Automatic Individual Arterial Input Functions Calculated From PCA Outperform Manual and Population-Averaged Approaches for the Pharmacokinetic Modeling of DCE-MR Images

[EN] Background: To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on prin- cipal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters. Methods: The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results. Results: Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61). Conclusion: The automatic PCA-based approach minimizes the variability associated to obtaining individual volume- based AIFs in DCE-MR studies of the prostate.Sanz Requena, R.; Prats-Montalbán, JM.; Marti Bonmati, L.; Alberich Bayarri, A.; García Martí, G.; Pérez, R.; Ferrer Riquelme, AJ. (2015). Automatic Individual Arterial Input Functions Calculated From PCA Outperform Manual and Population-Averaged Approaches for the Pharmacokinetic Modeling of DCE-MR Images. Journal of Magnetic Resonance Imaging. 42:477-487. doi:10.1002/jmri.24805S47748742Leach, M. O., Brindle, K. M., Evelhoch, J. L., Griffiths, J. R., Horsman, M. R., Jackson, A., … Workman, P. (2005). The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. British Journal of Cancer, 92(9), 1599-1610. doi:10.1038/sj.bjc.6602550Tofts, P. S., & Kermode, A. G. (1991). Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magnetic Resonance in Medicine, 17(2), 357-367. doi:10.1002/mrm.1910170208Parker, G. J. M., Roberts, C., Macdonald, A., Buonaccorsi, G. A., Cheung, S., Buckley, D. L., … Jayson, G. C. (2006). Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 56(5), 993-1000. doi:10.1002/mrm.21066Meng, R., Chang, S. D., Jones, E. C., Goldenberg, S. L., & Kozlowski, P. (2010). Comparison between Population Average and Experimentally Measured Arterial Input Function in Predicting Biopsy Results in Prostate Cancer. Academic Radiology, 17(4), 520-525. doi:10.1016/j.acra.2009.11.006Loveless, M. E., Halliday, J., Liess, C., Xu, L., Dortch, R. D., Whisenant, J., … Yankeelov, T. E. (2011). A quantitative comparison of the influence of individual versus population-derived vascular input functions on dynamic contrast enhanced-MRI in small animals. Magnetic Resonance in Medicine, 67(1), 226-236. doi:10.1002/mrm.22988Shukla-Dave, A., Lee, N., Stambuk, H., Wang, Y., Huang, W., Thaler, H. T., … Koutcher, J. A. (2009). Average arterial input function for quantitative dynamic contrast enhanced magnetic resonance imaging of neck nodal metastases. BMC Medical Physics, 9(1). doi:10.1186/1756-6649-9-4Wang, Y., Huang, W., Panicek, D. M., Schwartz, L. H., & Koutcher, J. A. (2008). Feasibility of using limited-population-based arterial input function for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced MRI data. Magnetic Resonance in Medicine, 59(5), 1183-1189. doi:10.1002/mrm.21432Rijpkema, M., Kaanders, J. H. A. M., Joosten, F. B. M., van der Kogel, A. J., & Heerschap, A. (2001). Method for quantitative mapping of dynamic MRI contrast agent uptake in human tumors. Journal of Magnetic Resonance Imaging, 14(4), 457-463. doi:10.1002/jmri.1207Singh, A., Rathore, R. K. S., Haris, M., Verma, S. K., Husain, N., & Gupta, R. K. (2009). Improved bolus arrival time and arterial input function estimation for tracer kinetic analysis in DCE-MRI. Journal of Magnetic Resonance Imaging, 29(1), 166-176. doi:10.1002/jmri.21624Shi, L., Wang, D., Liu, W., Fang, K., Wang, Y.-X. J., Huang, W., … Ahuja, A. T. (2013). Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering. Journal of Magnetic Resonance Imaging, 39(5), 1327-1337. doi:10.1002/jmri.24259Kim, J.-H., Im, G. H., Yang, J., Choi, D., Lee, W. J., & Lee, J. H. (2011). Quantitative dynamic contrast-enhanced MRI for mouse models using automatic detection of the arterial input function. NMR in Biomedicine, 25(4), 674-684. doi:10.1002/nbm.1784Li, X., Welch, E. B., Arlinghaus, L. R., Chakravarthy, A. B., Xu, L., Farley, J., … Yankeelov, T. E. (2011). A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer. Physics in Medicine and Biology, 56(17), 5753-5769. doi:10.1088/0031-9155/56/17/018Fedorov, A., Fluckiger, J., Ayers, G. D., Li, X., Gupta, S. N., Tempany, C., … Fennessy, F. M. (2014). A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation. Magnetic Resonance Imaging, 32(4), 321-329. doi:10.1016/j.mri.2014.01.004Lin, Y.-C., Chan, T.-H., Chi, C.-Y., Ng, S.-H., Liu, H.-L., Wei, K.-C., … Wang, J.