943 research outputs found
Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space
Diffusion MRI requires sufficient coverage of the diffusion wavevector space,
also known as the q-space, to adequately capture the pattern of water diffusion
in various directions and scales. As a result, the acquisition time can be
prohibitive for individuals who are unable to stay still in the scanner for an
extensive period of time, such as infants. To address this problem, in this
paper we harness non-local self-similar information in the x-q space of
diffusion MRI data for q-space upsampling. Specifically, we first perform
neighborhood matching to establish the relationships of signals in x-q space.
The signal relationships are then used to regularize an ill-posed inverse
problem related to the estimation of high angular resolution diffusion MRI data
from its low-resolution counterpart. Our framework allows information from
curved white matter structures to be used for effective regularization of the
otherwise ill-posed problem. Extensive evaluations using synthetic and infant
diffusion MRI data demonstrate the effectiveness of our method. Compared with
the widely adopted interpolation methods using spherical radial basis functions
and spherical harmonics, our method is able to produce high angular resolution
diffusion MRI data with greater quality, both qualitatively and quantitatively.Comment: 15 pages, 12 figure
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
Due to its causal semantics, Bayesian networks (BN) have been widely employed
to discover the underlying data relationship in exploratory studies, such as
brain research. Despite its success in modeling the probability distribution of
variables, BN is naturally a generative model, which is not necessarily
discriminative. This may cause the ignorance of subtle but critical network
changes that are of investigation values across populations. In this paper, we
propose to improve the discriminative power of BN models for continuous
variables from two different perspectives. This brings two general
discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the
first framework, we employ Fisher kernel to bridge the generative models of GBN
and the discriminative classifiers of SVMs, and convert the GBN parameter
learning to Fisher kernel learning via minimizing a generalization error bound
of SVMs. In the second framework, we employ the max-margin criterion and build
it directly upon GBN models to explicitly optimize the classification
performance of the GBNs. The advantages and disadvantages of the two frameworks
are discussed and experimentally compared. Both of them demonstrate strong
power in learning discriminative parameters of GBNs for neuroimaging based
brain network analysis, as well as maintaining reasonable representation
capacity. The contributions of this paper also include a new Directed Acyclic
Graph (DAG) constraint with theoretical guarantee to ensure the graph validity
of GBN.Comment: 16 pages and 5 figures for the article (excluding appendix
Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks
Detecting Cognitive States from fMRI Images by Machine Learning and Multivariante Classification
The major obstacle in building classifiers that robustly detect a particular cognitive state across different subjects using fMRI images has been the high inter-subject functional variability in brain activation patterns. To overcome this obstacle, firstly, the brain regions that are relevant to the problem under study are determined from the training data; then, statistical information of each brain region is extracted to form regional features, which are robust to inter-subject functional variations within the brain region; finally, the regional feature statistical variations across different samples are further alleviated by a PCA technique. To improve the generalization ability and efficiency of the classification, from the extracted regional features, a hybrid feature selection method is utilized to select the most discriminative features, which are used to train a SVM classifier for decoding brain states from fMRI images. The performance of this method is validated in a deception fMRI study. The proposed method yielded better results compared to other commonly used fMRI image classification methods
Fast image registration by hierarchical soft correspondence detection
A new approach, based on the hierarchical soft correspondence detection, has been presented for significantly improving the speed of our previous HAMMER image registration algorithm. Currently, HAMMER takes a relative long time, e.g., up to 80 minutes, to register two regular sized images using Linux machine (with 2.40GHz CPU and 2-Gbyte memory). This is because the results of correspondence detection, used to guide the image warping, can be ambiguous in complex structures and thus the image warping has to be conservative and accordingly takes long time to complete. In this paper, a hierarchical soft correspondence detection technique has been employed to detect correspondences more robustly, thereby allowing the image warping to be completed straightforwardly and fast. By incorporating this hierarchical soft correspondence detection technique into the HAMMER registration framework, the robustness and the accuracy of registration (in terms of low average registration error) can be both achieved. Experimental results on real and simulated data show that the new registration algorithm, based the hierarchical soft correspondence detection, can run nine times faster than HAMMER while keeping the similar registration accuracy
- …