Perfusion estimation using MRI-based measurements and a porous media flow model
The measurement of perfusion and filtration of blood in biological tissue give rise to important clinical parameters used in diagnosis, follow-up, and therapy. In this paper we address techniques for perfusion analysis using processed contrast agent concentration data from dynamic MRI acquisitions. New methodology for analysis is evaluated and verified using synthetic data generated on a tissue geometry. Author summaryAccurate knowledge of tissue perfusion is crucial for proper diagnostics and treatment of several medical disorders. Traditional methods based on medical imaging are fast, but usually lack precision and robustness. In this paper, we address methodology to develop better diagnosis and treatment strategies for malignant tumors and stroke where blood perfusion may be altered. In our work, mathematical models for estimating perfusion are calibrated using magnetic resonance imaging (MRI) data, and more accurate representations of tissue parameters are provided. This methodology is a step towards minimal invasive and individually tailored diagnosis and treatment. We demonstrate the methodology with a twin-experiment using models of different complexity for generating data and estimating the tissue parameters. Both models are based on a mathematical description of how fluids flows in a porous medium, where the data-generating model uses higher resolution and a network representation of blood vessels than the estimating model. The calibration of unknown tissue parameters is done using a statistical framework, and the choice of methodology is motivated by applications from sub-surface reservoir characterization.