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Abstract #1477

Improved Image Quality when estimating Perfusion Parameters using Bayesian Fitting Algorithm

Irene Klærke Mikkelsen1, Anna Tietze1,2, Lars Ribe1, Anne Obel3, Mikkel Bo Hansen1, and Kim Mouridsen1

1CFIN, Aarhus University, Aarhus, Denmark, 2Dept. of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark, 3Neuroradiology, Aarhus University Hospital, Aarhus, Denmark

Dynamic Contrast Enhanced Perfusion Imaging (DCE) allows for quantification of the blood-brain barrier integrity in tumor patients. A key post-processing step is to fit a hemodynamic model to DCE data. The fitting procedure can, however, cause spurious voxels and image degradation. We compared the widely used Levenberg-Marquardt fitting algorithm to a Bayesian algorithm. Image quality was assessed in 42 tumor patients. The Bayesian approach provided the highest image quality scores. This was confirmed in simulated data with fewer outliers (spurious voxels) when using the Bayesian approach. The hemodynamic two-compartment model that separates cerebral blood flow and leakage, provides reliable Ve images, when the robust Bayesian fitting algorithm is used.

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