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

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1 Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States
2 Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States
3 Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
4 Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States
5 Department of Cancer Biology, Vanderbilt University, Nashville, TN, United States
DOI
10.7287/peerj.preprints.670v1
Published
Accepted
Subject Areas
Radiology and Medical Imaging, Computational Science
Keywords
Magnetic resonance imaging, DCE, quantitative imaging biomarkers, qMRI, cancer, parallel computing, Julia, medical imaging, numerical methods
Copyright
© 2014 Smith et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Smith DS, Li X, Arlinghaus LR, Yankeelov TE, Welch EB. 2014. DCEMRI.jl: A fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis. PeerJ PrePrints 2:e670v1

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 10-20x 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.

Author Comment

This is a submission to PeerJ for review.