Results 11 - 20
of
25
A nonparametric Riemannian framework for processing high angular resolution . . .
- NEUROIMAGE 56 (2011) 1181–1201
, 2011
"... ..."
(Show Context)
r Human Brain Mapping 000:000–000 (2012) r Application of Neuroanatomical Features to Tractography Clustering
"... Abstract: Diffusion tensor imaging allows unprecedented insight into brain neural connectivity in vivo by allowing reconstruction of neuronal tracts via captured patterns of water diffusion in white matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers ..."
Abstract
- Add to MetaCart
Abstract: Diffusion tensor imaging allows unprecedented insight into brain neural connectivity in vivo by allowing reconstruction of neuronal tracts via captured patterns of water diffusion in white matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers, rendering subsequent data analysis intractable. As a remedy, fiber clustering techniques are able to group fibers into dozens of bundles and thus facilitate analyses. Most existing fiber clustering methods rely on geometrical information of fibers, by viewing them as curves in 3D Euclidean space. The important neuroanatomical aspect of fibers, however, is ignored. In this article, the neuroanatomical information of each fiber is encapsulated in the associativity vector, which functions as the unique ‘‘fingerprint’ ’ of the fiber. Specifically, each entry in the associativity vector describes the relationship between the fiber and a certain anatomical ROI in a fuzzy manner. The value of the entry approaches 1 if the fiber is spatially related to the ROI at high confidence; on the contrary, the value drops closer to 0. The confidence of the ROI is calculated by diffusing the ROI according to the underlying fibers from tractography. In particular, we have adopted the fast marching method for simulation of ROI diffusion. Using the associativity vectors of fibers, we further model fibers as observations sampled from multivariate Gaussian mixtures in the feature space. To group all fibers into relevant major bundles, an expectation-maximization clustering approach is employed. Experimental results indicate that our method results in anatomically meaningful bundles that are highly consistent across subjects. Hum Brain Mapp
Research Article Change of Neural Connectivity of the Red Nucleus in Patients with Striatocapsular Hemorrhage: A Diffusion Tensor Tractography Study
"... License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The red nucleus (RN) is involved in motor control and it is known to have potential to compensate for injury of the corticospinal tract (CST). We investigated the chan ..."
Abstract
- Add to MetaCart
(Show Context)
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The red nucleus (RN) is involved in motor control and it is known to have potential to compensate for injury of the corticospinal tract (CST). We investigated the change of connectivity of the RN (RNc) and its relation to motor function in patients with striatocapsular hemorrhage. Thirty-five chronic patients with striatocapsular hemorrhage were recruited. Motricity Index (MI), Modified Brunnstrom Classification (MBC), and Functional Ambulation Category (FAC) were measured for motor function. The probabilistic tractographymethod was used for evaluation of the RNc. Fractional anisotropy (FA), mean diffusivity (MD), and tract volume (TV) of the RNc were measured. FA and TV ratios of the RNc in patients with discontinuation of the affected CST were significantly higher than those of patients with preserved integrity of the CST in the affected hemisphere (
RESEARCH ARTICLE Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization
"... ☯ These authors contributed equally to this work. ..."
(Show Context)
1 Collaborative Patch-Based Super-Resolution for Diffusion-Weighted Images
"... In this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non diffusion image (b0) to constrain the reconstruction of DW images. An extensive v ..."
Abstract
- Add to MetaCart
(Show Context)
In this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non diffusion image (b0) to constrain the reconstruction of DW images. An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquisitions. A comparison with classical interpolation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in terms of improvements on image reconstruction, fractional anisotropy (FA) estimation, generalized FA and angular reconstruction for tensor and high angular resolution diffusion imaging (HARDI) models. Besides, first results of reconstructed ultra high resolution DW images are presented at 0.6x0.6x0.6mm3 and 0.4x0.4x0.4mm3 using our gold standard based on the average of 10 acquisitions, and on a single acquisition. Finally, fiber tracking results show the potential of the proposed super-resolution approach to accurately analyze white matter brain architecture.
A Framework for ODF Inference by using Fiber Tract Adaptive MPG Selection
"... Abstract The authors propose a method that selects a set of motion probing gra-dient (MPG) directions, which is adapted for measuring fiber tracts in some spe-cific region of interest (ROI) with smaller number of MPGs. Given a training set of diffusion magnetic resonance (MR) images, the method sele ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract The authors propose a method that selects a set of motion probing gra-dient (MPG) directions, which is adapted for measuring fiber tracts in some spe-cific region of interest (ROI) with smaller number of MPGs. Given a training set of diffusion magnetic resonance (MR) images, the method selects the set of MPG directions by minimizing a cost function, which represents the square errors of the reconstructed oriented distribution functions (ODFs). This selection of MPGs is a combinatorial optimization problem, and a simulated annealing scheme is employed for selecting the MPGs. Experimental results demonstrated that the set of MPG di-rections selected by our proposed method reconstructed the ODFs more accurately than an existing method based on spherical harmonics and on greedy optimization.
