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PeerJ. 2015 Apr 23;3:e909. doi: 10.7717/peerj.909. eCollection 2015.

DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis.

Author information

1
Institute of Imaging Science, Vanderbilt University , Nashville, TN , USA ; Department of Radiology and Radiological Sciences, Vanderbilt University , USA.
2
Institute of Imaging Science, Vanderbilt University , Nashville, TN , USA ; Department of Radiology and Radiological Sciences, Vanderbilt University , USA ; Department of Physics and Astronomy, Vanderbilt University , USA ; Department of Cancer Biology, Vanderbilt University , USA.
3
Institute of Imaging Science, Vanderbilt University , Nashville, TN , USA ; Department of Radiology and Radiological Sciences, Vanderbilt University , USA ; Department of Biomedical Engineering, Vanderbilt University , USA.

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(N (1.9)), 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.

KEYWORDS:

Cancer; DCE; Julia; Magnetic resonance imaging; Medical imaging; Numerical methods; Parallel computing; Quantitative imaging biomarkers; qMRI

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