Improved bolus arrival time and arterial input function estimation for tracer kinetic analysis in DCE‐MRI

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. DYNAMIC CONTRAST-ENHANCED (DCE) magnetic resonance imaging (MRI) provides noninvasive methods for studying the vasculature of different tissues/lesions based on their response to the passage of intravenously injected contrast agent. The study of tissue vasculature is essential for understanding a wide range of disease processes. DCE-MRI data results in a signal intensity time curve, S(t), at individual voxels that can be converted into a concentration time curve, C(t) (1-3). The shape of C(t) is an important criterion for differentiation/characterization of different tissues. In the case of an intact blood brain barrier (BBB) the contrast remains intravascular. BBB breakdown results in leakage of contrast into the extracellular extravascular space, which leads to the enhancement of contrast (1). The shape of the curve in these tissues is different from that in normal tissues. A set of well-recognized mathematical models is available for T 1 -weighted (W) DCE-MRI data that can provide useful information on tissue vasculature (1-4). Accurate and precise estimation of the indices of these models is essential for understanding tissue vasculature that may help in the diagnosis of different pathologies. With the presently available resolution and the number of feasible scans the understanding and the accuracy of the resulting C(t) is still not fully resolved, as issues like compartmentalization of a voxel, effect of water exchange between different compartments of the same voxel, partial volume effect (PVE), inflow of fresh spins (time of flight), etc, continue to be gray areas. Continuous efforts are being made to achieve accurate and precise quantitation of ...

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Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging

“…Several approaches for automatic estimation of BAT have been proposed. [25][26][27][28][29] Huisman et al 25 developed a three-component model for prostate with BAT as a parameter estimated by fitting a curve that focuses on the most probable slope. In Ref.…”

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