Catálogo de publicaciones - libros

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.

We present a method for automatically finding correspondence in Diffusion Tensor Imaging (DTI) from deformable registration to a common atlas. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with diffeomorphic correspondence between each image. The registration image match metric uses a feature detector for thin fiber structures of white matter, and interpolation and averaging of diffusion tensors use the Riemannian symmetric space framework. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies and for building DTI population atlases.

A reoccurring theme in the diffusion tensor imaging literature is the per-voxel estimation of a symmetric 3 ×3 tensor describing the measured diffusion. In this work we attempt to generalize this approach by calculating 2 or 3 or up to diffusion tensors for each voxel. We show that our procedure can more accurately describe the diffusion particularly when crossing fibers or fiber-bundles are present in the datasets.

Head motion is a significant source of error in fMRI activation detection and a common approach is to apply 3D volumetric rigid body motion correction techniques. However, in 2D multislice fMRI, each slice may have a distinct set of motion parameters due to inter-slice motion. Here, we apply an automated mutual information based slice-to-volume rigid body registration technique on time series data synthesized from a T MRI brain dataset with simulated motion, functional activation, noise and geometric distortion. The map-slice-to-volume (MSV) technique was previously applied to patient data without ground truths for motion and activation regions. In this study, the activation images and area under the receiver operating characteristic curves for various time series datasets indicate that the MSV registration improves the activation detection capability when compared to results obtained from Statistical Parametric Mapping (SPM). The effect of temporal median filtering of motion parameters on activation detection performance was also investigated.

A new approach to group activation analysis in fMRI studies that test hypotheses focused on specific brain structures is presented and used to analyze hippocampal activation in a visual scene encoding study. The approach leverages the method [10] to normalize hippocampal anatomy and project intra-subject hippocampal activation maps into a common reference space, eliminating normalization errors inherent in whole-brain approaches and guaranteeing that peaks detected in the random effects activation map are indeed associated with the hippocampus. When applied to real fMRI data, the method detects more significant hippocampal activation than the established whole-brain method.

Functional Magnetic Resonance imaging studies analyse sequences of brain volumes whose intensity changes predominantly reflect blood oxygenation level dependent (BOLD) effects. The most comprehensive signal model to date of the BOLD effect is formulated as a continuous-time system of nonlinear stochastic differential equations. In this paper we present a particle filtering method for the analysis of the BOLD system, and demonstrate it to be both accurate and robust in estimating the hidden physiological states including cerebral blood flow, cerebral blood volume, total deoxyhemoglobin content, and the flow inducing signal, from functional imaging data.

We present here a method that aims at producing representations of functional brain data on the cortical surface from functional MRI volumes. Such representations are required for subsequent cortical-based functional analysis. We propose a projection technique based on the definition, around each node of the grey/white matter interface mesh, of convolution kernels whose shape and distribution rely on the geometry of the local anatomy. For one anatomy, a set of convolution kernels is computed that can be used to project any functional data registered with this anatomy. The method is presented together with experiments on synthetic data and real statistical t-maps.

In this paper, we present algorithms to automatically detect and match landmark curves on cortical surfaces to get an optimized brain conformal parametrization. First, we propose an automatic landmark curve tracing method based on the principal directions of the local Weingarten matrix. Our algorithm obtains a hypothesized landmark curves using the Chan-Vese segmentation method, which solves a Partial Differential Equation (PDE) on a manifold with global conformal parameterization. Based on the global conformal parametrization of a cortical surface, our method adjusts the landmark curves iteratively on the spherical or rectangular parameter domain of the cortical surface along its principal direction field, using umbilic points of the surface as anchors. The landmark curves can then be mapped back onto the cortical surface. Experimental results show that the landmark curves detected by our algorithm closely resemble these manually labeled curves. Next, we applied these automatically labeled landmark curves to generate an optimized conformal parametrization of the cortical surface, in the sense that homologous features across subjects are caused to lie at the same parameter locations in a conformal grid. Experimental results show that our method can effectively help in automatically matching cortical surfaces across subjects.

We describe a new method to reconstruct human brain structures from 3D magnetic resonance brain images. Our method provides a fully automatic topology correction mechanism, thus avoiding tedious manual correction. Topological correctness is important because it is an essential prerequisite for brain atlas deformation and surface flattening. Our method uses an axis-aligned sweep through the volume to locate handles. Handles are detected by successively constructing and analyzing a directed graph. A multiple local region-growing process is used which simultaneously acts on the foreground and the background to isolate handles and tunnels. The sizes of handles and tunnels are measured, then handles are removed or tunnels filled based on their sizes. This process was used for 256 T1-weighted MR volumes.

This paper presents a method to improve the semi-automatic method for intensity inhomogeneity correction by Dawant et al. through introducing a fully automatic approach to reference points generation, which is based on order statistics and integrates information from the fine to coarse scale representations of the input image. The method has been validated and compared with two popular methods, N3 and BFC. Advantages of the proposed method are demonstrated.