Ignace Lemahieu

This work presents a spatial filtering method for the estimation of atrial fibrillation activity in the cutaneous electrocardiogram. A linear extraction filter is obtained by maximising the extractor output power on the significant spectral support of the signal of interest. An iterative procedure based on a quasi-maximum likelihood estimator is proposed to jointly estimate the significant spectral support and the extraction filter. Compared with a previously proposed spatio-temporal blind source separation method, our approach yields an improved atrial activity signal estimate as quantified by a higher spectral concentration of the extractor output. The proposed methodology can readily be adapted to signal extraction problems in other application domains.

A gold standard for the validation of diffusion weighted magnetic resonance imaging (DW-MRI) in brain white matter (WM) is essential for clinical purposes but still not available. Synthetic anisotropic fiber bundles are proposed as phantoms for the validation of DW-MRI because of their well-known structure, their long preservability and the possibility to create complex geometries such as curved and fiber crossings. A crucial question is how the different material properties and size of the fiber phantoms influence the outcome of the DW-MRI experiment. Several fiber materials are compared in this study. The effect of surface relaxation and internal gradients on the SNR is evaluated. In addition, the dependency of the fiber density and fiber radius on the diffusion properties is investigated.

Fan beam collimators can obtain a higher sensitivity without loss in resolution at the cost of a reduced field of view. The geometric response has been studied both analytically and with numerical and Monte Carlo simulations, but a fast and accurate Monte Carlo simulator for fan beam geometry is not available. The goal of this work is therefore to accelerate 99mTc fan beam simulations in Gate, with full MC modeling of the collimator and detector in order to retain the characteristic hexagonal hole pattern of the collimator. To this end, two problems need to be solved: the long calculation time of particle transport in fan beam collimator geometry and the lack of dedicated variance reduction techniques. The first problem is solved by a dedicated tracking algorithm for fan beam collimator. The second problem is solved by the introduction of fan beam forced detection with variable solid angles. Our methods were validated with both analog Gate simulations and measurements. A good agreement was found for the hexagonal hole pattern, energy spectra, spatial resolution and sensitivity.

EEG source analysis is a valuable tool for brain functionality research and for diagnosing neurological disorders, such as epilepsy. It requires a geometrical representation of the human head or a head model, which is often modeled as an isotropic conductor. However, it is known that some brain tissues, such as the skull or white matter, have an anisotropic conductivity. Many studies reported that the anisotropic conductivities have an influence on the calculated electrode potentials. However, few studies have assessed the influence of anisotropic conductivities on the dipole estimations. In this study, we want to determine the dipole estimation errors due to not taking into account the anisotropic conductivities of the skull and/or brain tissues. Therefore, head models are constructed with the same geometry, but with an anisotropically conducting skull and/or brain tissue compartment. These head models are used in simulation studies where the dipole location and orientation error is calculated due to neglecting anisotropic conductivities of the skull and brain tissue. Results show that not taking into account the anisotropic conductivities of the skull yields a dipole location error between 2 and 25 mm, with an average of 10 mm. When the anisotropic conductivities of the brain tissues are neglected, the dipole location error ranges between 0 and 5 mm. In this case, the average dipole location error was 2.3 mm. In all simulations, the dipole orientation error was smaller than 10 degrees . We can conclude that the anisotropic conductivities of the skull have to be incorporated to improve the accuracy of EEG source analysis. The results of the simulation, as presented here, also suggest that incorporation of the anisotropic conductivities of brain tissues is not necessary. However, more studies are needed to confirm these suggestions.

This paper presents a method for signal extraction based on conditional second-order moments of the output of the extraction filter. The estimator of the filter is derived from an approximate maximum likelihood criterion conditioned on a presence indicator of the source of interest. The conditional moment is shown to be a contrast function under the conditions that 1) all cross-moments of the same order between the source signal of interest and the other source signals are null and 2) that the source of interest has the largest conditional moment among all sources. For the two-source two-observation case, this allows us to derive the theoretical recovery bounds of the contrast when the conditional cross-moment does not vanish. A comparison with empirical results confirms these bounds. Simulations show that the estimator is quite robust to additive Gaussian distributed noise. Also through simulations, we show that the error level induced by a rough approximation of the presence indicator shows a strong similarity with that of additive noise. The robustness, with respect both to noise and to inaccuracies in the prior information about the source presence, guarantees a wide applicability of the proposed method.