Local Water Diffusion Phenomenon Clustering From High Angular Resolution Diffusion Imaging (HARDI)∗
"... The understanding of neurodegenerative diseases un-doubtedly passes through the study of human brain white matter fiber tracts. To date, diffusion magnetic resonance imaging (dMRI) is the unique technique to obtain infor-mation about the neural architecture of the human brain, thus permitting the st ..."
Abstract
- Add to MetaCart
(Show Context)
The understanding of neurodegenerative diseases un-doubtedly passes through the study of human brain white matter fiber tracts. To date, diffusion magnetic resonance imaging (dMRI) is the unique technique to obtain infor-mation about the neural architecture of the human brain, thus permitting the study of white matter connections and their integrity. However, a remaining challenge of the dMRI community is to better characterize com-plex fiber crossing configurations, where diffusion tensor imaging (DTI) is limited but high angular resolution diffusion imaging (HARDI) now brings solutions. This paper investigates the development of both identification and classification process of the local water diffusion phenomenon based on HARDI data to automatically de-tect imaging voxels where there are single and crossing fiber bundle populations. The technique is based on knowledge extraction processes and is validated on a dMRI phantom dataset with ground truth. 1
Evaluation of Diffusion-Tensor Imaging-Based Global Search and Tractography for Tumor Surgery Close to the Language System
"... Pre-operative planning and intra-operative guidance in neurosurgery require detailed information about the location of functional areas and their anatomo-functional connectivity. In particular, regarding the language system, post-operative deficits such as aphasia can be avoided. By combining functi ..."
Abstract
- Add to MetaCart
(Show Context)
Pre-operative planning and intra-operative guidance in neurosurgery require detailed information about the location of functional areas and their anatomo-functional connectivity. In particular, regarding the language system, post-operative deficits such as aphasia can be avoided. By combining functional magnetic resonance imaging and diffusion tensor imaging, the connectivity between functional areas can be reconstructed by tractography techniques that need to cope with limitations such as limited resolution and low anisotropic diffusion close to functional areas. Tumors pose particular challenges because of edema, displacement effects on brain tissue and infiltration of white matter. Under these conditions, standard fiber tracking methods reconstruct pathways of insufficient quality. Therefore, robust global or probabilistic approaches are required. In this study, two commonly used standard fiber tracking algorithms, streamline propagation and tensor deflection, were compared with a previously published global search, Gibbs tracking and a connection-oriented probabilistic tractography approach. All methods were applied to reconstruct neuronal pathways of the language system of patients undergoing brain tumor surgery, and control subjects. Connections between Broca and Wernicke areas via the arcuate fasciculus (AF) and the inferior fronto-occipital fasciculus (IFOF) were validated by a clinical expert to ensure anatomical feasibility, and compared using distance- and diffusion-based similarity metrics to evaluate their agreement on pathway locations. For both patients and controls, a strong agreement between all methods was observed regarding the
DTI-DeformIt: GENERATING GROUND-TRUTH VALIDATION DATA FOR DIFFUSION TENSOR IMAGE ANALYSIS TASKS
"... We propose DTI-DeformIt: a framework to generate realis-tic synthetic datasets from a smaller number of, or even one, annotated image(s). Our approach extends the DeformIt tech-nique of Hamarneh et al. [1] to handle the deformations and noise conditions of diffusion tensor images. An implemen-tation ..."
Abstract
- Add to MetaCart
(Show Context)
We propose DTI-DeformIt: a framework to generate realis-tic synthetic datasets from a smaller number of, or even one, annotated image(s). Our approach extends the DeformIt tech-nique of Hamarneh et al. [1] to handle the deformations and noise conditions of diffusion tensor images. An implemen-tation of our proposed framework is also provided as a free download. We further show that DTI-DeformIt generates im-ages that, according to eigenvector distance, are no different from real images than other real images, making them suit-able for machine learning and validation.
unknown title
"... Global tractography with embedded anatomical priors for quantitative connectivity analysis ..."
Abstract
- Add to MetaCart
(Show Context)
Global tractography with embedded anatomical priors for quantitative connectivity analysis
