Optimization of Magnetic Resonance Fingerprinting with Subspace Reconstruction

Cramér-Rao Lower Bound has been used to optimize the acquisition design for MRF, but the effect from undersampling and reconstruction has not been taken into consideration. In this work, we evaluated the estimation error and standard deviation using CRLB optimized acquisition parameters with spiral undersampling trajectory and subspace reconstruction. The results demonstrated that CRLB produced lower estimation variation but is sensitive to the selected number of subspaces. A rank that is too low or two high an increase the estimation error. The dictionary match on coefficient maps provided lower estimation error and variation compared to match on whole time series.