-J. (2012). Blind estimation of the arterial input function in dynamic contrast-enhanced MRI using purity maximization. Magnetic Resonance in Medicine, 68(5), 1439-1449. doi:10.1002/mrm.24144Roberts, C., Little, R., Watson, Y., Zhao, S., Buckley, D. L., & Parker, G. J. M. (2010). The effect of blood inflow andB1-field inhomogeneity on measurement of the arterial input function in axial 3D spoiled gradient echo dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 65(1), 108-119. doi:10.1002/mrm.22593Jackson, J. E. (1991). A Use’s Guide to Principal Components. Wiley Series in Probability and Statistics. doi:10.1002/0471725331Prats-Montalbán, J. M., Sanz-Requena, R., Martí-Bonmatí, L., & Ferrer, A. (2013). Prostate functional magnetic resonance image analysis using multivariate curve resolution methods. Journal of Chemometrics, 28(8), 672-680. doi:10.1002/cem.2585Eyal, E., Bloch, B. N., Rofsky, N. M., Furman-Haran, E., Genega, E. M., Lenkinski, R. E., & Degani, H. (2010). Principal Component Analysis of Dynamic Contrast Enhanced MRI in Human Prostate Cancer. Investigative Radiology, 45(4), 174-181. doi:10.1097/rli.0b013e3181d0a02fTofts, P. S. (1997). Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. Journal of Magnetic Resonance Imaging, 7(1), 91-101. doi:10.1002/jmri.1880070113Donahue, K. M., Burstein, D., Manning, W. J., & Gray, M. L. (1994). Studies of Gd-DTPA relaxivity and proton exchange rates in tissue. Magnetic Resonance in Medicine, 32(1), 66-76. doi:10.1002/mrm.1910320110Taylor, J. S., & Reddick, W. E. (2000). Evolution from empirical dynamic contrast-enhanced magnetic resonance imaging to pharmacokinetic MRI. Advanced Drug Delivery Reviews, 41(1), 91-110. doi:10.1016/s0169-409x(99)00058-7Port, R. E., Knopp, M. V., & Brix, G. (2001). Dynamic contrast-enhanced MRI using Gd-DTPA: Interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumors. Magnetic Resonance in Medicine, 45(6), 1030-1038. doi:10.1002/mrm.1137Dale, B. M., Jesberger, J. A., Lewin, J. S., Hillenbrand, C. M., & Duerk, J. L. (2003). Determining and optimizing the precision of quantitative measurements of perfusion from dynamic contrast enhanced MRI. Journal of Magnetic Resonance Imaging, 18(5), 575-584. doi:10.1002/jmri.10399Garpebring, A., Brynolfsson, P., Yu, J., Wirestam, R., Johansson, A., Asklund, T., & Karlsson, M. (2012). Uncertainty estimation in dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 69(4), 992-1002. doi:10.1002/mrm.24328Onxley, J. D., Yoo, D. S., Muradyan, N., MacFall, J. R., Brizel, D. M., & Craciunescu, O. I. (2014). Comprehensive Population-Averaged Arterial Input Function for Dynamic Contrast–Enhanced vMagnetic Resonance Imaging of Head and Neck Cancer. International Journal of Radiation Oncology*Biology*Physics, 89(3), 658-665. doi:10.1016/j.ijrobp.2014.03.006Chen, Y.-J., Chu, W.-C., Pu, Y.-S., Chueh, S.-C., Shun, C.-T., & Tseng, W.-Y. I. (2012). Washout gradient in dynamic contrast-enhanced MRI is associated with tumor aggressiveness of prostate cancer. Journal of Magnetic Resonance Imaging, 36(4), 912-919. doi:10.1002/jmri.23723Vos, E. K., Litjens, G. J. S., Kobus, T., Hambrock, T., Kaa, C. A. H. de, Barentsz, J. O., … Scheenen, T. W. J. (2013). Assessment of Prostate Cancer Aggressiveness Using Dynamic Contrast-enhanced Magnetic Resonance Imaging at 3 T. European Urology, 64(3), 448-455. doi:10.1016/j.eururo.2013.05.045Yang, C., Karczmar, G. S., Medved, M., Oto, A., Zamora, M., & Stadler, W. M. (2009). Reproducibility assessment of a multiple reference tissue method for quantitative dynamic contrast enhanced-MRI analysis. Magnetic Resonance in Medicine, 61(4), 851-859. doi:10.1002/mrm.21912McGrath, D. M., Bradley, D. P., Tessier, J. L., Lacey, T., Taylor, C. J., & Parker, G. J. M. (2009). Comparison of model-based arterial input functions for dynamic contrast-enhanced MRI in tumor bearing rats. Magnetic Resonance in Medicine, 61(5), 1173-1184. doi:10.1002/mrm.21959Orton, M. R., d’ Arcy, J. A., Walker-Samuel, S., Hawkes, D. J., Atkinson, D., Collins, D. J., & Leach, M. O. (2008). Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI. Physics in Medicine and Biology, 53(5), 1225-1239. doi:10.1088/0031-9155/53/5/005Heisen, M., Fan, X., Buurman, J., van Riel, N. A. W., Karczmar, G. S., & ter Haar Romeny, B. M. (2010). The use of a reference tissue arterial input function with low-temporal-resolution DCE-MRI data. Physics in Medicine and Biology, 55(16), 4871-4883. doi:10.1088/0031-9155/55/16/01

