396 research outputs found
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
Parallel MRI is a fast imaging technique that enables the acquisition of
highly resolved images in space or/and in time. The performance of parallel
imaging strongly depends on the reconstruction algorithm, which can proceed
either in the original k-space (GRAPPA, SMASH) or in the image domain
(SENSE-like methods). To improve the performance of the widely used SENSE
algorithm, 2D- or slice-specific regularization in the wavelet domain has been
deeply investigated. In this paper, we extend this approach using 3D-wavelet
representations in order to handle all slices together and address
reconstruction artifacts which propagate across adjacent slices. The gain
induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE:
3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal
acquisition is considered. Another important extension accounts for temporal
correlations that exist between successive scans in functional MRI (fMRI). In
addition to the case of 2D+t acquisition schemes addressed by some other
methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition
schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and
4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that
all regularization parameters are estimated in the maximum likelihood sense on
a reference scan. The gain induced by such extensions is illustrated on both
anatomical and functional image reconstruction, and also measured in terms of
statistical sensitivity for the 4D-UWR-SENSE approach during a fast
event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE
reconstruction at the subject and group levels (15 subjects) for different
contrasts of interest (eg, motor or computation tasks) and using different
parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353
Hemodynamically informed parcellation of cerebral FMRI data
Standard detection of evoked brain activity in functional MRI (fMRI) relies
on a fixed and known shape of the impulse response of the neurovascular
coupling, namely the hemodynamic response function (HRF). To cope with this
issue, the joint detection-estimation (JDE) framework has been proposed. This
formalism enables to estimate a HRF per region but for doing so, it assumes a
prior brain partition (or parcellation) regarding hemodynamic territories. This
partition has to be accurate enough to recover accurate HRF shapes but has also
to overcome the detection-estimation issue: the lack of hemodynamics
information in the non-active positions. An hemodynamically-based parcellation
method is proposed, consisting first of a feature extraction step, followed by
a Gaussian Mixture-based parcellation, which considers the injection of the
activation levels in the parcellation process, in order to overcome the
detection-estimation issue and find the underlying hemodynamics
Variable density sampling based on physically plausible gradient waveform. Application to 3D MRI angiography
Performing k-space variable density sampling is a popular way of reducing
scanning time in Magnetic Resonance Imaging (MRI). Unfortunately, given a
sampling trajectory, it is not clear how to traverse it using gradient
waveforms. In this paper, we actually show that existing methods [1, 2] can
yield large traversal time if the trajectory contains high curvature areas.
Therefore, we consider here a new method for gradient waveform design which is
based on the projection of unrealistic initial trajectory onto the set of
hardware constraints. Next, we show on realistic simulations that this
algorithm allows implementing variable density trajectories resulting from the
piecewise linear solution of the Travelling Salesman Problem in a reasonable
time. Finally, we demonstrate the application of this approach to 2D MRI
reconstruction and 3D angiography in the mouse brain.Comment: IEEE International Symposium on Biomedical Imaging (ISBI), Apr 2015,
New-York, United State
“That’s What You Want to do as a Teacher, Make a Difference, Let the Child Be, Have High Expectations”: Stories of Becoming, Being and Unbecoming an Early Childhood Teacher
This article explores the experiences of four individuals who changed careers into early childhood teaching in Victoria, Australia and later left the profession. The study was conducted with a narrative inquiry approach and reveals insight into motivations for becoming an early childhood teacher (ECT), experiences of being an ECT and factors that lead to un-becoming an ECT. Participants were motivated by pragmatic reasons such as career advancement and family-work compatibility alongside intrinsic interest when becoming an ECT. They entered the profession eager to support children’s learning and development. However, their experiences compromised their health and wellbeing and inhibited them from teaching as they envisioned. The findings of the study hold implications for policy makers, employers and higher education in effort to retain and sustain ECTs
Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach
In standard clinical within-subject analyses of event-related fMRI data, two
steps are usually performed separately: detection of brain activity and
