modified Demons objective function can be efficiently approximated on the sphere using iterative smoothing. Based on one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast. The Spherical Demons algorithm can also be modified to register a given spherical image to a probabilistic atlas. We demonstrate two variants of the algorithm corresponding to warping the atlas or warping the subject. Registration of a cortical surface mesh to an atlas mesh, both with more than 160 k nodes requires less than 5 min when warping the atlas and less than 3 min when warping the subject on a Xeon 3.2 GHz single processor machine. This is comparable to the fastest nondiffeomorphic landmark-free surface registration algorithms. Furthermore, the accuracy of our method compares favorably to the popular FreeSurfer registration algorithm. We validate the technique in two different applications that use registration to transfer segmentation labels onto a new image 1) parcellation of in vivo cortical surfaces and 2) Brodmann area localization in ex vivo cortical surfaces.
Yogesh Rathi, James G Malcolm, Oleg Michailovich, Carl-Fredrik Westin, Martha E Shenton, and Sylvain Bouix. 2010. “Tensor kernels for simultaneous fiber model estimation and tractography.” Magn Reson Med, 64, 1, Pp. 138-48.Abstract
This paper proposes a novel framework for joint orientation distribution function estimation and tractography based on a new class of tensor kernels. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the measured signal or estimated fiber orientation. In this work, fiber tracking is formulated as recursive estimation: at each step of tracing the fiber, the current estimate of the orientation distribution function is guided by the previous. To do this, second-and higher-order tensor-based kernels are employed. A weighted mixture of these tensor kernels is used for representing crossing and branching fiber structures. While tracing a fiber, the parameters of the mixture model are estimated based on the orientation distribution function at that location and a smoothness term that penalizes deviation from the previous estimate along the fiber direction. This ensures smooth estimation along the direction of propagation of the fiber. In synthetic experiments, using a mixture of two and three components it is shown that this approach improves the angular resolution at crossings. In vivo experiments using two and three components examine the corpus callosum and corticospinal tract and confirm the ability to trace through regions known to contain such crossing and branching.
Ayelet Dominitz and Allen Tannenbaum. 2010. “Texture mapping via optimal mass transport.” IEEE Trans Vis Comput Graph, 16, 3, Pp. 419-33.Abstract
In this paper, we present a novel method for texture mapping of closed surfaces. Our method is based on the technique of optimal mass transport (also known as the "earth-mover's metric"). This is a classical problem that concerns determining the optimal way, in the sense of minimal transportation cost, of moving a pile of soil from one site to another. In our context, the resulting mapping is area preserving and minimizes angle distortion in the optimal mass sense. Indeed, we first begin with an angle-preserving mapping (which may greatly distort area) and then correct it using the mass transport procedure derived via a certain gradient flow. In order to obtain fast convergence to the optimal mapping, we incorporate a multiresolution scheme into our flow. We also use ideas from discrete exterior calculus in our computations.
Lioz Etgar, Arie Nakhmani, Allen Tannenbaum, Efrat Lifshitz, and Rina Tannenbaum. 2010. “Trajectory control of PbSe-gamma-Fe2O3 nanoplatforms under viscous flow and an external magnetic field.” Nanotechnology, 21, 17, Pp. 175702.Abstract
The flow behavior of nanostructure clusters, consisting of chemically bonded PbSe quantum dots and magnetic gamma-Fe(2)O(3) nanoparticles, has been investigated. The clusters are regarded as model nanoplatforms with multiple functionalities, where the gamma-Fe(2)O(3) magnets serve as transport vehicles, manipulated by an external magnetic field gradient, and the quantum dots act as fluorescence tags within an optical window in the near-infrared regime. The clusters' flow was characterized by visualizing their trajectories within a viscous fluid (mimicking a blood stream), using an optical imaging method, while the trajectory pictures were analyzed by a specially developed processing package. The trajectories were examined under various flow rates, viscosities and applied magnetic field strengths. The results revealed a control of the trajectories even at low magnetic fields (<1 T), validating the use of similar nanoplatforms as active targeting constituents in personalized medicine.
Vandana Mohan, Ganesh Sundaramoorthi, and Allen Tannenbaum. 2010. “Tubular surface segmentation for extracting anatomical structures from medical imagery.” IEEE Trans Med Imaging, 29, 12, Pp. 1945-58.Abstract
This work provides a model for tubular structures, and devises an algorithm to automatically extract tubular anatomical structures from medical imagery. Our model fits many anatomical structures in medical imagery, in particular, various fiber bundles in the brain (imaged through diffusion-weighted magnetic resonance (DW-MRI)) such as the cingulum bundle, and blood vessel trees in computed tomography angiograms (CTAs). Extraction of the cingulum bundle is of interest because of possible ties to schizophrenia, and extracting blood vessels is helpful in the diagnosis of cardiovascular diseases. The tubular model we propose has advantages over many existing approaches in literature: fewer degrees-of-freedom over a general deformable surface hence energies defined on such tubes are less sensitive to undesirable local minima, and the tube (in 3-D) can be naturally represented by a 4-D curve (a radius function and centerline), which leads to computationally less costly algorithms and has the advantage that the centerline of the tube is obtained without additional effort. Our model also generalizes to tubular trees, and the extraction algorithm that we design automatically detects and evolves branches of the tree. We demonstrate the performance of our algorithm on 20 datasets of DW-MRI data and 32 datasets of CTA, and quantify the results of our algorithm when expert segmentations are available.
Thomas Walter, David W Shattuck, Richard Baldock, Mark E Bastin, Anne E Carpenter, Suzanne Duce, Jan Ellenberg, Adam Fraser, Nicholas Hamilton, Steve Pieper, Mark A Ragan, Jurgen E Schneider, Pavel Tomancak, and Jean-Karim Hériché. 2010. “Visualization of image data from cells to organisms.” Nat Methods, 7, 3 Suppl, Pp. S26-41.Abstract
Advances in imaging techniques and high-throughput technologies are providing scientists with unprecedented possibilities to visualize internal structures of cells, organs and organisms and to collect systematic image data characterizing genes and proteins on a large scale. To make the best use of these increasingly complex and large image data resources, the scientific community must be provided with methods to query, analyze and crosslink these resources to give an intuitive visual representation of the data. This review gives an overview of existing methods and tools for this purpose and highlights some of their limitations and challenges.
Anna Custo, David A Boas, Daisuke Tsuzuki, Ippeita Dan, Rickson Mesquita, Bruce Fischl, Eric WL Grimson, and Williams Wells. 2010. “Anatomical Atlas-guided Diffuse Optical Tomography of Brain Activation.” Neuroimage, 49, 1, Pp. 561-7.Abstract

