Reconstruction of local frequencies for recovering the unwrapped phase in optical interferometry.

In optics, when interferograms or digital holograms are recorded and their phase is recovered, it is common to obtain a wrapped phase with some errors, noise and artifacts such as singularities due to the non linearities of the demodulation process. This paper shows how to reconstruct the frequency field of the wrapped phase by using adaptive Gabor filters. Gabor filters are Gaussian quadrature filters tuned in at a certain frequency. We adapt these Gabor filters by tuning them locally and estimating the frequency using wrapped finite differences of the estimated phase. Doing this process iteratively, the frequency estimation is refined and smoothed. The unwrapped phase is easily recovered by integrating the recovered frequency field using, for example, a simple line raster integration. We don't have problems with phase inconsistencies or residues while integrating the phase, because these are removed. The obtained unwrapped phase is clean, consistent and practically error-free. We show estimation errors with simulated data and the performance of the proposed method using real-world recorded wavefronts.

Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison.

A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the 'White Matter Modeling Challenge' during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion-based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non-Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal-predicting strategies, such as bootstrapping or cross-validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.

The hemodynamic response to acoustically modified syllables in premature and full term newborn infants acquired by near infrared spectroscopy / Respuesta hemodinámica a sílabas modificadas acústicamente en recién nacidos prematuros y a término adquirida por espectroscopía del infrarrojo cercano / Resposta hemodinâmica a sílabas modificadas acusticamente em recém-nascidos prematuros e a termo adquirida por espectroscopia do infravermelho próximo

This research assesses, in newborns, the hemodynamic response to acoustically modified syllables (pronounced in a prolonged manner), versus the response to unmodified syllables (pronounced at a normal rate). The aim was to assess which of these stimulation conditions produced better syllable discrimination in two groups of neonates: 13 preterm (mean gestational age 30 weeks, SD 3 weeks), and 13 full term newborns (mean age 38 weeks, SD 1 week). Syllable discrimination, in each condition, was assessed by using an oddball paradigm (equal syllable trials vs. different syllable trials). The statistical analysis was based on the comparison between the hemodynamic response [oxyHbO] obtained by Near Infrared Spectroscopy (NIRS) to different syllable trials vs. equal syllable trials, in each condition. The modified syllable condition was better in producing trial discrimination in both groups. The amplitude of the hemodynamic response to the different syllable trials was greater than the one to the equal syllable trials: for term infants, t = 2.59, p = 0.024, and for preterm t = 2.38, p = 0.035. This finding occurred in the left temporal lobe. These data suggest that the modified syllables facilitate processing of phonemes from birth.

Esta investigación evalúa, en neonatos, la respuesta hemodinámica ante sílabas modificadas acústicamente (pronunciadas de manera prolongada) en comparación con la respuesta a sílabas no modificadas (pronunciadas a una velocidad normal). El objetivo fue evaluar cuál de estas condiciones de estimulación producía una mejor discriminación silábica en dos grupos de neonatos: 13 prematuros (edad gestacional promedio de 30 semanas, DE 3 semanas) y 13 nacidos a término (edad gestacional promedio de 38 semanas, DE 1 semana). La discriminación de sílabas, en cada condición, se evaluó mediante un paradigma oddball (ensayos con sílabas iguales vs. ensayos con sílaba diferente). El análisis estadístico se basó en la comparación de la respuesta hemodinámica [oxyHb] obtenida por espectroscopia de infrarrojo cercano (NIRS) ante ensayos con sílabas iguales Vs. ensayos con una sílaba diferente en cada condición. Se encontró que la condición de sílabas modificadas obtuvo mejores resultados para la discriminación entre ensayos en ambos grupos. La amplitud de la respuesta hemodinámica ante el ensayo con una sílaba diferente fue significativamente mayor que ante el ensayo con sílabas iguales: en recién nacidos a término, t = 2,59, p = 0,024 y en los prematuros, t = 2,38, p = 0,035. Este hallazgo ocurrió en el lóbulo temporal izquierdo. Estos datos sugieren que las sílabas modificadas facilitan el procesamiento de fonemas desde el nacimiento.

