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28
Bayesian secondlevel analysis of functional magnetic resonance images
 Neuroimage
, 2003
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Anisotropic 2D and 3D averaging of fMRI signals
 IEEE Trans. on Medical Imaging
, 2001
"... A novel method for denoising functional MRI temporal signals is presented in this note. The method is based on progressively enhancing the temporal signal by means of adaptive anisotropic spatial averaging. This average is based on a new metric here proposed for comparing temporal signals correspond ..."
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A novel method for denoising functional MRI temporal signals is presented in this note. The method is based on progressively enhancing the temporal signal by means of adaptive anisotropic spatial averaging. This average is based on a new metric here proposed for comparing temporal signals corresponding to active fMRI regions. Examples are presented both for simulated and real two and three dimensional data. The software implementing the proposed technique is publicly available for the research community. Keywords Functional MRI, anisotropic averaging, Fourier spectrum, signal metrics. I. Introduction Functional Magnetic Resonance Imaging (fMRI) is the most significant and revolutionary advance in MRI in recent years, e.g., [1], [2], [3]. This technique uses MRI to noninvasively map areas of increased neuronal activity in the human brain without the use of an exogenous contrast agent. The majority of fMRI experiments are based on the blood oxygenation level dependent (BOLD) contr...
Cluster priors in the Bayesian modelling of fMRI data
, 2001
"... Functional magnetic resonance imaging (fMRI) is a scanning technique for revealing haemodynamic changes connected with brain processing on the neuronal level. In neuropsychology, fMRI has been used in designed experiments together with controlled stimulation. fMRI data are temporal series of digital ..."
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Functional magnetic resonance imaging (fMRI) is a scanning technique for revealing haemodynamic changes connected with brain processing on the neuronal level. In neuropsychology, fMRI has been used in designed experiments together with controlled stimulation. fMRI data are temporal series of digital images corrupted by spatiotemporally correlated physiological processes and scanner noise. The statistical challenge in analysing fMRI data is to localize stimulusrelated brain activation and estimate its characteristics. In this thesis, the focus is on spatial aspects of activations. A Bayesian approach is proposed and an a priori model which describes the clustering of activations is suggested. The prior is used to control the spatial extent, coherence and locations of clusters. Marked Gibbs point processes have been used to construct the prior. The prior is designed so that expert knowledge on the neuronal processing of interest can be incorporated into statistical analysis. To model the conditional distribution of observations, given the activations, Gaussian conditional autoregressive processes have been applied. Using these processes, heteroskedasticity and spatial autocorrelation in noise is accounted for. Inference is based on Markov chain Monte Carlo (MCMC) simulations of the posterior distribution. A modified version of an existing general simulation method for Gibbs point processes is devised to sample the posterior. Real fMRI data are analysed and the influence of different amounts of prior information on the uncertainty inactivations is illustrated. An example of analysing synthetic data is provided to compare the new method with conventional nonparametric techniques. The conclusion is that, by adopting a structural approach, relevant features of activations can be accounted for leading to a potentially more efficient inference.
A WaveletBased Statistical Analysis of fMRI data
 I. Motivation and Data Distribution Modeling, in press, NeuroInformatics
, 2005
"... We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We use structural MRI and functional fMRI to empirically estimate the distribution of the w ..."
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We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We use structural MRI and functional fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and across spatial locations. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of which is processed separately. A frequencyadaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. The empirical distributions of the signals are computed on all regions in compressed wavelet space. These are modeled by heavytail distributions because their histograms exhibit slower tail decay than the Gaussian. We discovered that Cauchy, Bessel KForms and Pareto distributions provide the most accurate asymptotic models for the distribution of the wavelet coefficients of the data. Finally, we propose a new model for statistical analysis of functional MRI data using this atlasbased waveletspace representation. In the second part of our investigation we will apply this technique to analyze a large fMRI data set involving repeated presentation of sensorymotor response stimuli in young, elderly and demented subjects.
Classification of anatomical structures in MR brain images using fuzzy parameters
 IEEE Transactions on Biomedical Engineering
, 2004
"... Abstract—We present an algorithm that automatically segments and classifies the brain structures in a set of magnetic resonance (MR) brain images using expert information contained in a small subset of the image set. The algorithm is intended to do the segmentation and classification tasks mimickin ..."
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Abstract—We present an algorithm that automatically segments and classifies the brain structures in a set of magnetic resonance (MR) brain images using expert information contained in a small subset of the image set. The algorithm is intended to do the segmentation and classification tasks mimicking the way a human expert would reason. The algorithm uses a knowledge base taken from a small subset of semiautomatically classified images that is combined with a set of fuzzy indexes that capture the experience and expectation a human expert uses during recognition tasks. The fuzzy indexes are tissue specific and spatial specific, in order to consider the biological variations in the tissues and the acquisition inhomogeneities through the image set. The brain structures are segmented and classified one at a time. For each brain structure the algorithm needs one semiautomatically classified image and makes one pass through the image set. The algorithm uses lowlevel image processing techniques on a pixel basis for the segmentations, then validates or corrects the segmentations, and makes the final classification decision using higher level criteria measured by the set of fuzzy indexes. We use singleecho MR images because of their high volumetric resolution; but even though we are working with only one image per brain slice, we have multiple sources of information on each pixel: absolute and relative positions in the image, gray level value, statistics of the pixel and its threedimensional neighborhood and relation to its counterpart pixels in adjacent images. We have validated our algorithm for ease of use and precision both with clinical experts and with measurable error indexes over a Brainweb simulated MR set. Index Terms—Biomedical image processing, image classification, image segmentation, magnetic resonance imaging. I.
