Robust Evaluation of Contrast-Enhanced Imaging for Perfusion Quantification
MCML Authors
Andreas Mittermeier
Dr.
Abstract
Andreas Mittermeier
Dr.
Abstract
This thesis advances the quantification and prediction of hemodynamic parameters in dynamic contrast-enhanced (DCE) imaging through two innovative approaches. The Bayesian Tofts model (BTM) improves the reliability and uncertainty estimation of perfusion parameters, demonstrating its potential for enhanced treatment response assessment in cancer care. Additionally, the development of a deep learning model offers a promising alternative by directly predicting clinical endpoints from raw DCE-CT data, eliminating the need for traditional tracer-kinetic modeling and paving the way for more efficient and accurate clinical applications in stroke and other conditions. (Shortened.)
BibTeXKey: Mit23