High time-resolved cardiac functional imaging using temporal regularization for small animal on a clinical 3T scanner.

Accurate assessment of mice cardiac function with magnetic resonance imaging is essential for longitudinal studies and for drug development related to cardiovascular diseases. Whereas dedicated small animal MR scanners are not readily available, it would be a great advantage to be able to perform cardiac assessment on clinical systems, in particular, in the context of translational research. However, mouse imaging remains challenging since it requires both high spatial and temporal resolutions, while gradient performances of clinical scanners often limit the reachable parameters.

Spread spectrum magnetic resonance imaging.

We propose a novel compressed sensing technique to accelerate the magnetic resonance imaging (MRI) acquisition process. The method, coined spread spectrum MRI or simply s(2)MRI, consists of premodulating the signal of interest by a linear chirp before random k-space under-sampling, and then reconstructing the signal with nonlinear algorithms that promote sparsity. The effectiveness of the procedure is theoretically underpinned by the optimization of the coherence between the sparsity and sensing bases.

Laser-Doppler flowmetry at large interoptode spacing in human tibia diaphysis: Monte Carlo simulations and preliminary experimental results.

Laser-Doppler flowmetry (LDF) is an outstanding tool to monitor blood flow in a continuous and non-invasive way. In this work, we study LDF at large interoptode spacing applied to a human bone model (i.e.

Lung texture classification using locally-oriented Riesz components.

We develop a texture analysis framework to assist radiologists in interpreting high-resolution computed tomography (HRCT) images of the lungs of patients affected with interstitial lung diseases (ILD). Novel texture descriptors based on the Riesz transform are proposed to analyze lung texture without any assumption on prevailing scales and orientations. A global classification accuracy of 78.

White-matter connectivity between face-responsive regions in the human brain.

Face recognition is of major social importance and involves highly selective brain regions thought to be organized in a distributed functional network. However, the exact architecture of interconnections between these regions remains unknown. We used functional magnetic resonance imaging to identify face-responsive regions in 22 participants and then employed diffusion tensor imaging with probabilistic tractography to establish the white-matter pathways between these functionally defined regions.

Steerable pyramids and tight wavelet frames in L2(R(d)).

We present a functional framework for the design of tight steerable wavelet frames in any number of dimensions. The 2-D version of the method can be viewed as a generalization of Simoncelli's steerable pyramid that gives access to a larger palette of steerable wavelets via a suitable parametrization. The backbone of our construction is a primal isotropic wavelet frame that provides the multiresolution decomposition of the signal.

Nonlocal means with dimensionality reduction and SURE-based parameter selection.

Nonlocal means (NLM) is an effective denoising method that applies adaptive averaging based on similarity between neighborhoods in the image. An attractive way to both improve and speed-up NLM is by first performing a linear projection of the neighborhood. One particular example is to use principal components analysis (PCA) to perform dimensionality reduction.

Impact of transient emotions on functional connectivity during subsequent resting state: a wavelet correlation approach.

The functional properties of resting brain activity are poorly understood, but have generally been related to self-monitoring and introspective processes. Here we investigated how emotionally positive and negative information differentially influenced subsequent brain activity at rest. We acquired fMRI data in 15 participants during rest periods following fearful, joyful, and neutral movies.

EEG microstate sequences in healthy humans at rest reveal scale-free dynamics.

Recent findings identified electroencephalography (EEG) microstates as the electrophysiological correlates of fMRI resting-state networks. Microstates are defined as short periods (100 ms) during which the EEG scalp topography remains quasi-stable; that is, the global topography is fixed but strength might vary and polarity invert. Microstates represent the subsecond coherent activation within global functional brain networks.

Adolescent resting state networks and their associations with schizotypal trait expression.

The rising interest in temporally coherent brain networks during baseline adult cerebral activity finds convergent evidence for an identifiable set of resting state networks (RSNs). To date, little is know concerning the earlier developmental stages of functional connectivity in RSNs. This study's main objective is to characterize the RSNs in a sample of adolescents.

A new method to measure local oxygen consumption in human skeletal muscle during dynamic exercise using near-infrared spectroscopy.

Near infrared spectroscopy (NIRS) can readily report on changes in blood volume and oxygenation. However, it has proved more problematic to measure real-time changes in blood flow and oxygen consumption. Here we report the development of a novel method using NIRS to measure local oxygen consumption in human muscle.

Decoding brain states from fMRI connectivity graphs.

Functional connectivity analysis of fMRI data can reveal synchronised activity between anatomically distinct brain regions. Here, we extract the characteristic connectivity signatures of different brain states to perform classification, allowing us to decode the different states based on the functional connectivity patterns. Our approach is based on polythetic decision trees, which combine powerful discriminative ability with interpretability of results.

A fast time-domain algorithm for the assessment of tissue blood flow in laser-Doppler flowmetry.

In this study, we derive a fast, novel time-domain algorithm to compute the nth-order moment of the power spectral density of the photoelectric current as measured in laser-Doppler flowmetry (LDF). It is well established that in the LDF literature these moments are closely related to fundamental physiological parameters, i.e.

Triplet imaging of oxygen consumption during the contraction of a single smooth muscle cell (A7r5).

