Diffusion magnetic resonance imaging (dMRI) is a non-invasive method that allows connectivity mapping of the brain. However, despite major advances in this field, accurate inference of these patterns and its applicability within a clinical context is still in its early stages. This thesis describes a conceptually novel method for reconstructing neuronal pathways inside the brain from diffusion-...

Introduction Several deconvolution methods have been proposed to increase the angular resolution of HARDI or QBI [1][2]. However, there is no deconvolution method directly applied to diffusion ODF without resorting to spherical decomposition. In this study, we developed a deconvolution method that could be directly applied to diffusion ODF, thus extending its applicability to other q-space meth...

For the purpose of the ISBI HARDI reconstruction challenge 2013 and for the heavyweight category, we reconstructed the diffusion datasets using two methods: a) Generalized Q-sampling Imaging 2 [1], [2] with spherical deconvolution [3],[4] (GQID), and b) Diffusion Spectrum Imaging with Deconvolution [5] (DSID). GQI2 provides a direct analytical formula to calculate the solid angle ODF (ψGQI2) of...

The wavelength dependence of the incoherent point spread function in a wide-field microscope was investigated experimentally. Dispersion in the sample and optics can lead to significant changes in the point spread function as wavelength is varied over the range commonly used in fluorescence microscopy. For a given sample, optical conditions can generally be optimized to produce a point spread f...

In this paper we present a novel method for multi-fiber reconstruction given a diffusion-weighted MRI dataset. There are several existing methods that employ various spherical deconvolution kernels for achieving this task. However the kernels in all of the existing methods rely on certain assumptions regarding the properties of the underlying fibers, which introduce inaccuracies and unnatural l...

We consider the problem of estimating a density of probability from indirect data in the spherical convolution model. We aim at building an estimate of the unknown density as a linear combination of functions of an overcomplete dictionary. The procedure is devised through a well-calibrated `1-penalized criterion. The spherical deconvolution setting has been barely studied so far, and the two ma...

Fiber orientation is the key information in diffusion tractography. Several deconvolution methods have been proposed to obtain fiber orientations by estimating a fiber orientation distribution function (ODF). However, the L 2 regularization used in deconvolution often leads to false fibers that compromise the specificity of the results. To address this problem, we propose a method called diffus...

This report reviews, in light of signal processing, the problem of relighting a Lambertian convex object with distant light source, whose crucial task is the decomposition of reflectance function into albedos (reflection coefficients) and lighting, based on a set of images and the 3-D geometry of the object from which the images were taken. A reflectance function is the result of filtering a li...