Parameter estimation

In quantitative magnetic resonance T1 mapping, the Variable Flip Angle (VFA) steady state spoiled gradient recalled echo (SPGR) imaging technique is popular as it provides a series of high resolution T1 weighted images in a clinically feasible time. Fast, linear methods that estimate T1 maps from these weighted images have been proposed, such as DESPOT1 and iterative reweighted linear least squares (IRWLLS).

We developed a super-resolution reconstruction methodology for diffusion and relaxometry MRI. It allows to improve the trade-off between acquisition time, spatial resolution and SNR. From a set of low resolution multi-slice images, each with a different (diffusion or relaxometry) contrast, an image is reconstructed with a much higher spatial resolution compared to a 3D image that is directly acquired in the same time frame.

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.