Nicolas Wiest-Daesslé

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Diffusion-Weighted MRI (DW-MRI) is subject to random noise yielding measures that are different from their real values, and thus biasing the subsequently estimated tensors. The Non-Local Means (NLMeans) filter has recently been proposed to denoise MRI with high signal-to-noise ratio (SNR). This filter has been shown to allow the best restoration of image(More)
In this paper we study the impact of denoising the raw high angular resolution diffusion imaging (HARDI) data with the Non-Local Means filter adapted to Rician noise (NLMr). We first show that NLMr filtering improves robustness of apparent diffusion coefficient (ADC) and orientation distribution function (ODF) reconstructions from synthetic HARDI datasets.(More)
Diffusion tensor imaging (DT-MRI) is very sensitive to corrupting noise due to the non linear relationship between the diffusion-weighted image intensities (DW-MRI) and the resulting diffusion tensor. Denoising is a crucial step to increase the quality of the estimated tensor field. This enhanced quality allows for a better quantification and a better image(More)
A number of problems frequently encountered in brain image analysis can be conveniently solved within a registration framework, such as alignment of monoor multi-sequence Magnetic Resonance Images (MRI) for single or multiple subjects, computation of the cerebral mid-sagittal plane in anatomical or diffusion-tensor MRI, correction of acquisition distortions(More)
We propose and evaluate a new block-matching strategy for rigid-body registration of multimodal or multisequence medical images. The classical algorithm first matches points of both images by maximizing the iconic similarity of blocks of voxels around them, then estimates the rigid-body transformation best superposing these matched pairs of points, and(More)
We propose to use a recently introduced optimisation method in the context of rigid registration of medical images. This optimisation method, introduced by Powell and called NEWUOA, is compared with two other widely used algorithms: Powell’s direction set and Nelder-Mead’s downhill simplex method. This paper performs a comparative evaluation of the(More)
Diffusion tensor imaging permits study of white matter fibre bundles; however, its main limitation is lack of validation on anatomical data, especially in crossing fibre regions. Our study aimed to compare four deterministic tractography algorithms used in clinical routine. We studied the corticospinal tract, the bundle mediating voluntary movement. Our(More)
We propose an iterative two-step method to compute a diffeomorphic non-rigid transformation between images of anatomical structures with rigid parts, without any user intervention or prior knowledge on the image intensities. First we compute spatially sparse, locally optimal rigid transformations between the two images using a new block matching strategy(More)
Cerebral hemispheres represent both structural and functional asymmetry, which differs among right- and left-handers. The left hemisphere is specialised for language and task execution of the right hand in right-handers. We studied the corticospinal tract in right- and left-handers by diffusion tensor imaging and tractography. The present study aimed at(More)
This paper gives an overview of the evolution of clinical neuroinformatics in the domain of neurosurgery. It shows how image guided neurosurgery (IGNS) is evolving according to the integration of new imaging modalities before, during and after the surgical procedure and how this acts as the premise of the Operative Room of the future. These different(More)