Esta pesquisa avalia, em neonatos, a resposta hemodinâmica diante sílabas modificadas acusticamente (pronunciadas de maneira prolongada) em comparação com a resposta a sílabas não modificadas (pronunciadas a uma velocidade normal). O objetivo foi avaliar qual destas condições de estimulação produzia uma melhor discriminação silábica em dois grupos de neonatos: 13 prematuros (idade gestacional média de 30 semanas, DE 3 semanas) e 13 nascidos a termo (idade gestacional média de 38 semanas, DE 1 semana). A discriminação de sílabas, em cada condição, foi avaliada mediante um paradigma oddball (ensaios com sílabas iguais vs. ensaios com sílaba diferente). A análise estadística se baseou na comparação da resposta hemodinâmica [oxyHb] obtida por espectroscopia de infravermelho próximo (NIRS) ante ensaios com sílabas iguais Vs. ensaios com uma sílaba diferente em cada condição. Encontrou-se que a condição de sílabas modificadas obteve melhores resultados para a discriminação entre ensaios em ambos os grupos. A amplitude da resposta hemodinâmica ante o ensaio com uma sílaba diferente foi significativamente maior que perante o ensaio com sílabas iguais: em recém-nascidos a termo, t = 2,59, p = 0,024 e nos prematuros, t = 2,38, p = 0,035. Este descobrimento ocorreu no lóbulo temporal esquerdo. Estes dados sugerem que as sílabas modificadas facilitam o processamento de fonemas desde o nascimento.

A maximum linear separation criterion for the analysis of neurophysiological data.

In this paper we propose an approach for the extraction of features that differentiate two populations or two experimental conditions in a neurophysiological experiment. These features consist of summarizing variables defined as total activity (e.g., total normalized log-power), computed over sets of sites in a discrete domain, such as the time-frequency-topography space. These sets are obtained as those that maximize the linear separation between the two populations, and the corresponding maps provide information that may complement that obtained by standard procedures, such as statistical parametric mapping. It is shown experimentally, using both simulated and real data, that the proposed approach may provide useful information even when the standard procedures fail, due to the conservative nature of the multiple comparison correction that must be applied in the later case.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology--specifically, morphological erosions and dilations--that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level. The method is easily adapted to permutation-based procedures (with the usual restrictions), and therefore does not require strong assumptions about the distribution and spatio-temporal correlation structure of the data. Some examples of applications to synthetic data, including realistic fMRI simulations, as well as to real fMRI and electroencephalographic data are presented, illustrating the power of the presented technique. Comparisons with other methods that combine voxel intensity and cluster size, as well as some extensions of the method presented here based on their basic ideas are presented as well.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology - specifically, morphological erosions and dilations - that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level. The method is easily adapted to permutation-based procedures (with the usual restrictions), and therefore does not require strong assumptions about the distribution and spatio-temporal correlation structure of the data. Some examples of applications to synthetic data, including realistic fMRI simulations, as well as to real fMRI and electroencephalographic data are presented, illustrating the power of the presented technique. Comparisons with other methods that combine voxel intensity and cluster size, as well as some extensions of the method presented here based on their basic ideas are presented as well.

Region-based current-source reconstruction for the inverse EEG problem.

This paper presents a new method for the reconstruction of current sources for the electroencephalography (EEG) inverse problem, which produces reconstructed sources, which are confined to a few anatomical regions. The method is based on a partition of the gray matter into a set of regions, and in the construction of a simple linear model for the potential produced by feasible source configurations inside each one of these regions. The proposed method computes the solution in two stages: in the first one, a subset of active regions is found so that the combined potentials produced by sources inside them approximate the measured potential data. In the second stage, a detailed reconstruction of the current sources inside each active region is performed. Experimental results with synthetic data are presented, which show that the proposed scheme is fast, computationally efficient and robust to noise, producing results that are competitive with other published methods, especially when the current sources are effectively distributed in few anatomical regions. The proposed method is also validated with real data from an experiment with visual evoked potentials.

Bayesian segmentation of range images of polyhedral objects using entropy-controlled quadratic Markov measure field models.

We present a method based on Bayesian estimation with prior Markov random field models for segmentation of range images of polyhedral objects. This method includes new ways to determine the confidence associated with the information given for every pixel in the image as well as an improved method for localization of the boundaries between regions. The performance of the method compares favorably with other state-of-the-art procedures when evaluated using a standard benchmark.

