Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Undersampling Patterns on Derived Parameters for DCE- and DSC-MRI

Compressive sensing (CS) in Cartesian magnetic resonance imaging (MRI) involves random partial Fourier acquisitions. The random nature of these acquisitions can lead to variance in reconstruction errors. In quantitative MRI, variance in the reconstructed images translates to an uncertainty in the derived quantitative maps. We show that for a spatially regularized 2 ×-accelerated human breast CS DCE-MRI acquisition with a 192² matrix size, the coefficients of variation (CoVs) in voxel-level parameters due to the random acquisition are 1.1%, 0.96%, and 1.5% for the tissue parameters K^trans, v_e, and v_p, with an average error in the mean of -2.5%, -2.0%, and -3.7%, respectively. Only 5% of the acquisition schemes had a systematic underestimation larger than than 4.2%, 3.7%, and 6.1%, respectively. For a 2× -accelerated rat brain CS DSC-MRI study with a 64² matrix size, the CoVs due to the random acquisition were 19%, 9.5%, and 15% for the cerebral blood flow and blood volume and mean transit time, respectively, and the average errors in the tumor mean were 9.2%, 0.49%, and -7.0%, respectively. Across 11000 different CS reconstructions, we saw no outliers in the distribution of parameters, suggesting that, despite the random undersampling schemes, CS accelerated quantitative MRI may have a predictable level of performance.