ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies

BMC Medical Imaging

Russell E. Jacobs³  Berislav V. Zlokovic¹  Axel Montagne¹  Naomi Santa-Maria³  Thomas S. C. Ng²  Samuel R. Barnes³ 

[1]Zilkha Neurogenetic Institute and Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

[2]Department of Medicine, University of California, Irvine Medical Center, Orange, CA, USA

[3]Division of Biology and Biological Engineering, California Institute of Technology, Pasadena 91125, CA, USA

关键词: Nested model;    MRI;    MATLAB;    Data-driven kinetic modelling;    Parametric mapping;    ADC;    Imaging;    DCE-MRI;   

Others  :  1220826
DOI  :  10.1186/s12880-015-0062-3

 received in 2015-01-04, accepted in 2015-05-29,  发布年份 2015

【 摘 要 】

Background

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising technique to characterize pathology and evaluate treatment response. However, analysis of DCE-MRI data is complex and benefits from concurrent analysis of multiple kinetic models and parameters. Few software tools are currently available that specifically focuses on DCE-MRI analysis with multiple kinetic models. Here, we developed ROCKETSHIP, an open-source, flexible and modular software for DCE-MRI analysis. ROCKETSHIP incorporates analyses with multiple kinetic models, including data-driven nested model analysis.

Results

ROCKETSHIP was implemented using the MATLAB programming language. Robustness of the software to provide reliable fits using multiple kinetic models is demonstrated using simulated data. Simulations also demonstrate the utility of the data-driven nested model analysis. Applicability of ROCKETSHIP for both preclinical and clinical studies is shown using DCE-MRI studies of the human brain and a murine tumor model.

Conclusion

A DCE-MRI software suite was implemented and tested using simulations. Its applicability to both preclinical and clinical datasets is shown. ROCKETSHIP was designed to be easily accessible for the beginner, but flexible enough for changes or additions to be made by the advanced user as well. The availability of a flexible analysis tool will aid future studies using DCE-MRI.

A public release of ROCKETSHIP is available at https://github.com/petmri/ROCKETSHIP webcite.

【 授权许可】

   
2015 Barnes et al.

【 图 表 】

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Figure 13.

