Parameter estimation

Super resolution reconstruction for quantitative imaging

We developed a super-resolution reconstruction methodology for diffusion and relaxometry MRI. It allows to improved the trade-off between acquisition time, spatial resolution and SNR.

Multi-tissue constrained spherical deconvolution

Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space.

Iterative reweighted linear least squares for the robust estimation of diffusion magnetic resonance parameters

Diffusion weighted magnetic resonance (DW-MR) imaging suffers from physiological noise such as artifacts caused by motion or system instabilities. This obviates the need for robust diffusion parameter estimation techniques. In the past, several techniques have been presented including RESTORE and iRESTORE. However, these techniques are based on nonlinear estimators, and are consequently computationally intensive. We present a new robust, iteratively reweighted linear least squares (IRLLS) estimator.

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