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We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10-20x speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 x 192 breast image, we achieved run times of < 1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N1.9), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.
I ran into this while looking at DCE@urLAB. Very nice work!
One comment is that your list of publicly available packages for DCE MRI analysis does not include PK modeling tool distributed as an extension of 3D Slicer. It is free, open source, cross-platform, and supports batch mode usage. It does not implement all the models that you have, and it does not have a comprehensive evaluation done. See details here.