Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects

Purpose: To evaluate the impact of (k,t) data sampling on the variance of tracer-kinetic parameter (TK) estimation in high-resolution whole-brain dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints.

Methods: Three anatomically and physiologically realistic brain-tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone-based, lattice, pseudo-random, and pseudo-radial; with 50-time frames and 4-fold to 25-fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK parameters were estimated by indirect estimation (i.e., image-time-series reconstruction followed by model fitting), and direct estimation from the under-sampled data. We evaluated methods based on the Cramér-Rao bound and Monte-Carlo simulations, over the range of signal-to-noise ratio (SNR) seen in clinical brain DCE-MRI.

Results: Lattice-based sampling provided the lowest SDs, followed by pseudo-random, pseudo-radial, and zone-based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo-random sampling resulted in 19% higher averaged SD compared to lattice-based sampling. Zone-based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice-based and pseudo-random sampling up to undersampling factors of 25.

Conclusion: Lattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice-based and pseudo-random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25-fold undersampling.