Accelerating Multi-Component Relaxometry in Steady State with an Application of Constrained Reconstruction in Parametric Dimension

Julia V. Velikina¹, Samuel A. Hurley¹, Andrew L. Alexander¹, Alexey A. Samsonov^1,2

We propose a novel way to accelerate multi-component relaxometry by a factor of 4 by applying variable density undersampling in the flip angle dimension of the acquired SPGR and bSSFP data and then reconstructing the obtained incomplete data sets using constrained reconstruction in the parametric (flip angle) dimension. Finally, parameter maps, such as myelin water fraction, are derived from the reconstructed image series. We compare our results with the ones obtained using parallel imaging alone and fully sampled data.