Tracer kinetic models as temporal constraints during brain tumor DCE-MRI reconstruction

Purpose To apply tracer kinetic models as temporal constraints during reconstruction of under-sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). Methods A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under-sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in-vivo 3T datasets. The performances of the proposed under-sampled reconstruction scheme and an existing compressed sensing-based temporal finite-difference (tFD) under-sampled reconstruction were compared against the fully sampled inverse Fourier Transform-based reconstruction. Results Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts-Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO-based experiments showed good fidelity in recovery of kinetic maps from 20-fold under-sampled data. In-vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under-sampled reduction factors >= 20. Conclusions Tracer kinetic models can be applied as temporal constraints during brain tumor DCE-MRI reconstruction. The proposed under-sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters.