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Abstract #5061

To evaluate the effect of different initial guess selection approaches on quantitative analysis of DCE-MRI data of brain tumor patients

Dinil Sasi1, Sameer Manickam1,2, Rakshit Dadarwal1, Ayan Debnath1,3, Snekha Thakran1, Rakesh K Gupta4, and Anup Singh1,5

1Indian Institute of Technology Delhi, New Delhi, India, 2KTH Royal Institute of Technology, Stockholm, Sweden, 3University of Pennsylvania, Philadelphia, PA, United States, 4Fortis memorial research institute, Gurugram, India, 5AIIMS, New Delhi, India

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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