We describe a neuroimaging protocol that utilizes an anatomical atlas of the human head to guide diffuse optical tomography of human brain activation. The protocol is demonstrated by imaging the hemodynamic response to median-nerve stimulation in three healthy subjects, and comparing the images obtained using a head atlas with the images obtained using the subject-specific head anatomy. The results indicate that using the head atlas anatomy it is possible to reconstruct the location of the brain activation to the expected gyrus of the brain, in agreement with the results obtained with the subject-specific head anatomy. The benefits of this novel method derive from eliminating the need for subject-specific head anatomy and thus obviating the need for a subject-specific MRI to improve the anatomical interpretation of diffuse optical tomography images of brain activation.

Yogesh Rathi, James Malcolm, Oleg Michailovich, Jill Goldstein, Larry Seidman, Robert W McCarley, Carl-Fredrik Westin, and Martha E Shenton. 2010. “Biomarkers for Identifying First Episode Schizophrenia Patients using Diffusion Weighted Imaging.” Med Image Comput Comput Assist Interv, 13, Pt 1, Pp. 657-65.Abstract

Recent advances in diffusion weighted MR imaging (dMRI) has made it a tool of choice for investigating white matter abnormalities of the brain and central nervous system. In this work, we design a system that detects abnormal features (biomarkers) of first-episode schizophrenia patients and then classifies them using these features. We use two different models of the dMRI data, namely, spherical harmonics and the two-tensor model. The algorithm works by first computing several diffusion measures from each model. An affine-invariant representation of each subject is then computed, thus avoiding the need for registration. This representation is used within a kernel based feature selection algorithm to determine the biomarkers that are statistically different between the two populations. Confirmation of how well these biomarkers identify each population is obtained by using several classifiers such as, k-nearest neighbors, Parzen window classifier, and support vector machines to separate 21 first-episode patients from 20 age-matched normal controls. Classification results using leave-many-out cross-validation scheme are given for each representation. This algorithm is a first step towards early detection of schizophrenia.