Parameter estimation efficiency using nonlinear models
 in fMRI, Technical report, n o RR5758, INRIA
, 2005
"... apport de rechercheinria00070262, version 1 19 May 2006Parameter estimation efficiency using nonlinear models in fMRI ..."
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apport de rechercheinria00070262, version 1 19 May 2006Parameter estimation efficiency using nonlinear models in fMRI
Robust Anisotropic Diffusion to Produce Clear Statistical Parametric Map from Noisy fMRI HAE YONG KIM
 in: Proceedings of Sibgrapi—Brazilian Symposium on Computer Graphics and Image Processing
, 2002
"... Functional magnetic resonance imaging (fMRI) uses MRI to noninvasively map areas of increased neuronal activity in human brain without the use of an exogenous contrast agent. Low signaltonoise ratio of fMRI images makes it necessary to use sophisticated image processing techniques, such as stati ..."
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Functional magnetic resonance imaging (fMRI) uses MRI to noninvasively map areas of increased neuronal activity in human brain without the use of an exogenous contrast agent. Low signaltonoise ratio of fMRI images makes it necessary to use sophisticated image processing techniques, such as statistical parametric map (SPM), to detect activated brain areas. This paper presents a new technique to obtain clear SPM from noisy fMRI data. It is based on the robust anisotropic diffusion. A direct application of the anisotropic diffusion to fMRI does not work, mainly due to the lack of sharp boundaries between activated and nonactivated regions. To overcome this difficulty, we propose to calculate SPM from noisy fMRI, compute diffusion coefficients in the SPM space, and then perform the diffusion in fMRI images using the coefficients previously computed. These steps are iterated until the convergence. Experimental results using the new technique yielded surprisingly sharp and noiseless SPMs.
Robust voxelwise Joint Detection Estimation of brain activity in fMRI
 in "IEEE International Conference on Image Processing (ICIP
"... We address the issue of jointly detecting brain activity and estimating brain hemodynamics from functional MRI data. To this end, we adopt the socalled JointDetectionEstimation (JDE) framework introduced in [1] and augmented in [2]. An inherent difficulty is to find the right spatial scale at whi ..."
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We address the issue of jointly detecting brain activity and estimating brain hemodynamics from functional MRI data. To this end, we adopt the socalled JointDetectionEstimation (JDE) framework introduced in [1] and augmented in [2]. An inherent difficulty is to find the right spatial scale at which brain hemodynamics estimation makes sense. The voxel level is clearly not appropriate as estimating a full hemodynamic response function (HRF) from a single voxel time course may suffer from a poor signaltonoiseratio and lead to potentially misleading results (nonphysiological HRF shapes). More robust estimation can be obtained by considering groups of voxels (i.e. parcels) with some functional homogeneity properties. Current JDE approaches are therefore based on an initial parcellation but with no guarantee of its optimality or goodness. In this work, we propose a joint parcellationdetectionestimation (JPDE) procedure that incorporates an additional parcel estimation step solving this way both the parcellation choice and robust HRF estimation issues. As in [3], inference is carried out in a Bayesian setting using variational approximation techniques for computational efficiency. Index Terms — Variational EM, MRF, Biomedical signal detection, Magnetic resonance imaging.
Activation detection in fMRI data via multiscale singularity detection
 Proceedings of the SPIE  The International Society for Optical Engineering
, 2000
"... Detection of active areas in the brain by functional magnetic resonance imaging (fMRI) is a challenging problem in medical imaging. Moreover, determining the onset and end of activation signals at specific locations in 3space can determine networks of temporal relationships required for brain mappi ..."
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Detection of active areas in the brain by functional magnetic resonance imaging (fMRI) is a challenging problem in medical imaging. Moreover, determining the onset and end of activation signals at specific locations in 3space can determine networks of temporal relationships required for brain mapping. We introduce a method for activation detection in fMRI data via wavelet analysis of singular features. We pose the problem of determining activated areas as singularity detection in the temporal domain. Overcomplete wavelet expansions at integer scales are used to trace wavelet modulus maxima across scales to construct maxima lines. From these maxima lines, singularities in the signal are localized corresponding to the onset and end of an activation signal. We present results for simulated phantom waveforms and clinical fMRI data from human finger tapping experiments. Different levels of noise were added to two waveforms of phantom data. No assumptions about specific frequency and amplitude of an activation signal were made prior to analysis. Detection was reliable for modest levels of random noise, but less precise at higher levels. For clinical fMRI data, activation maps were comparable to those of existing standard techniques.
Activation detection and characterisation in brain fMRI sequences. Application to the study of monkey vision.
, 2001
"... In this report, we propose a number of new ways of detecting activations in fMRI sequences that require a minimum of hypotheses and avoid any premodelling of the expected signal. In particular, we try to avoid as much as possible linear models. The sensitivity of the methods with respect to signal ..."
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In this report, we propose a number of new ways of detecting activations in fMRI sequences that require a minimum of hypotheses and avoid any premodelling of the expected signal. In particular, we try to avoid as much as possible linear models. The sensitivity of the methods with respect to signal autocorrelation is investigated, in order to reduce or control it. Considering a experimental block design, a key point is the ability of taking into account transitions between different signal levels, but still without the use of predefined impulse response. The methods that we propose are based on wellknown Anova and information theoretical models. The problem of statistical test validation is also studied and partly solved. The power of these methods seems high enough to avoid any smoothing, spatial or temporal, of the data. Once an