223 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
A half-quadratic block-coordinate descent method for spectral estimation
International audienceIn short-time spectral estimation, Sacchi et al. (IEEE Trans. Signal Process. 46(1) (1998) 31) and Ciuciu et al. (IEEE Trans. Signal Process. 49 (2001) 2202) derived new nonlinear spectral estimators de4ned as minimizers ofpenalized criteria. The 4rst contributors have introduced separable penalizations for line spectra (LS) recovering, whereas the latter have proposed circular Gibbs–Markov functions for smooth spectra (SS) restoration, and combined both contributions for estimation of “mixed” spectra (MS), i.e., frequency peaks superimposed on a homogeneous background (Ciuciu et al., 2001). Sacchi et al. resorted to the iteratively reweighted least squares (IRLS) algorithm for the minimization stage. Here, we show that IRLS is a block-coordinate descent (BCD) method performing the minimization of a half-quadratic (HQ) energy. The latter, derived from the Geman and Reynolds construction, has the same minimizer as the initial criterion but depends on more variables. After proving that such a construction is not available for Gibbs–Markov penalizations, we extend the pioneering work ofGeman and Yang (IEEE Trans. Image Process. 4(7) (1995) 932) that leads to a suitable HQ energy for any kind ofpenalization encountered in Ciuciu et al. (2001). The BCD algorithm used for minimizing such HQ criteria is actually an original residual steepest descent (RSD) procedure (IEEE Trans. Acoust. Speech Signal Process. ASSP-33(1) (1985) 174) and thus converges in any convex case. A comparison between RSD, IRLS when available, and a pseudo-conjugate gradient algorithm is addressed in any case
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
Impact of perceptual learning on resting-state fMRI connectivity: A supervised classification study
International audiencePerceptual learning sculpts ongoing brain activity [1]. This finding has been observed by statistically comparing the functional connectivity (FC) patterns computed from resting-state functional MRI (rs-fMRI) data recorded before and after intensive training to a visual attention task. Hence, functional connectivity serves a dynamic role in brain function, supporting the consolidation of previous experience. Following this line of research, we trained three groups of individuals to a visual discrimination task during a magneto-encephalography (MEG) experiment [2]. The same individuals were then scanned in rs-fMRI. Here, in a supervised classification framework, we demonstrate that FC metrics computed on rs-fMRI data are able to predict the type of training the participants received. On top of that, we show that the prediction accuracies based on tangent embedding FC measure outperform those based on our recently developed multivariate wavelet-based Hurst exponent estimator [3], which captures low frequency fluctuations in ongoing brain activity too
Group-level impacts of within- and between-subject hemodynamic variability in fMRI
International audienceInter-subject fMRI analyses have specific issues regarding the reliability of the results concerning both the detection of brain activation patterns and the estimation of the underlying dynamics. Among these issues lies the variability of the hemodynamic response function (HRF), that is usually accounted for using functional basis sets in the general linear model context. Here, we use the joint detection-estimation approach (JDE) (Makni et al., 2008; Vincent et al., 2010) which combines regional nonparametric HRF inference with spatially adaptive regularization of activation clusters to avoid global smoothing of fMRI images. We show that the JDE-based inference brings a significant improvement in statistical sensitivity for detecting evoked activity in parietal regions. In contrast, the canonical HRF associated with spatially adaptive regularization is more sensitive in other regions, such as motor cortex. This different regional behavior is shown to reflect a larger discrepancy of HRF with the canonical model. By varying parallel imaging acceleration factor, SNR-specific region-based hemodynamic parameters (activation delay and duration) were extracted from the JDE inference. Complementary analyses highlighted their significant departure from the canonical parameters and the strongest between-subject variability that occurs in the parietal region, irrespective of the SNR value. Finally, statistical evidence that the fluctuation of the HRF shape is responsible for the significant change in activation detection performance is demonstrated using paired t-tests between hemodynamic parameters inferred by GLM and JDE
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
Application and validation of spatial mixture modelling for the joint detection-estimation of brain activity in fMRI.
International audienceWithin-subject analysis in event-related functional Magnetic Resonance Imaging (fMRI) first relies on (i) a detection step to localize which parts of the brain are activated by a given stimulus type, and second on (ii) an estimation step to recover the temporal dynamics of the brain response. Recently, a Bayesian detection-estimation approach that jointly addresses (i)-(ii) has been proposed in [1]. This work is based on an independent mixture model (IMM) and provides both a spatial activity map and an estimate of brain dynamics. In [2], we accounted for spatial correlation using a spatial mixture model (SMM) based on a binary Markov random field. Here, we assess the SMM robustness and flexibility on simulations which diverge from the priors and the generative BOLD model and further extend comparison between SMM and IMM on real fMRI data, focusing on a region of interest in the auditory cortex
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