The measurement of tissue and cell oxygenation is important for understanding cell metabolism. We have addressed this problem with a novel optical technique, called triplet imaging, that exploits oxygen-induced triplet lifetime changes and is compatible with a variety of fluorophores. A modulated excitation of varying pulse widths allows the extraction of the lifetime of the essentially dark triplet state using a high-fluorescence signal intensity.

BOLD correlates of EEG topography reveal rapid resting-state network dynamics.

Resting-state functional connectivity studies with fMRI showed that the brain is intrinsically organized into large-scale functional networks for which the hemodynamic signature is stable for about 10s. Spatial analyses of the topography of the spontaneous EEG also show discrete epochs of stable global brain states (so-called microstates), but they remain quasi-stationary for only about 100 ms. In order to test the relationship between the rapidly fluctuating EEG-defined microstates and the slowly oscillating fMRI-defined resting states, we recorded 64-channel EEG in the scanner while subjects were at rest with their eyes closed.

Wavelet steerability and the higher-order Riesz transform.

Our main goal in this paper is to set the foundations of a general continuous-domain framework for designing steerable, reversible signal transformations (a.k.a.

Multiresolution monogenic signal analysis using the Riesz-Laplace wavelet transform.

The monogenic signal is the natural 2-D counterpart of the 1-D analytic signal. We propose to transpose the concept to the wavelet domain by considering a complexified version of the Riesz transform which has the remarkable property of mapping a real-valued (primary) wavelet basis of L(2) (R(2)) into a complex one. The Riesz operator is also steerable in the sense that it give access to the Hilbert transform of the signal along any orientation.

The photo-electric current in laser-Doppler flowmetry by Monte Carlo simulations.

Monte Carlo (MC) simulations significantly contributed to a better understanding of laser-Doppler flowmetry (LDF). Here it is shown that the data obtained from standard MC simulations can be reinterpreted and used to extract more information such as the photo-electric current (i(t)). This is important because i(t) is the starting point for evaluating any existing or new algorithm to be used in LDF instrumentation.

Decoding of emotional information in voice-sensitive cortices.

The ability to correctly interpret emotional signals from others is crucial for successful social interaction. Previous neuroimaging studies showed that voice-sensitive auditory areas activate to a broad spectrum of vocally expressed emotions more than to neutral speech melody (prosody). However, this enhanced response occurs irrespective of the specific emotion category, making it impossible to distinguish different vocal emotions with conventional analyses.

Invariances, Laplacian-like wavelet bases, and the whitening of fractal processes.

In this contribution, we study the notion of affine invariance (specifically, invariance to the shifting, scaling, and rotation of the coordinate system) as a starting point for the development of mathematical tools and approaches useful in the characterization and analysis of multivariate fractional Brownian motion (fBm) fields. In particular, using a rigorous and powerful distribution theoretic formulation, we extend previous results of Blu and Unser (2006) to the multivariate case, showing that polyharmonic splines and fBm processes can be seen as the (deterministic vs stochastic) solutions to an identical fractional partial differential equation that involves a fractional Laplacian operator. We then show that wavelets derived from polyharmonic splines have a behavior similar to the fractional Laplacian, which also turns out to be the whitening operator for fBm fields.

Complex wavelet bases, steerability, and the marr-like pyramid.

Our aim in this paper is to tighten the link between wavelets, some classical image-processing operators, and David Marr's theory of early vision. The cornerstone of our approach is a new complex wavelet basis that behaves like a smoothed version of the Gradient-Laplace operator. Starting from first principles, we show that a single-generator wavelet can be defined analytically and that it yields a semi-orthogonal complex basis of L2 (R2), irrespective of the dilation matrix used.

The pairing of a wavelet basis with a mildly redundant analysis via subband regression.

A distinction is usually made between wavelet bases and wavelet frames. The former are associated with a one-to-one representation of signals, which is somewhat constrained but most efficient computationally. The latter are over-complete, but they offer advantages in terms of flexibility (shape of the basis functions) and shift-invariance.

Dynamic PET reconstruction using wavelet regularization with adapted basis functions.

Tomographic reconstruction from positron emission tomography (PET) data is an ill-posed problem that requires regularization. An attractive approach is to impose an l(1) -regularization constraint, which favors sparse solutions in the wavelet domain. This can be achieved quite efficiently thanks to the iterative algorithm developed by Daubechies et al.

Model-based 2.5-d deconvolution for extended depth of field in brightfield microscopy.

Due to the limited depth of field of brightfield microscopes, it is usually impossible to image thick specimens entirely in focus. By optically sectioning the specimen, the in-focus information at the specimen's surface can be acquired over a range of images. Commonly based on a high-pass criterion, extended-depth-of-field methods aim at combining the in-focus information from these images into a single image of the texture on the specimen's surface.

Reversible, fast, and high-quality grid conversions.

A new grid conversion method is proposed to resample between two 2-D periodic lattices with the same sampling density. The main feature of our approach is the symmetric reversibility, which means that when using the same algorithm for the converse operation, then the initial data is recovered exactly. To that purpose, we decompose the lattice conversion process into (at most) three successive shear operations.