Publications by Year: 2006

CONTEXT: Structural brain abnormalities, including larger cerebrospinal fluid (CSF) volumes, have been observed in men diagnosed as having schizotypal personality disorder (SPD). OBJECTIVES: To determine whether women with SPD have abnormalities similar to those of men with SPD and to elucidate specific SPD regional volume deficits and symptom correlations. DESIGN: Naturalistic study. SETTING AND PARTICIPANTS: Thirty neuroleptic-naive women with SPD and 29 female control subjects, both recruited from the community. Participants were group matched for age, parental socioeconomic status, handedness, and IQ. INTERVENTIONS: A new segmentation method was applied to magnetic resonance images to automatically parcel the images into CSF, gray matter, and white matter. The neocortex was manually separated from subcortical and other nonneocortical structures. Voxel-based morphometry was applied to determine global and regional volume deficits. MAIN OUTCOME MEASURES: Left and right neocortical gray matter, white matter, and CSF relative volumes as well as clinical symptoms from the Structured Interview for Schizotypy and the Schizotypal Personality Questionnaire-Brief Version. RESULTS: Smaller left (3.84%) and right (3.83%) neocortical gray matter relative volumes associated with larger left (9.66%) and right (9.61%) sulcal CSF relative volumes were found in women with SPD compared with controls. Voxel-based morphometry showed that the neocortical deficits in SPD were especially prominent in the left superior and middle temporal gyri, left inferior parietal region with postcentral gyrus, and right superior frontal and inferior parietal gyri. In the SPD group, larger lateral ventricle volumes correlated with more severe symptoms on the Structured Interview for Schizotypy and the Schizotypal Personality Questionnaire-Brief Version. CONCLUSIONS: The smaller neocortical gray matter volume and larger sulcal CSF volume provide evidence of the brain basis of this personality disorder and emphasize the communality of brain abnormalities in the schizophrenia spectrum.

In this paper we propose a formal formulation for the estimation of Diffusion Tensors in the space of symmetric positive semidefinite (PSD) tensors. Traditionally, diffusion tensor model estimation has been carried out imposing tensor symmetry without constraints for negative eigenvalues. When diffusion weighted data does not follow the diffusion model,due to noise or signal drop, negative eigenvalues may arise. An estimation method that accounts for the positive definiteness is desirable to respect the underlying principle of diffusion. This paper proposes such an estimation method and provides a theoretical interpretation of the result. A closed-form solution is derived that is the optimal data-fit in the matrix 2-norm sense,removing the need for optimization-based tensor estimation.

In this paper we propose a formal formulation for the estimation of Diffusion Tensors in the space of symmetric positive semidefinite (PSD) tensors. Traditionally, diffusion tensor model estimation has been carried out imposing tensor symmetry without constraints for negative eigenvalues. When diffusion weighted data does not follow the diffusion model, due to noise or signal drop, negative eigenvalues may arise. An estimation method that accounts for the positive definiteness is desirable to respect the underlying principle of diffusion. This paper proposes such an estimation method and provides a theoretical interpretation of the result. A closed-form solution is derived that is the optimal data-fit in the matrix 2-norm sense, removing the need for optimization-based tensor estimation.

Unaliasing by Fourier-encoding the overlaps using the temporal dimension (UNFOLD) is a method to reduce the data acquisition burden in dynamic MRI. The method works by forcing aliased signals to behave in specific ways through time, so that these unwanted signals can be detected and removed. Unexpected events in time, such as displacements caused by breathing, have the potential to disturb the temporal strategy and may affect UNFOLD’s ability to suppress aliasing artifacts. This work presents an extension of the UNFOLD method to accommodate temporal encoding disruptions. While the main type of disruption considered here comes from respiratory motion, other types of disruption can be envisioned, such as departures from the usual UNFOLD k-space sampling scheme. This extended version of UNFOLD was incorporated into UNFOLD-sensitivity encoding (UNFOLD-SENSE), and should also be applicable to closely related methods such as temporal SENSE (TSENSE), k-t Broaduse Linear Acquisition Speed up Technique (k-t BLAST), and k-t SENSE. Five patients were imaged with a modified version of a myocardial-perfusion sequence, and UNFOLD was used either alone or in conjunction with SENSE to obtain an acceleration of 2.0 (in three patients) or 3.0 (in two patients). In both cases this extended version of UNFOLD was able to suppress artifacts caused by the presence of breathing motion.

Selective RF excitation is employed in magnetic resonance imaging (MRI) to achieve a variety of effects, such as slice selection. More elaborate transverse magnetization patterns can be realized via tailored RF excitation pulses, useful for example to image any specific region geometry within the field of view, or, to acquire non-Fourier encoded samples of the underlying magnetization distribution. In this manuscript, we review prior work on the combination of selective RF excitation with parallel MRI acquisition techniques. This combination can be used both to advantageously manipulate the numerical conditioning of the reconstruction problem, as well as to compact the information content of the acquired data so as to improve the achievable acceleration rate. With the latter application it is possible to also consider the acceleration provided by parallel imaging alone as a compaction of information content, which in certain cases can be used to reduce the length of the selective excitations. The main contribution of this review is to show how the combination of selective excitation with parallel imaging provides the latter an added flexibility that can be used to either enhance image quality, increase imaging speed, or both.

