1,172 research outputs found
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
In this paper, we introduce a dictionary learning based approach applied to
the problem of real-time reconstruction of MR image sequences that are highly
undersampled in k-space. Unlike traditional dictionary learning, our method
integrates both global and patch-wise (local) sparsity information and
incorporates some priori information into the reconstruction process. Moreover,
we use a Dependent Hierarchical Beta-process as the prior for the group-based
dictionary learning, which adaptively infers the dictionary size and the
sparsity of each patch; and also ensures that similar patches are manifested in
terms of similar dictionary atoms. An efficient numerical algorithm based on
the alternating direction method of multipliers (ADMM) is also presented.
Through extensive experimental results we show that our proposed method
achieves superior reconstruction quality, compared to the other state-of-the-
art DL-based methods
A Mixed Model Approach for Estimating Regional Functional Connectivity from Voxel-level BOLD Signals
Resting state brain functional connectivity quantifies the similarity between
brain regions, each of which consists of voxels at which dynamic signals are
acquired via neuroimaging techniques such as blood-oxygen-level-dependent
signals in functional magnetic resonance imaging. Pearson correlation and
similar metrics have been adopted by neuroscientists to estimate inter-regional
connectivity, usually after averaging of signals within regions. However,
dependencies between signals within each region and the presence of noise could
contaminate such inter-regional correlation estimates. We propose a
mixed-effects model with a novel covariance structure that explicitly isolates
the different sources of variability in the observed BOLD signals, including
correlated regional signals, local spatiotemporal variability, and measurement
error. Methods for tackling the computational challenges associated with
restricted maximum likelihood estimation will be discussed. Large sample
properties are discussed and used for uncertainty quantification. Simulation
results demonstrate that the parameters of the proposed model parameters can be
accurately estimated and is superior to the Pearson correlation of averages in
the presence of spatiotemporal noise. The proposed model is also applied to a
real data set of BOLD signals collected from rats to construct individual brain
networks.Comment: 25 pages, 6 figure
Bayesian analysis of functional magnetic resonance imaging data with spatially varying auto‐regressive orders
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148250/1/rssc12320.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148250/2/rssc12320_am.pd
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