Toward Developing Pharmacokinetic Response Criteria to Chemoradiation in Head and Neck Cancer Patients Using Dynamic Contrast-Enhanced MRI

<p><bold>Purpose:</bold> The purpose of this study was to assess the feasibility of using dynamic contrast-enhanced MRI to monitor early treatment-induced changes in pharmacokinetic (PK) parameters in head and neck cancer patients. The intrinsic variability of three parameters (K<super>trans</super>, v_e, and iAUC60) without treatment intervention was measured and compared to the treatment-induced variability.</p><p><bold>Materials and Methods:</bold> Twenty patients were imaged while undergoing chemoradiation therapy (CRT) for head and neck malignancies. The imaging protocol included two baseline scans one week apart (B1, B2), and a third scan after 1-2 weeks of chemoradiation (ETX - early treatment). The images were acquired on a 1.5T scanner in the coronal plane with a temporal resolution of 10 sec. A population-averaged arterial input function was calculated from plasma concentration curves in both the left and the right carotids of each patient at each time point (B1, B2, ETX). The statistical significance of using a left/right AIF or a time-point-specific AIF was evaluated using Bland-Altman plots. To further ensure the correct calculation of PK parameters, the accuracy of the flip angles produced by the MR scanner was measured in phantoms and a volunteer. PK analysis was performed in iCAD (Nashua, NH) based on the modified Tofts model. This study focuses on two PK parameters used in the Tofts model (K<super>trans</super>, v_e), and one semi-quantitative parameter that was also calculated in iCAD using an uptake integral approach (iAUC60). K<super>trans</super>, v_e, and iAUC60 were averaged over regions of interest (ROIs), some of which covered primary tumors, and others of which covered known nodal metastases. Bland-Altman plots were used to describe the intrinsic variability in each parameter between the two baseline scans. The coefficient of repeatability (CR) between the baseline values was determined from the Bland-Altman plots and compared to the magnitude of the observed treatment-induced changes.</p><p><bold>Results:</bold> The plasma parameters for the population-averaged AIF were a₁ = 27.1135 kg/liter, a₂ = 17.6486 kg/liter, m₁ = 11.7525 min<super>-1</super>, and m₂ = 0.2054 min<super>-1</super>. The use of a left- or right-sided AIF was determined to be unnecessary, as it did not give statistically different PK parameters than the population-averaged AIF. The use of a time-point-specific AIF was not necessary in most cases, though it may give more accurate results when K<super>trans</super> values are > 1 min<super>-1</super>. The flip angle tests revealed high inaccuracies at a flip angle of 5¡ã, so flip angles ¡Ü 5¡ã were not used in PK analysis. The intrinsic variability of K<super>trans</super>, v_e, and iAUC60 was very high. For nodes, the CRs from the B1-B2 Bland-Altman plots were 0.725 min<super>-1</super> for K<super>trans</super>, 0.315 for v_e, and 18.15 mM-sec for iAUC60. The fractions of node ROIs which showed treatment-induced changes greater than the CR were 3 out of 14 for K<super>trans</super>, 3 out of 17 for v_e, and 7 out of 17 for iAUC60. For primaries, the CRs were 1.385 min<super>-1</super> for K<super>trans</super>, 0.305 for v_e, and 62.85 mM-sec for iAUC60. The fractions of primary ROIs which showed treatment-induced changes greater than the CR were 0 out of 9 for K<super>trans</super>, 1 out of 11 for v_e, and 2 out of 12 for iAUC60.</p><p><bold>Conclusions:</bold> A population-averaged AIF for head and neck was generated that accounts for differences in right vs. left carotids, day-to-day AIF fluctuations, and treatment-induced AIF changes. It is not necessary to use a side-specific or a time-point-specific AIF. When K<super>trans</super> is greater than 1 min<super>-1</super>, PK parameter accuracy may be improved with the use of a time-point-specific AIF. Using the average AIF, large intrinsic fluctuations were observed in ROI-averaged values of K<super>trans</super>, v_e, and iAUC60, making these parameters poor evaluators of early treatment response in head and neck cancer. Nodes were slightly more likely than primaries to show significant treatment-induced changes. Overall, the use of averaged MR-based PK parameters to assess early treatment response is limited and challenging. An analysis of voxel-based variability might be better suited to this task.</p>Thesi