Section: New Results
Image processing on Diffusion Weighted Magnetic Resonance Imaging
Correction of Distortions
Diffusion weighted MR images are acquired using specific magnetic gradients. Multiple MR acquisition are required to get enough orientation samples, in order to correctly characterise the diffusion process in the 3D space. The diffusion sensitising gradients create eddy currents in the receiver coil. These induced currents change the geometry of the acquired images by creating spatial distortions. In order to estimate properly the diffusion process for each voxel of the image the distortions must be compensated. A widely used approach for this purpose is the registration of each diffusion-weighted image to the first diffusion un-weighted image of the sequence. This registration consists in maximising a similarity criterion between the intensities of the images to be matched. In this context, efficient optimisation methods are needed to obtain good performances. We introduce a new optimisation algorithm (called NEWUOA) to address this registration problem  .
Diffusion Weighted and Diffusion Tensor Image Denoising
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 interpretation. We propose different methods based on the Non-Local (NL) means algorithm. This approach uses the natural redundancy of information in images to remove the noise. The results show that the intensity based NL-means approaches give better results in the context of DT-MRI than other classical denoising methods, such as Gaussian Smoothing, Anisotropic Diffusion and Total Variation  .
Methods for processing and representation of diffusion tensor MRI data
For the last two years, we have been developing a general purpose application for the processing of diffusion-weighted (DW) MR images. State-of-the-art techniques have been implemented to allow for individual and group studies. These techniques include: import/export routines, correction of image artifacts, tensor field estimation, computation of quantitative indices (fractional anisotropy, mean diffusivity, relative anisotropy, etc.), spatial normalization (both intra- and inter-subject, linear and non-linear), quantitative tractography (deterministic, stochastic, etc.), and visualization (e.g. ellipsoids or more general glyphs).
Computation of the mid-sagittal plane in diffusion tensor MR brain images
We proposed a method for the automated computation of the mid-sagittal plane of the brain in diffusion tensor MR images. We proposed to estimate this plane as the one that best matches the two hemispheres of the brain by reflection symmetry. This is done via the automated minimisation of a correlation-type global criterion over the tensor image. The minimisation is performed using the NEWUOA algorithm in a multiresolution framework. We validated our algorithm on synthetic diffusion tensor MR images. We quantitatively compared this computed plane with similar planes obtained from scalar diffusion images (such as FA and ADC maps) and from the B0 image (that is, without diffusion sensitisation). Finally, we showed some results on real diffusion tensor MR images.
Motor and sensory fibers for hand function - a diffusion tensor MRI study
We acquired 3D anatomical (T1-weighted), functional (fMRI) and diffusion-tensor (DTI) MR images for a quantitative study of the cortical hand areas in a population of 40 healthy right-handed and left-handed subjects. First, specific paradigms were used to identify cortical motor and sensory areas of the left and right hand in fMRI. DTI tractography was then performed to delineate the fiber tracts connecting these fMRI-defined regions of interest with the brain stem. Quantitative indices (fractional anisotropy, mean diffusivity) were then computed over the tracts of interest and statistics were performed to assess differences between right and left hands in the population of right and left-handed subjects.