Automated macrovessel artifact correction in dynamic susceptibility contrast magnetic resonance imaging using independent component analysis

Dynamic susceptibility contrast (DSC) MRI is the most commonly used functional MRI-based method for studying changes in cerebral perfusion. However, several studies indicated a systematic overestimation of perfusion parameters compared to other imaging modalities related to the high sensitivity of DSC-MRI for blood flow in large vessels. In this study, we therefore suggest an improved, automated, robust and efficient method allowing for generating hemodynamic parameter maps where signal influence from large vessels is minimized. Based on independent component analysis (ICA), this fully automated approach corrects DSC-MRI data without any user interaction, thus making a clinical applicability possible. The accuracy of the proposed method was tested in ten patients with cerebrovascular disease. Application of our correction algorithm resulted in a significant reduction of the effect of macro-vessel signal on hemodynamic parameters like the cerebral blood flow (CBF) and the cerebral blood volume (CBV) compared to uncorrected data. As desired, our method specifically corrected for macro-vessel artifacts in cortical grey matter tissue, leaving white matter tissue parameters largely unaffected. This may increase sensitivity and reliability of detecting perfusion abnormalities in patient groups, in particular with regard to stroke and other cerebrovascular disorders.