Non Local Spatial and Angular Matching : a new denoising technique for diffusion MRI
Résumé
Diffusion Weighted Images (DWIs) datasets suffer from low Signal-to-Noise Ratio (SNR), especially at high b-values. Acquiring data at high b-values contains relevant information and is now of great interest for connectomics studies. High noise levels bias the measurements because of the non-Gaussian nature of the noise, which in turn can lead to a false and biased estimation of the diffusion parameters. Therefore, high SNR DWIs is important in order to draw meaningful conclusions in subsequent data or group analyses. The acquired DWIs differ between themselves, but still share the same underlying structure. It is also known that natural images are redundant and can be sparsified. We thus propose to use the redundancy of DWIs as a sparse representation to reduce the noise level and achieve a higher SNR using dictionary learning and sparse coding, without the need for additional acquisition time. We show quantitative results on the ISBI 2013 HARDI challenge phantom.