Entropy-controlled quadratic markov measure field models for efficient image segmentation.

We present a new Markov random field (MRF) based model for parametric image segmentation. Instead of directly computing a label map, our method computes the probability that the observed data at each pixel is generated by a particular intensity model. Prior information about segmentation smoothness and low entropy of the probability distribution maps is codified in the form of a MRF with quadratic potentials so that the optimal estimator is obtained by solving a quadratic cost function with linear constraints. Although, for segmentation purposes, the mode of the probability distribution at each pixel is naturally used as an optimal estimator, our method permits the use of other estimators, such as the mean or the median, which may be more appropriate for certain applications. Numerical experiments and comparisons with other published schemes are performed, using both synthetic images and real data of brain MRI for which expert hand-made segmentations are available. Finally, we show that the proposed methodology may be easily extended to other problems, such as stereo disparity estimation.

Exploration of event-induced EEG phase synchronization patterns in cognitive tasks using a time-frequency-topography visualization system.

In this paper, we present a method for the study of synchronization patterns measured from EEG scalp potentials in psychophysiological experiments. This method is based on various techniques: a time-frequency decomposition using sinusoidal filters which improve phase accuracy for low frequencies, a Bayesian approach for the estimation of significant synchrony changes, and a time-frequency-topography visualization technique which allows for easy exploration and provides detailed insights of a particular experiment. Particularly, we focus on in-phase synchrony using an instantaneous phase-lock measure. We also discuss some of the most common methods in the literature, focusing on their relevance to long-range synchrony analysis; this discussion includes a comparison among various synchrony measures. Finally, we present the analysis of a figure categorization experiment to illustrate our method.

Robust nonrigid multimodal image registration using local frequency maps.

Automatic multi-modal image registration is central to numerous tasks in medical imaging today and has a vast range of applications e.g., image guidance, atlas construction, etc. In this paper, we present a novel multi-modal 3D non-rigid registration algorithm where in 3D images to be registered are represented by their corresponding local frequency maps efficiently computed using the Riesz transform as opposed to the popularly used Gabor filters. The non-rigid registration between these local frequency maps is formulated in a statistically robust framework involving the minimization of the integral squared error a.k.a. L2E (L2 error). This error is expressed as the squared difference between the true density of the residual (which is the squared difference between the non-rigidly transformed reference and the target local frequency representations) and a Gaussian or mixture of Gaussians density approximation of the same. The non-rigid transformation is expressed in a B-spline basis to achieve the desired smoothness in the transformation as well as computational efficiency. The key contributions of this work are (i) the use of Riesz transform to achieve better efficiency in computing the local frequency representation in comparison to Gabor filter-based approaches, (ii) new mathematical model for local-frequency based non-rigid registration, (iii) analytic computation of the gradient of the robust non-rigid registration cost function to achieve efficient and accurate registration. The proposed non-rigid L2E-based registration is a significant extension of research reported in literature to date. We present experimental results for registering several real data sets with synthetic and real non-rigid misalignments.

Half-quadratic cost functions for phase unwrapping.

We present a generic regularized formulation, based on robust half-quadratic regularization, for unwrapping noisy and discontinuous wrapped phase maps. Two cases are presented: the convex case and the nonconvex case. The unwrapped phase with the convex formulation is unique and robust to noise; however, the convex function solution deteriorates as a result of real discontinuities in phase maps. Therefore we also present a nonconvex formulation that, with a parameter continuation strategy, shows superior performance.

Exploratory EEG data analysis for psychophysiological experiments.

A method for the exploratory analysis of electroencephalographic (EEG) data for neurophysiological experiments is presented. It is based on a time-frequency decomposition of the EEG time series, which is measured by several electrodes in the scalp surface, and includes the computation of a statistic that measures the deviations of the log-power with respect to the pre-stimulus average; the computation of a significance index for these deviations; a new type of display (the time-frequency-topography plot) for the visualization of these indices, and the segmentation of the time-frequency plane into regions with uniform activation patterns. As a particular example, an experiment to study EEG changes during figure and word categorization is analyzed in detail.