RESUMEN
In this paper, we propose a novel method for correcting the geometric distortions in diffusion weighted images (DWI) obtained with echo planar imaging (EPI) protocol. Our EPI distortion correction approach employs a deformable registration framework with the B-splines transformation, where the control point distributions are non-uniform and functions of the expected norm of the spatial distortions. In our framework, the amount of distortions are first computed by estimating the B(0) fieldmap from an initial segmentation of a distortion-free structural image and tissue susceptibility models. Fieldmap estimates are propagated to obtain expected spatial distortion maps, which are used in the sampling of active B-spline control points. This transformation is flexible in locations with large distortion expectations, yet with relatively few degrees-of-freedom and does not suffer from local optima convergence and hence does not distort anatomically salient locations. Results indicate that with the proposed correction scheme, tensor derived scalar maps and fiber tracts of the same subject computed from data acquired with different phase encoding directions provide better coherency and consistency compared traditional registration based approaches.
Asunto(s)
Imagen de Difusión Tensora/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Algoritmos , Artefactos , Encéfalo/patología , Mapeo Encefálico/métodos , Computadores , Elasticidad , Humanos , Modelos EstadísticosRESUMEN
The use of multivariate pattern recognition for the analysis of neural representations encoded in fMRI data has become a significant research topic, with wide applications in neuroscience and psychology. A popular approach is to learn a mapping from the data to the observed behavior. However, identifying the instantaneous cognitive state without reference to external conditions is a relatively unexplored problem and could provide important insights into mental processes. In this paper, we present preliminary but promising results from the application of an unsupervised learning technique to identify distinct brain states. The temporal ordering of the states were seen to be synchronized with the experimental conditions, while the spatial distribution of activity in a state conformed with the expected functional recruitment.
Asunto(s)
Inteligencia Artificial , Mapeo Encefálico/métodos , Encéfalo/fisiología , Cognición/fisiología , Potenciales Evocados/fisiología , Imagen por Resonancia Magnética/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Algoritmos , Humanos , Aumento de la Imagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
We propose an anisotropic diffusion method to denoise and aid the reconstruction of planar objects in three-dimensional images. The contribution of this paper is the development of a planarity function characterizing plate-like structures using an image Hessian's eigensystem. We then construct a diffusion tensor for anisotropically smoothing plates and satisfying necessary scale-space properties. Our method finds applications in improving the fidelity of highly noisy cell membrane images from confocal microscopy. In dense cellular regions, cell membranes assume linear shapes (planar) between neighbors. The imaging process makes cell membranes appear as diffuse structures owing to the non-uniform fluorescent marker distribution, point-spread function of the optics, and anisotropic voxel resolution which make automatic cell segmentation difficult. We apply diffusion filtering to identify and enhance membranes. We demonstrate the use of our methods on 3D cell membrane images of a zebrafish embryo acquired using fluorescent microscopy and quantify the improvement in image quality.
RESUMEN
In this work, we propose a novel method for deformable tensor-to-tensor registration of Diffusion Tensor Images. Our registration method models the distances in between the tensors with Geode-sic-Loxodromes and employs a version of Multi-Dimensional Scaling (MDS) algorithm to unfold the manifold described with this metric. Defining the same shape properties as tensors, the vector images obtained through MDS are fed into a multi-step vector-image registration scheme and the resulting deformation fields are used to reorient the tensor fields. Results on brain DTI indicate that the proposed method is very suitable for deformable fiber-to-fiber correspondence and DTI-atlas construction.