11,324 research outputs found
Linear Dynamic Sparse Modelling for functional MR imaging
The reconstruction quality of a functional MRI sequence is determined by reconstruction algorithms as well as the information obtained from measurements. In this paper, we propose a Linear Dynamic Sparse Modelling method which is composed of measurement design and reconstruction processes to improve the image quality from both aspects. This method models an fMRI sequence as a linear dynamic sparse model which is based on a key assumption that variations of functional MR images are sparse over time in the wavelet domain. The Hierarchical Bayesian Kalman filter which follows the model is employed to implement the reconstruction process. To accomplish the measurement design process, we propose an Informative Measurement Design (IMD) method. The IMD method addresses the measurement design problem of selecting k feasible measurements such that the mutual information between the unknown image and measurements is maximised, where k is a given budget and the mutual information is extracted from the linear dynamic sparse model. The experimental results demonstrated that our proposed method succeeded in boosting the quality of functional MR images
PEAR: PEriodic And fixed Rank separation for fast fMRI
In functional MRI (fMRI), faster acquisition via undersampling of data can
improve the spatial-temporal resolution trade-off and increase statistical
robustness through increased degrees-of-freedom. High quality reconstruction of
fMRI data from undersampled measurements requires proper modeling of the data.
We present an fMRI reconstruction approach based on modeling the fMRI signal as
a sum of periodic and fixed rank components, for improved reconstruction from
undersampled measurements. We decompose the fMRI signal into a component which
a has fixed rank and a component consisting of a sum of periodic signals which
is sparse in the temporal Fourier domain. Data reconstruction is performed by
solving a constrained problem that enforces a fixed, moderate rank on one of
the components, and a limited number of temporal frequencies on the other. Our
approach is coined PEAR - PEriodic And fixed Rank separation for fast fMRI.
Experimental results include purely synthetic simulation, a simulation with
real timecourses and retrospective undersampling of a real fMRI dataset.
Evaluation was performed both quantitatively and visually versus ground truth,
comparing PEAR to two additional recent methods for fMRI reconstruction from
undersampled measurements. Results demonstrate PEAR's improvement in estimating
the timecourses and activation maps versus the methods compared against at
acceleration ratios of R=8,16 (for simulated data) and R=6.66,10 (for real
data). PEAR results in reconstruction with higher fidelity than when using a
fixed-rank based model or a conventional Low-rank+Sparse algorithm. We have
shown that splitting the functional information between the components leads to
better modeling of fMRI, over state-of-the-art methods
Learning and comparing functional connectomes across subjects
Functional connectomes capture brain interactions via synchronized
fluctuations in the functional magnetic resonance imaging signal. If measured
during rest, they map the intrinsic functional architecture of the brain. With
task-driven experiments they represent integration mechanisms between
specialized brain areas. Analyzing their variability across subjects and
conditions can reveal markers of brain pathologies and mechanisms underlying
cognition. Methods of estimating functional connectomes from the imaging signal
have undergone rapid developments and the literature is full of diverse
strategies for comparing them. This review aims to clarify links across
functional-connectivity methods as well as to expose different steps to perform
a group study of functional connectomes
Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression
We present a new method for the detection of gene pathways associated with a
multivariate quantitative trait, and use it to identify causal pathways
associated with an imaging endophenotype characteristic of longitudinal
structural change in the brains of patients with Alzheimer's disease (AD). Our
method, known as pathways sparse reduced-rank regression (PsRRR), uses group
lasso penalised regression to jointly model the effects of genome-wide single
nucleotide polymorphisms (SNPs), grouped into functional pathways using prior
knowledge of gene-gene interactions. Pathways are ranked in order of importance
using a resampling strategy that exploits finite sample variability. Our
application study uses whole genome scans and MR images from 464 subjects in
the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs
are mapped to 185 gene pathways from the KEGG pathways database. Voxel-wise
imaging signatures characteristic of AD are obtained by analysing 3D patterns
of structural change at 6, 12 and 24 months relative to baseline. High-ranking,
AD endophenotype-associated pathways in our study include those describing
chemokine, Jak-stat and insulin signalling pathways, and tight junction
interactions. All of these have been previously implicated in AD biology. In a
secondary analysis, we investigate SNPs and genes that may be driving pathway
selection, and identify a number of previously validated AD genes including
CR1, APOE and TOMM40
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