Quantitative analysis of dynamic-contrast-enhanced(DCE)-MRI data using various tracer kinetic models is widely used in cancer diagnosis and follow-up. In general, voxelwise model fitting using nonlinear-least-square method requires a long processing time depending upon image-resolution, data noise, choice of initial guess, model type and computer-platform. In this study, we proposed a tissue specific initial guess selection approach, for the voxel wise fitting using nonlinear–least-square method, which substantially reduced computation-time without compromising accuracy of parameters compared to regular global initial guess approach. It also performed better than recently proposed Image-Downsampling-Expedited-Adaptive-Least-squares fitting approach. Parallel-processing was also implemented to further reduce the time
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