ABSTRACT
PURPOSE: To develop a methodology for improved estimation of bolus arrival time (BAT) and arterial input function (AIF) which are prerequisites for tracer kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and to verify the applicability of the same in the case of intracranial lesions (brain tumor and tuberculoma). MATERIALS AND METHODS: A continuous piecewise linear (PL) model (with BAT as one of the free parameters) is proposed for concentration time curve C(t) in T(1)-weighted DCE-MRI. The resulting improved procedure suggested for automatic extraction of AIF is compared with earlier methods. The accuracy of BAT and other estimated parameters is tested over simulated as well as experimental data. RESULTS: The proposed PL model provides a good approximation of C(t) trends of interest and fit parameters show their significance in a better understanding and classification of different tissues. BAT was correctly estimated. The automatic and robust estimation of AIF obtained using the proposed methodology also corrects for partial volume effects. The accuracy of tracer kinetic analysis is improved and the proposed methodology also reduces the time complexity of the computations. CONCLUSION: The PL model parameters along with AIF measured by the proposed procedure can be used for an improved tracer kinetic analysis of DCE-MRI data.
Subject(s)
Algorithms , Brain Diseases/diagnosis , Brain Diseases/physiopathology , Cerebral Arteries/metabolism , Contrast Media/pharmacokinetics , Image Interpretation, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Brain , Computer Simulation , Female , Glioma/blood supply , Glioma/diagnosis , Glioma/physiopathology , Humans , Kinetics , Male , Metabolic Clearance Rate , Middle Aged , Models, Neurological , Reproducibility of Results , Sensitivity and Specificity , Tuberculoma/diagnosis , Tuberculoma/physiopathology , Young AdultABSTRACT
PURPOSE: To estimate precontrast tissue parameter (T(10)) using fast spin echo (FSE) and to quantify physiological and hemodynamic parameters with leakage correction using T(1)-weighted dynamic contrast-enhanced (DCE) perfusion imaging. MATERIALS AND METHODS: Voxel-wise T(10) computation was performed followed by the analysis of T(1)-weighted DCE perfusion data for the conversion of signal intensity time curve to concentration time curve, estimation of hemodynamic and physiological perfusion indices, and a method for leakage correction. Validations of accuracy of the computations have also been carried out. RESULTS: The computed T(10) and hemodynamic perfusion indices in normal white and gray matter were in good agreement with the literature values. Physiological perfusion indices in these regions were found negligible, validating computations. Cerebral blood volume (CBV) values change negligibly over the length of concentration time curve in white matter, gray matter, and lesion (CBV(corrected)), while CBV(uncorrected) (lesion) shows linear increase over time. CONCLUSION: T(1)-weighted DCE perfusion data along with FSE-based T(1) estimation can be used for an accurate estimation of hemodynamic and physiological perfusion indices.