Poisson’s equation, a fundamental partial differential equation in classical physics, has a number of properties that are interesting for shape analysis. In particular, the equipotential sets of the solution graph become smoother as the potential increases. We use the displacement map, the length of the streamlines formed by the gradient field of the solution, to measure the "complexity" (or smoothness) of the equipotential sets, and study its behavior as the potential increases. We believe that this function complexity = f(potential), which we call the shape characteristic, is a very natural way to express shape. Robust algorithms are presented to compute the solution to Poisson’s equation, the displacement map, and the shape characteristic. We first illustrate our technique on two-dimensional synthetic examples and natural silhouettes. We then perform two shape analysis studies on three-dimensional neuroanatomical data extracted from magnetic resonance (MR) images of the brain. In the first study, we investigate changes in the caudate nucleus in Schizotypal Personality Disorder (SPD) and confirm previously published results on this structure. In the second study, we present a data set of caudate nuclei of premature infants with asymmetric white matter injury. Our method shows structural shape differences that volumetric measurements were unable to detect.

Individual white matter fibers cannot be resolved by current magnetic resonance (MR) technology. Many fibers of a fiber bundle will pass through an individual volume element (voxel). Individual visualized fiber tracts are thus the result of interpolation on a relatively coarse voxel grid, and an infinite number of them may be generated in a given volume by interpolation. This paper aims at creating a level set representation of a fiber bundle to describe this apparent continuum of fibers. It further introduces a coordinate system warped to the fiber bundle geometry, allowing for the definition of geometrically meaningful fiber bundle measures.

Diffusion tensor magnetic resonance imaging provides structural information about nerve fiber tissue. The first eigenvector of the diffusion tensor is aligned with the nerve fibers, i.e., longitudinally in the spinal cord. The underlying hypothesis of this study is that the presence of collateral nerve fibers running orthogonal to the longitudinal fibers results in an orderly arrangement of the second eigenvectors. Magnetic resonance diffusion tensor scans were performed with line scan diffusion imaging on a clinical MR scanner. Axial sections were scanned in a human cervical spinal cord specimen at 625 microm resolution and the cervical spinal cord of four normal volunteers at 1250 microm resolution. The spinal cord specimen was fixed and stained for later light microscopy of the collateral fiber architecture at 0.53 microm resolution. Diffusion measured by MR was found to be anisotropic for both white and gray matter areas of the spinal cord specimen; the average fractional anisotropy (FA) was 0.63 +/- 0.09 (diffusion eigenvalues lambda1 0.38 +/- 0.05 micros/mm2, lambda2 0.14 +/- 0.03 micros/mm2, lambda3 0.10 +/- 0.03 micros/mm2) in white matter and 0.27 +/- 0.04 (lambda1 0.36 +/- 0.04 micros/mm2, lambda2 0.28 +/- 0.03 micros/mm2, lambda3 0.21 +/- 0.04 micros/mm2 in gray matter. The normal-volunteer FA values were similar, i.e., 0.66 +/- 0.04 (lambda1 1.66 +/- 0.14 micros/mm2, lambda2 0.55 +/- 0.02 micros/mm2, lambda3 0.40 +/- 0.01 micros/mm2) in white matter and 0.35 +/- 0.03 (lambda1 1.14 +/- 0.07 micros/mm2, lambda2 0.70 +/- 0.03 micros/mm2, lambda3 0.58 +/- 0.02 micros/mm2) in gray matter. The first eigenvector pointed, as expected, in the longitudinal direction. The second eigenvector directions exhibited a striking arrangement, consistent with the distribution of interconnecting collateral nerve fibers discerned on the histology section. This finding was confirmed for the specimen by quantitative pixel-wise comparison of second eigenvector directions and collateral fiber directions assessed on light microscopy image data. Diffusion tensor MRI can reveal non-invasively and in great detail the intricate fiber architecture of the human spinal cord.

Two image reconstruction methods currently dominate parallel MR imaging: SENSE and GRAPPA. While both seek to reconstruct images from subsampled multi-channel MRI data, there exist fundamental differences between the two. In particular, SENSE reconstructs an image of the excited spin-density directly whereas GRAPPA reconstructs estimates of the fully sampled raw coil data and then combines them to obtain an image. In this work we show that these differences can be exploited such that each method can compliment the other. In the case of SENSE, which requires an estimate of the coil sensitivity map before reconstruction, one can use GRAPPA to improve the coil sensitivity estimates. Alternatively, using coil sensitivity estimates and the SENSE reconstruction equations, one can improve the GRAPPA reconstruction parameter estimation. Together, these approaches can provide higher image quality than either method alone.

This report introduces a novel method to characterize the diffusion-time dependence of the diffusion-weighted magnetic resonance (MR) signal in biological tissues. The approach utilizes the theory of diffusion in disordered media where two parameters, the random walk dimension and the spectral dimension, describe the evolution of the average propagators obtained from q-space MR experiments. These parameters were estimated, using several schemes, on diffusion MR spectroscopy data obtained from human red blood cell ghosts and nervous tissue autopsy samples. The experiments demonstrated that water diffusion in human tissue is anomalous, where the mean-square displacements vary slower than linearly with diffusion time. These observations are consistent with a fractal microstructure for human tissues. Differences observed between healthy human nervous tissue and glioblastoma samples suggest that the proposed methodology may provide a novel, clinically useful form of diffusion MR contrast.