A Fully Automated Method for Quantitative Cerebral Hemodynamic Analysis Using DSC–MRI

Abstract

Dynamic susceptibility contrast (DSC)-based perfusion analysis from MR images has become an established method for analysis of cerebral blood volume (CBV) in glioma patients. To date, little emphasis has, however, been placed on quantitative perfusion analysis of these patients, mainly due to the associated increased technical complexity and lack of sufficient stability in a clinical setting. The aim of our study was to develop a fully automated analysis framework for quantitative DSC-based perfusion analysis. The method presented here generates quantitative hemodynamic maps without user interaction, combined with automatic segmentation of normal-appearing cerebral tissue. Validation of 101 patients with confirmed glioma after surgery gave mean values for CBF, CBV, and MTT, extracted automatically from normal-appearing whole-brain white and gray matter, in good agreement with literature values. The measured age-and gender-related variations in the same parameters were also in agreement with those in the literature. Several established analysis methods were compared and the resulting perfusion metrics depended significantly on method and parameter choice. In conclusion, we present an accurate, fast, and automatic quantitative perfusion analysis method where all analysis steps are based on raw DSC data only.

A control point interpolation method for the non-parametric quantification of cerebral haemodynamics from dynamic susceptibility contrast MRI

“…The AIF was defined in the vessel subclass with highest Pmax and lowest FM and a venous output function (VOF) in the same class but with highest Pmax and greatest FM. The AIF was then scaled such that the mean value after the first passage of bolus (the tail of AIF) was equal to the tail of the VOF using the tail scaling technique (Bjørnerud and Emblem, 2010).…”

T1- and T2*-Dominant Extravasation Correction in DSC-MRI: Part II—Predicting Patient Outcome after a Single Dose of Cediranib in Recurrent Glioblastoma Patients

“…Arterial input functions were determined in each DSC image slice using a previously published automatic method based on K-means cluster analysis of the DSC concentration curves (Bjornerud and Emblem, 2010). Here, an iterative Tikhonov regularization-based SVD method was used to minimize oscillations in the 'tail' of the residue function used to estimate the rate constant K a .…”

Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme

“…Brain pixels were isolated from the non-brain pixels in T2-w, DWI and PWI images by generating a brain mask with a skull-stripping algorithm in Statistical Parametric Mapping (SPM8) software (http://www.fil.ion.ucl.ac.uk/spm/) [26]. Quantitative analysis of tissue perfusion was performed employing established tracer kinetic models [27,28].…”