estimation of the hemodynamic response. Because these two steps are inherently
linked, we adopt the so-called region-based Joint Detection-Estimation (JDE)
framework that addresses this joint issue using a multivariate inference for
detection and estimation. JDE is built by making use of a regional bilinear
generative model of the BOLD response and constraining the parameter estimation
by physiological priors using temporal and spatial information in a Markovian
modeling. In contrast to previous works that use Markov Chain Monte Carlo
(MCMC) techniques to approximate the resulting intractable posterior
distribution, we recast the JDE into a missing data framework and derive a
Variational Expectation-Maximization (VEM) algorithm for its inference. A
variational approximation is used to approximate the Markovian model in the
unsupervised spatially adaptive JDE inference, which allows fine automatic
tuning of spatial regularisation parameters. It follows a new algorithm that
exhibits interesting properties compared to the previously used MCMC-based
approach. Experiments on artificial and real data show that VEM-JDE is robust
to model mis-specification and provides computational gain while maintaining
good performance in terms of activation detection and hemodynamic shape
recovery
ICA-based sparse feature recovery from fMRI datasets
Spatial Independent Components Analysis (ICA) is increasingly used in the
context of functional Magnetic Resonance Imaging (fMRI) to study cognition and
brain pathologies. Salient features present in some of the extracted
Independent Components (ICs) can be interpreted as brain networks, but the
segmentation of the corresponding regions from ICs is still ill-controlled.
Here we propose a new ICA-based procedure for extraction of sparse features
from fMRI datasets. Specifically, we introduce a new thresholding procedure
that controls the deviation from isotropy in the ICA mixing model. Unlike
current heuristics, our procedure guarantees an exact, possibly conservative,
level of specificity in feature detection. We evaluate the sensitivity and
specificity of the method on synthetic and fMRI data and show that it
outperforms state-of-the-art approaches
Physiologically Informed Bayesian Analysis of ASL fMRI Data
Arterial Spin Labelling (ASL) functional Magnetic Resonance Imaging (fMRI)
data provides a quantitative measure of blood perfusion, that can be correlated
to neuronal activation. In contrast to BOLD measure, it is a direct measure of
cerebral blood flow. However, ASL data has a lower SNR and resolution so that
the recovery of the perfusion response of interest suffers from the
contamination by a stronger hemodynamic component in the ASL signal. In this
work we consider a model of both hemodynamic and perfusion components within
the ASL signal. A physiological link between these two components is analyzed
and used for a more accurate estimation of the perfusion response function in
particular in the usual ASL low SNR conditions
“That’s What You Want to do as a Teacher, Make a Difference, Let the Child Be, Have High Expectations”: Stories of Becoming, Being and Unbecoming an Early Childhood Teacher
This article explores the experiences of four individuals who changed careers into early childhood teaching in Victoria, Australia and later left the profession. The study was conducted with a narrative inquiry approach and reveals insight into motivations for becoming an early childhood teacher (ECT), experiences of being an ECT and factors that lead to un-becoming an ECT. Participants were motivated by pragmatic reasons such as career advancement and family-work compatibility alongside intrinsic interest when becoming an ECT. They entered the profession eager to support children’s learning and development. However, their experiences compromised their health and wellbeing and inhibited them from teaching as they envisioned. The findings of the study hold implications for policy makers, employers and higher education in effort to retain and sustain ECTs
A Hierarchical Bayesian Model for Frame Representation
In many signal processing problems, it may be fruitful to represent the
signal under study in a frame. If a probabilistic approach is adopted, it
becomes then necessary to estimate the hyper-parameters characterizing the
probability distribution of the frame coefficients. This problem is difficult
since in general the frame synthesis operator is not bijective. Consequently,
the frame coefficients are not directly observable. This paper introduces a
hierarchical Bayesian model for frame representation. The posterior
distribution of the frame coefficients and model hyper-parameters is derived.
Hybrid Markov Chain Monte Carlo algorithms are subsequently proposed to sample
from this posterior distribution. The generated samples are then exploited to
estimate the hyper-parameters and the frame coefficients of the target signal.
Validation experiments show that the proposed algorithms provide an accurate
estimation of the frame coefficients and hyper-parameters. Application to
practical problems of image denoising show the impact of the resulting Bayesian
estimation on the recovered signal quality
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