【 参考文献 】

• [1]Parker GJ, Padhani AR. T1‐W DCE‐MRI: T1‐Weighted Dynamic Contrast‐Enhanced MRI. 2004.
• [2]Cuenod C, Fournier L, Balvay D, Guinebretiere J-M. Tumor angiogenesis: pathophysiology and implications for contrast-enhanced MRI and CT assessment. Abdom Imaging. 2006; 31(2):188-93.
• [3]Hylton N. Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker. J Clin Oncol. 2006; 24(20):3293-8.
• [4]O’Connor JP, Jackson A, Parker GJ, Jayson GC. DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents. Br J Cancer. 2007; 96(2):189-95.
• [5]Frias AE, Schabel MC, Roberts VHJ, Tudorica A, Grigsby PL, Oh KY, et al. Using dynamic contrast-enhanced MRI to quantitatively characterize maternal vascular organization in the primate placenta. Magn Reson Med. 2014;1570–1578.
• [6]Montagne A, Barnes SR, Sweeney MD, Halliday MR, Sagare AP, Zhao Z et al.. Blood-Brain Barrier Breakdown in the Aging Human Hippocampus. Neuron. 2015; 85(2):296-302.
• [7]Sourbron S, Ingrisch M, Siefert A, Reiser M, Herrmann K. Quantification of cerebral blood flow, cerebral blood volume, and blood–brain-barrier leakage with DCE-MRI. Magn Reson Med. 2009; 62(1):205-17.
• [8]Strijkers GJ, Mulder WJ, van Tilborg GA, Nicolay K. MRI Contrast Agents: Current Status and Future Perspectives. Anticancer Agents Med Chem. 2007; 7(3):291-305.
• [9]Di Giovanni P, Azlan CA, Ahearn TS, Semple SI, Gilbert FJ, Redpath TW. The accuracy of pharmacokinetic parameter measurement in DCE-MRI of the breast at 3 T. Phys Med Biol. 2010; 55(1):121.
• [10]Chassidim Y, Veksler R, Lublinsky S, Pell GS, Friedman A, Shelef I. Quantitative imaging assessment of blood-brain barrier permeability in humans. Fluids Barriers CNS. 2013; 10(1):9. BioMed Central Full Text
• [11]Chih‐Feng C, Ling‐Wei H, Chun‐Chung L, Chen‐Chang L, Hsu‐Huei W, Yuan‐Hsiung T et al.. In vivo correlation between semi‐quantitative hemodynamic parameters and Ktrans derived from DCE‐MRI of brain tumors. Int J Imag Syst Tech. 2012; 22(2):132-6.
• [12]Sourbron SP, Buckley DL. Classic models for dynamic contrast-enhanced MRI. NMR Biomed. 2013; 26(8):1004-27.
• [13]Jackson A, Li K-L, Zhu X. Semi-Quantitative Parameter Analysis of DCE-MRI Revisited: Monte-Carlo Simulation, Clinical Comparisons, and Clinical Validation of Measurement Errors in Patients with Type 2 Neurofibromatosis. PLoS One. 2014; 9(3): Article ID e90300
• [14]Koh TS, Bisdas S, Koh DM, Thng CH. Fundamentals of tracer kinetics for dynamic contrast‐enhanced MRI. J Magn Reson Imaging. 2011; 34(6):1262-76.
• [15]Parker GJM, Roberts C, Macdonald A, Buonaccorsi GA, Cheung S, Buckley DL et al.. Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med. 2006; 56(5):993-1000.
• [16]Heye T, Boll DT, Reiner CS, Bashir MR, Dale BM, Merkle EM. Impact of precontrast T10 relaxation times on dynamic contrast‐enhanced MRI pharmacokinetic parameters: T10 mapping versus a fixed T10 reference value. J Magn Reson Imaging. 2013; 39(5):1136-45.
• [17]Srikanchana R, Thomasson D, Choyke P, Dwyer A. A comparison of Pharmacokinetic Models of Dynamic Contrast Enhanced MRI. Computer-Based Medical Systems, 2004 CBMS 2004 Proceedings 17th IEEE Symposium on: 24-25 June 2004 2004. 2004.361-6.
• [18]Cramer SP, Larsson HB. Accurate determination of blood-brain barrier permeability using dynamic contrast-enhanced T1-weighted MRI: a simulation and in vivo study on healthy subjects and multiple sclerosis patients. J Cereb Blood Flow Metab. 2014; 34(10):1655-65.
• [19]Zloković BV, Lipovac MN, Begley DJ, Davson H, Rakić L. Transport of Leucine-Enkephalin Across the Blood-Brain Barrier in the Perfused Guinea Pig Brain. J Neurochem. 1987; 49(1):310-5.
• [20]Zlokovic BV, Begley DJ, Chain-Eliash DG. Blood-brain barrier permeability to leucine-enkephalin, d-Alanine2-d-leucine5-enkephalin and their N-terminal amino acid (tyrosine). Brain Res. 1985; 336(1):125-32.
• [21]Zlokovic B. Cerebrovascular Permeability to Peptides: Manipulations of Transport Systems at the Blood-Brain Barrier. Pharm Res. 1995; 12(10):1395-406.
• [22]Larsson HB, Courivaud F, Rostrup E, Hansen AE. Measurement of brain perfusion, blood volume, and blood-brain barrier permeability, using dynamic contrast-enhanced T(1)-weighted MRI at 3 tesla. Magn Reson Med. 2009; 62(5):1270-81.
• [23]Ewing JR, Bagher-Ebadian H. Model selection in measures of vascular parameters using dynamic contrast-enhanced MRI: experimental and clinical applications. NMR Biomed. 2013; 26(8):1028-41.
• [24]Ewing JR, Brown SL, Lu M, Panda S, Ding G, Knight RA et al.. Model selection in magnetic resonance imaging measurements of vascular permeability: Gadomer in a 9L model of rat cerebral tumor. J Cereb Blood Flow Metab. 2006; 26(3):310-20.
• [25]Stefanovski D, Moate PJ, Boston RC. WinSAAM: a windows-based compartmental modeling system. Metabolism. 2003; 52(9):1153-66.
• [26]Barrett PHR, Bell BM, Cobelli C, Golde H, Schumitzky A, Vicini P et al.. SAAM II: Simulation, analysis, and modeling software for tracer and pharmacokinetic studies. Metabolism. 1998; 47(4):484-92.
• [27]Heye T, Davenport MS, Horvath JJ, Feuerlein S, Breault SR, Bashir MR et al.. Reproducibility of dynamic contrast-enhanced MR imaging. Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions. Radiology. 2013; 266(3):801-11.
• [28]Smith DS, Li X, Arlinghaus LR, Yankeelov TE, Welch EB. DCEMRI.jl: A fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis. PeerJ Pre Prints. 2014; 2:e670v671.
