Linearization improves the repeatability of quantitative Dynamic Contrast-Enhanced MRI
Author and article information
Abstract
We studied the effect of linearization on the repeatability of the Tofts and reference region models (RRM) for Dynamic Contrast-Enhanced MRI (DCE MRI). We compared the repeatabilities of these two linearized models, the standard non-linear version, and semi-quantitative methods of analysis. Simulated and experimental DCE MRI data from 12 rats with a flank tumor of C6 glioma acquired over three consecutive days were analyzed using four quantitative and semi-quantitative DCE MRI metrics. The quantitative methods used were: 1) Linear Tofts model (LTM), 2) Non-linear Tofts model (NTM), 3) Linear RRM (LRRM), and 4) Non-linear RRM (NRRM). The following semi-quantitative metrics were used: 1) Maximum enhancement ratio (MER), 2) time to peak (TTP), 3) initial area under the curve (iauc64), and 4) slope. LTM and NTM were used to estimate Ktrans, while LRRM and NRRM were used to estimate Ktrans relative to muscle (RKtrans). Repeatability was assessed by calculating the within-subject coefficient of variation (wSCV) and the percent intra-subject variation (iSV) determined with the Gage repeatability and reproducibility (R&R) analysis. The iSV for RKtrans using LRRM was two-fold lower compared to NRRM at all simulated and experimental conditions. A similar trend was observed for the Tofts model, where LTM was at least 50% more repeatable than the NTM under all experimental and simulated conditions. The semi-quantitative metrics iauc64 and MER were as equally reproducible as Ktrans and RKtrans estimated by LTM and LRRM respectively. The iSV for iauc64 and MER were significantly lower than the iSV for slope and TTP. In simulations and experimental results, linearization improves the repeatability of quantitative DCE MRI by at least 30%, making it as repeatable as semi-quantitative metrics.
Cite this as
2017. Linearization improves the repeatability of quantitative Dynamic Contrast-Enhanced MRI. PeerJ Preprints 5:e2824v1 https://doi.org/10.7287/peerj.preprints.2824v1Author comment
Initial version of our manuscript submitted for peer review to NMR in Biomedicine.
Sections
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Kyle M. Jones conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Marisa H. Borders contributed reagents/materials/analysis tools, reviewed drafts of the paper, guidance on clinical utility of new methods.
Kimberly A. Fitzpatrick contributed reagents/materials/analysis tools, reviewed drafts of the paper, guidance on clinical utility of new methods.
Mark D. Pagel contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper.
Julio Cárdenas-Rodríguez conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Animal Ethics
The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):
The Institutional Animal Care and Use Committee of the University of Texas MD Anderson Cancer Center approved the studies.
Data Deposition
The following information was supplied regarding data availability:
1) Github
2) Gage-repeatability-DCE-MRI
3) https://jcardenasrdz.github.io/Gage-repeatability-DCE-MRI/
Funding
This study was supported by the National Cancer Institute of the National Institutes of Health under award numbers R01CA167183, R01CA197029, and P30CA023074. K.M.J. is supported by a fellowship from NIH grants T32HL007955 and T32HL066988. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
