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.
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.
Authors: R K Gupta; R Awasthi; R K Garg; N Kumar; P K Gupta; A K Singh; P Sahoo; V K Paliwal; K N Prasad; C M Pandey; R K S Rathore Journal: AJNR Am J Neuroradiol Date: 2012-11-22 Impact factor: 3.825
Authors: Alireza Mehrtash; Sandeep N Gupta; Dattesh Shanbhag; James V Miller; Tina Kapur; Fiona M Fennessy; Ron Kikinis; Andriy Fedorov Journal: J Med Imaging (Bellingham) Date: 2016-03-01
Authors: Eleanor Evans; Stephen J Sawiak; Alexander O Ward; Guido Buonincontri; Robert C Hawkes; T Adrian Carpenter Journal: Nucl Instrum Methods Phys Res A Date: 2014-01-11 Impact factor: 1.455