Thomas J Whitford, Marek Kubicki, Jason S Schneiderman, Lauren J O'Donnell, Rebecca King, Jorge L Alvarado, Usman Khan, Douglas Markant, Paul G Nestor, Margaret Niznikiewicz, Robert W McCarley, Carl-Fredrik Westin, and Martha E Shenton. 2010. “Corpus Callosum Abnormalities and their Association with Psychotic Symptoms in Patients with Schizophrenia.” Biol Psychiatry, 68, 1, Pp. 70-7.Abstract

BACKGROUND: While the neuroanatomical underpinnings of the functional brain disconnectivity observed in patients with schizophrenia (SZ) remain elusive, white matter fiber bundles of the brain are a likely candidate, given that they represent the infrastructure for long-distance neural communication. METHODS: This study investigated for diffusion abnormalities in 19 patients with chronic SZ, relative to 19 matched control subjects, across tractography-defined segments of the corpus callosum. Diffusion-weighted images were acquired with 51 noncollinear gradients on a 3T scanner (1.7 mm isotropic voxels). The corpus callosum was extracted by means of whole-brain tractography and automated fiber clustering and was parcelled into six segments on the basis of fiber trajectories. The diffusion indexes of fractional anisotropy (FA) and mode were calculated for each segment. RESULTS: Relative to the healthy control subjects, the SZ patients exhibited mode increases in the parietal fibers, suggesting a relative absence of crossing fibers. Schizophrenia patients also exhibited FA reductions in the frontal fibers, which were underpinned by increases in radial diffusivity, consistent with myelin abnormalities. Significant correlations were observed between patients' degree of reality distortion and their FA and radial diffusivity, such that the most severely psychotic patients were the least abnormal in terms of their frontal fiber diffusivity. CONCLUSIONS: The SZ patients exhibited a variety of diffusion abnormalities in the corpus callosum, which were related to the severity of their psychotic symptoms. To the extent that diffusion abnormalities influence axonal transmission velocities, these results provide support for those theories that emphasize neural timing abnormalities in the etiology of schizophrenia.

Aristotle N Voineskos, Nancy J Lobaugh, Sylvain Bouix, Tarek K Rajji, Dielle Miranda, James L Kennedy, Benoit H Mulsant, Bruce G Pollock, and Martha E Shenton. 2010. “Diffusion Tensor Tractography Findings in Schizophrenia across the Adult Lifespan.” Brain, 133, Pt 5, Pp. 1494-504.Abstract

In healthy adult individuals, late life is a dynamic time of change with respect to the microstructural integrity of white matter tracts. Yet, elderly individuals are generally excluded from diffusion tensor imaging studies in schizophrenia. Therefore, we examined microstructural integrity of frontotemporal and interhemispheric white matter tracts in schizophrenia across the adult lifespan. Diffusion tensor imaging data from 25 younger schizophrenic patients (< or = 55 years), 25 younger controls, 25 older schizophrenic patients (> or = 56 years) and 25 older controls were analysed. Patients with schizophrenia in each group were individually matched to controls. Whole-brain tractography and clustering segmentation were employed to isolate white matter tracts. Groups were compared using repeated measures analysis of variance with 12 within-group measures of fractional anisotropy: (left and right) uncinate fasciculus, arcuate fasciculus, inferior longitudinal fasciculus, inferior occipito-frontal fasciculus, cingulum bundle, and genu and splenium of the corpus callosum. For each white matter tract, fractional anisotropy was then regressed against age in patients and controls, and correlation coefficients compared. The main effect of group (F(3,92) = 12.2, P < 0.001), and group by tract interactions (F(26,832) = 1.68, P = 0.018) were evident for fractional anisotropy values. Younger patients had significantly lower fractional anisotropy than younger controls (Bonferroni-corrected alpha = 0.0042) in the left uncinate fasciculus (t(48) = 3.7, P = 0.001) and right cingulum bundle (t(48) = 3.6, P = 0.001), with considerable effect size, but the older groups did not differ. Schizophrenic patients did not demonstrate accelerated age-related decline compared with healthy controls in any white matter tract. To our knowledge, this is the first study to examine the microstructural integrity of frontotemporal white matter tracts across the adult lifespan in schizophrenia. The left uncinate fasciculus and right cingulum bundle are disrupted in younger chronic patients with schizophrenia compared with matched controls, suggesting that these white matter tracts are related to frontotemporal disconnectivity. The absence of accelerated age-related decline, or differences between older community-dwelling patients and controls, suggests that these patients may possess resilience to white matter disruption.

Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. 2010. “An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem.” SIAM J Sci Comput, 32, 1, Pp. 197-211.Abstract
In this paper we present a new computationally efficient numerical scheme for the minimizing flow approach for the computation of the optimal L(2) mass transport mapping. In contrast to the integration of a time dependent partial differential equation proposed in [S. Angenent, S. Haker, and A. Tannenbaum, SIAM J. Math. Anal., 35 (2003), pp. 61-97], we employ in the present work a direct variational method. The efficacy of the approach is demonstrated on both real and synthetic data.
Lauren J O'Donnell, Carl-Fredrik Westin, Isaiah Norton, Stephen Whalen, Laura Rigolo, Ruth Propper, and Alexandra J Golby. 2010. “The Fiber Laterality Histogram: A New Way to Measure White Matter Asymmetry.” Med Image Comput Comput Assist Interv, 13, Pt 2, Pp. 225-32.Abstract
The quantification of brain asymmetries may provide biomarkers for presurgical localization of language function and can improve our understanding of neural structure-function relationships in health and disease. We propose a new method for studying the asymmetry of the white matter tracts in the entire brain, and we apply it to a preliminary study of normal subjects across the handedness spectrum. Methods for quantifying white matter asymmetry using diffusion MRI tractography have thus far been based on comparing numbers of fibers or volumes of a single fiber tract across hemispheres. We propose a generalization of such methods, where the "number of fibers" laterality measurement is extended to the entire brain using a soft fiber comparison metric. We summarize the distribution of fiber laterality indices over the whole brain in a histogram, and we measure properties of the distribution such as its skewness, median, and inter-quartile range. The whole-brain fiber laterality histogram can be measured in an exploratory fashion without hypothesizing asymmetries only in particular structures. We demonstrate an overall difference in white matter asymmetry in consistent- and inconsistent-handers: the skewness of the fiber laterality histogram is significantly different across handedness groups.
Peter Savadjiev, Yogesh Rathi, James G Malcolm, Martha E Shenton, and Carl-Fredrik Westin. 2010. “A Geometry-based Particle Filtering Approach to White Matter Tractography.” Med Image Comput Comput Assist Interv, 13, Pt 2, Pp. 233-40.Abstract

We introduce a fibre tractography framework based on a particle filter which estimates a local geometrical model of the underlying white matter tract, formulated as a 'streamline flow' using generalized helicoids. The method is not dependent on the diffusion model, and is applicable to diffusion tensor (DT) data as well as to high angular resolution reconstructions. The geometrical model allows for a robust inference of local tract geometry, which, in the context of the causal filter estimation, guides tractography through regions with partial volume effects. We validate the method on synthetic data and present results on two types in vivo data: diffusion tensors and a spherical harmonic reconstruction of the fibre orientation distribution function (fODF).

Antonio Tristán-Vega, Carl-Fredrik Westin, and Santiago Aja-Fernández. 2010. “A New Methodology for the Estimation of Fiber Populations in the White Matter of the Brain with the Funk-Radon Transform.” Neuroimage, 49, 2, Pp. 1301-15.Abstract

The Funk-Radon Transform (FRT) is a powerful tool for the estimation of fiber populations with High Angular Resolution Diffusion Imaging (HARDI). It is used in Q-Ball imaging (QBI), and other HARDI techniques such as the recent Orientation Probability Density Transform (OPDT), to estimate fiber populations with very few restrictions on the diffusion model. The FRT consists in the integration of the attenuation signal, sampled by the MRI scanner on the unit sphere, along equators orthogonal to the directions of interest. It is easily proved that this calculation is equivalent to the integration of the diffusion propagator along such directions, although a characteristic blurring with a Bessel kernel is introduced. Under a different point of view, the FRT can be seen as an efficient way to compute the angular part of the integral of the attenuation signal in the plane orthogonal to each direction of the diffusion propagator. In this paper, Stoke's theorem is used to prove that the FRT can in fact be used to compute accurate estimates of the true integrals defining the functions of interest in HARDI, keeping the diffusion model as little restrictive as possible. Varying the assumptions on the attenuation signal, we derive new estimators of fiber orientations, generalizing both Q-Balls and the OPDT. Extensive experiments with both synthetic and real data have been intended to show that the new techniques improve existing ones in many situations.

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