• [29]Cetin O. An analysis tool to calculate permeability based on the Patlak method. J Med Syst. 2012; 36(3):1317-26.
• [30]Barboriak DP, MacFall JR, Padua AO, York GE, Viglianti BL, Dewhirst MW. Standardized software for calculation of Ktrans and vp from dynamic T1-weighted MR images. In: International Society for Magnetic Resonance in Medicine Workshop on MR in Drug Development: From Discovery to Clinical Therapeutic Trials: 2004; McLean, VA; 2004.
• [31]Whitcher B, Schmid VJ. Quantitative analysis of dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging for oncology in R. J Stat Softw. 2011; 44(5):1-29.
• [32]Ferl GZ. DATforDCEMRI: an R package for deconvolution analysis and visualization of DCE-MRI data. J Stat Softw. 2011; 44(3):1-18.
• [33]Ortuno JE, Ledesma-Carbayo MJ, Simoes RV, Candiota AP, Arus C, Santos A. DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data. BMC Bioinformatics. 2013; 14:316. BioMed Central Full Text
• [34]Zollner FG, Weisser G, Reich M, Kaiser S, Schoenberg SO, Sourbron SP et al.. UMMPerfusion: an open source software tool towards quantitative MRI perfusion analysis in clinical routine. J Digit Imaging. 2013; 26(2):344-52.
• [35]Balvay D, Frouin F, Calmon G, Bessoud B, Kahn E, Siauve N et al.. New criteria for assessing fit quality in dynamic contrast-enhanced T1-weighted MRI for perfusion and permeability imaging. Magn Reson Med. 2005; 54(4):868-77.
• [36]Khalifa F, Soliman A, El-Baz A, El-Ghar MA, El-Diasty T, Gimel’farb G et al.. Models and methods for analyzing DCE-MRI: A review. Med Phys. 2014; 41(12):124301.
• [37]Bagher-Ebadian H, Jain R, Nejad-Davarani SP, Mikkelsen T, Lu M, Jiang Q et al.. Model Selection for DCE-T1 Studies in Glioblastoma. Magn Reson Med. 2012; 68(1):241-51.
• [38]Huang W, Li X, Chen Y, Li X, Chang MC, Oborski MJ et al.. Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. Transl Oncol. 2014; 7(1):153-66.
• [39]Kim H, Folks KD, Guo L, Stockard CR, Fineberg NS, Grizzle WE et al.. DCE-MRI detects early vascular response in breast tumor xenografts following anti-DR5 therapy. Mol Imaging Biol. 2011; 13(1):94-103.
• [40]Yankeelov TE, DeBusk LM, Billheimer DD, Luci JJ, Lin PC, Price RR et al.. Repeatability of a reference region model for analysis of murine DCE-MRI data at 7T. J Magn Reson Imaging. 2006; 24(5):1140-7.
• [41]Loveless ME, Lawson D, Collins M, Nadella MV, Reimer C, Huszar D et al.. Comparisons of the efficacy of a Jak1/2 inhibitor (AZD1480) with a VEGF signaling inhibitor (cediranib) and sham treatments in mouse tumors using DCE-MRI, DW-MRI, and histology. Neoplasia. 2012; 14(1):54-64.
• [42]Anderson KB, Conder JA. Discussion of Multicyclic Hubbert Modeling as a Method for Forecasting Future Petroleum Production. Energy Fuels. 2011; 25(4):1578-84.
• [43]Glatting G, Kletting P, Reske SN, Hohl K, Ring C. Choosing the optimal fit function: comparison of the Akaike information criterion and the F-test. Med Phys. 2007; 34(11):4285-92.
• [44]Motulsky H, Christopoulos A. Fitting models to biological data using linear and nonlinear regression: a practical guide to curve fitting: Oxford University Press; 2004.
• [45]Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989; 45(1):255-68.
• [46]Li K-L, Wilmes LJ, Henry RG, Pallavicini MG, Park JW, Hu-Lowe DD et al.. Heterogeneity in the angiogenic response of a BT474 human breast cancer to a novel vascular endothelial growth factor-receptor tyrosine kinase inhibitor: Assessment by voxel analysis of dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2005; 22(4):511-9.
• [47]Henderson E, Rutt BK, Lee TY. Temporal sampling requirements for the tracer kinetics modeling of breast disease. Magn Reson Imaging. 1998; 16(9):1057-73.
• [48]Larsson C, Kleppesto M, Rasmussen I, Salo R, Vardal J, Brandal P et al.. Sampling requirements in DCE-MRI based analysis of high grade gliomas: simulations and clinical results. J Magn Reson Imaging. 2013; 37(4):818-29.
• [49]Ng TS, Wert D, Sohi H, Procissi D, Colcher D, Raubitschek AA et al.. Serial diffusion MRI to monitor and model treatment response of the targeted nanotherapy CRLX101. Clin Cancer Res. 2013; 19(9):2518-27.
• [50]McGrath DM, Bradley DP, Tessier JL, Lacey T, Taylor CJ, Parker GJ. Comparison of model‐based arterial input functions for dynamic contrast‐enhanced MRI in tumor bearing rats. Magn Reson Med. 2009; 61(5):1173-84.
• [51]Huang W, Li X, Morris EA, Tudorica LA, Seshan VE, Rooney WD et al.. The magnetic resonance shutter speed discriminates vascular properties of malignant and benign breast tumors in vivo. Proc Natl Acad Sci U S A. 2008; 105(46):17943-8.
• [52]Paudyal R, Poptani H, Cai K, Zhou R, Glickson JD. Impact of transvascular and cellular–interstitial water exchange on dynamic contrast-enhanced magnetic resonance imaging estimates of blood to tissue transfer constant and blood plasma volume. J Magn Reson Imaging. 2013; 37(2):435-44.
• [53]Li X, Rooney WD, Springer CS. A unified magnetic resonance imaging pharmacokinetic theory: intravascular and extracellular contrast reagents. Magn Reson Med. 2005; 54(6):1351-9.
• [54]Zhou R, Pickup S, Yankeelov TE, Springer CS, Glickson JD. Simultaneous measurement of arterial input function and tumor pharmacokinetics in mice by dynamic contrast enhanced imaging: Effects of transcytolemmal water exchange. Magn Reson Med. 2004; 52(2):248-57.