Reconstruction of DSC-MRI Data from Sparse Data Exploiting Temporal Redundancy and Contrast Localization

In order to asses brain perfusion, one of the available methods is the estimation of parameters such as cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) from Dynamic Susceptibility Contrast-MRI (DSC-MRI). This estimation requires both high temporal resolution to capture the rapid tracer kinetic, and high spatial resolution to detect small impairments and reliably discriminate boundaries.With this inmind, we propose a compressed sensing approach to decrease the acquisition time without sacrificing the reconstruction, especially in the region affected by tracer passage. To this end we propose the utilization of an available TVL1-L2 minimization scheme with a novel additional term that introduce the information on the volume at baseline (no tracer). We show on simulated data the benefit of such a scheme, that is able to achieve an accurate reconstruction even at high acceleration (x16), with a RMSE of 2.8, 10 times lower than the error obtained with the original reconstruction