Abstract
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 10–20× 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 × 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.
Highlights
Dynamic contrast enhanced MRI Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) involves the continuous acquisition of heavily T1-weighted MR images while a paramagnetic contrast agent (CA) is injected
Validation results for dcemriS4 have not been published, so we cannot compare its accuracy to DCEMRI.jl, but in our own testing, we found that dcemriS4 required roughly 10 s on average to fit the Extended Tofts-Kety model to the tissue curves derived from the breast data set, while DCEMRI.jl required 0.9 s
We have demonstrated an open source, free, and highly portable solution to DCE-MRI analysis that achieves similar accuracy of derived parameters, eschews needless complexity, and is 10–20× faster than comparable solutions
Summary
Dynamic contrast enhanced MRI Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) involves the continuous acquisition of heavily T1-weighted MR images while a paramagnetic contrast agent (CA) is injected. The CA increases the contrast between different tissues by changing their inherent relaxation rates. DCE-MRI has successfully been applied to assess vascular characteristics in both pre-clinical (Zwick et al, 2009; Jensen et al, 2010) and clinical settings (Lockhart et al, 2010; Mannelli et al, 2010). How to cite this article Smith et al (2015), DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis.
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