Pixel-wise perfusion quantification from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) might allow detection and evaluation of myocardial perfusion deficits beyond visual analysis. However, relationships between different quantification approaches were not thoroughly studied. The aim of this thesis was to determine and compare semi-quantitative (up-slope, area under the curve and signal intensity maximum) and quantitative (deconvolution using Fermi modeling, Tikhonov regularization with B-splines and ARMA modeling) perfusion parameter maps from motion-corrected myocardial DCE-MRI data acquired in patients with coronary heart disease under resting condition. Visual analysis for subendocardial perfusion deficits revealed the best sensitivity (100%) and specificity (100%) for signal intensity maximum maps. For no type of perfusion map dark rim artifacts were misinterpreted as perfusion deficits. Whereas semi-quantitative and quantitative parameters demonstrated only moderate correlations both pixel-based and patient-based, the correlations between different quantitative perfusion maps were strong. However, mean myocardial perfusion values of 0.72±0.13 (Fermi), 0.67±0.10 (Tikhonov) and 0.84±0.16 ml/min/g (ARMA) differed significantly. Consequently, pixel-wise myocardial perfusion quantification is feasible with any of the studied approaches, where practical aspects favor the deconvolution using Fermi modeling. Semi-quantitative parameters do not completely reflect quantitative myocardial perfusion, but signal intensity maximum maps represent a valuable tool for detection of perfusion deficits.
|Qualification||Master of Science|
|Publication status||Published - 2016|
- dynamic contrast-enhanced magnetic resonance imaging
- myocardial perfusion
- signal intensity to concentration conversion
- perfusion parameters