Time-Efficient Perfusion Imaging Using DCE- and DSC-MRI


Dynamic contrast enhanced MRI (DCE-MRI) and dynamic susceptibility contrast MRI (DSC-MRI) are perfusion imaging techniques used mainly for clinical and preclinical measurement of vessel permeability and capillary blood flow, respectively. It is advantageous to apply both methods to exploit their complementary information about the perfusion status of the tissue. We propose a novel acquisition method that combines advantages of the current simultaneous and sequential acquisition. The proposed method consists of a DCE-MRI acquisition interrupted by DSC-MRI acquisition. A new method for processing of the DCE-MRI data is proposed which takes the interleaved acquisition into account. Analysis of both the DCE- and DSC-MRI data is reformulated so that they are approximated by the same pharmacokinetic model (constrained distributed capillary adiabatic tissue homogeneity model). This provides a straightforward evaluation of the methodology as some of the estimated DCE- and DSC-MRI perfusion parameters should be identical. Evaluation on synthetic data showed an acceptable precision and no apparent bias introduced by the interleaved character of the DCE-MRI acquisition. Intravascular perfusion parameters obtained from clinical glioma data showed a fairly high correlation of blood flow estimates from DCE- and DSC-MRI, however, an unknown scaling factor was still present mainly because of the tissue-specific r2* relaxivity. The results show validity of the proposed acquisition method. They also indicate that simultaneous processing of both DCE- and DSC-MRI data with joint estimation of some perfusion parameters (included in both DCE- and DSC-MRI) might be possible to increase the reliability of the DCE- and DSC-MRI methods alone.

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  • [1] Jerosch-Herold, M. (2010). Quantification of myocardial perfusion by cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance, 12(1), 57.

  • [2] Jackson, A., Buckley, D.L., Parker, G.J.M. (eds.) (2004). Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Oncology (Medical Radiology). Springer.

  • [3] Tofts, P. (ed.) (2004). Quantitative MRI of the Brain: Measuring Changes Caused by Disease. Wiley.

  • [4] Willats, L., Calamante, F. (2013). The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast MRI. NMR in Biomedicine, 26(8), 913–931.

  • [5] Calamante, F., Gadian, D., Connelly, A. (2002). Quantification of perfusion using bolus tracking magnetic resonance imaging in stroke: Assumptions, limitations, and potential implications for clinical use. Stroke, 33(4), 1146–1151.

  • [6] Koh, T.S., Bisdas, S., Koh, D.M., Thng, C.H. (2011). Fundamentals of tracer kinetics for dynamic contrastenhanced MRI. Journal of Magnetic Resonance Imaging, 34(6), 1262–1276.

  • [7] Sourbron, S.P., Buckley, D.L. (2013). Classic models for dynamic contrast-enhanced MRI. NMR in Biomedicine, 26(8), 1004–1027.

  • [8] Tofts, P., Brix, G., Buckley, D., et al. (1999). Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: Standardized quantities and symbols. Journal of Magnetic Resonance Imaging, 10(3), 223–232.

  • [9] Tofts, P.S. (1997). Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. Journal of Magnetic Resonance Imaging, 7(1), 91–101.

  • [10] Brix, G., Kiessling, F., Lucht, R., Darai, S., Wasser, K., Delorme, S., Griebel, J. (2004). Microcirculation and microvasculature in breast tumors: Pharmacokinetic analysis of dynamic MR image series. Magnetic Resonance in Medicine, 52, 420–429.

  • [11] St Lawrence, K.S., Lee, T.Y. (1998). An adiabatic approximation to the tissue homogeneity model for water exchange in the brain: I. Theoretical derivation. Journal of Cerebral Blood Flow and Metabolism, 18(12), 1365–1377.

  • [12] Koh, T.S., Zeman, V., Darko, J., et al. (2001). The inclusion of capillary distribution in the adiabatic tissue homogeneity model of blood flow. Physics in Medicine and Biology, 46(5), 1519–1538.

  • [13] Bartoš, M., Jiřík, R., Kratochvíla, J., Standara, M., Starčuk Jr., Z., Taxt, T. (2014). The precision of DCEMRI using the tissue homogeneity model with continuous formulation of the perfusion parameters. Magnetic Resonance Imaging, 32(5), 505–513.

  • [14] Østergaard, L. (2005). Principles of cerebral perfusion imaging by bolus tracking. Journal of Magnetic Resonance Imaging, 22(6), 710–717.

  • [15] Essig, M., Shiroishi, M.S., Nguyen, T.B., et al. (2013). Perfusion MRI: the five most frequently asked technical questions. American Journal of Roentgenology, 200(1), 24–34.

  • [16] Donahue, K.M., Krouwer, H.G., Rand, S.D., Pathak, A.P., Marszalkowski, C.S., Censky, S.C., Prost, R.W. (2000). Utility of simultaneously acquired gradientecho and spin-echo cerebral blood volume and morphology maps in brain tumor patients. Magnetic Resonance in Medicine, 43(6), 845–853.

  • [17] Pike, M.M., Stoops, C.N., Langford, C.P., Akella, N.S., Nabors, L.B., Gillespie, G.Y. (2009). High-resolution longitudinal assessment of flow and permeability in mouse glioma vasculature: Sequential small molecule and SPIO dynamic contrast agent MRI. Magnetic Resonance in Medicine, 61(3), 615–625.

  • [18] Sourbron, S., Heilmann, M., Biffar, A., Walczak, C., Vautier, J., Volk, A., Peller, M. (2009). Bolus-tracking MRI with a simultaneous T1- and T2*-measurement. Magnetic Resonance in Medicine, 62(3), 672–681.

  • [19] Schmiedeskamp, H., Andre, J.B., Straka, M., et al. (2013). Simultaneous perfusion and permeability measurements using combined spin- and gradient-echo MRI. Journal of Cerebral Blood Flow and Metabolism, 33(5), 732–743.

  • [20] Heilmann, M.,Walczak, C., Vautier, J., et al. (2007). Simultaneous dynamic T1 and T2* measurement for AIF assessment combined with DCE MRI in a mouse tumor model. Magnetic Resonance Materials in Physics, Biology and Medicine, 20(4), 193–203.

  • [21] Sourbron, S., Heilmann, M., Walczak, C., Vautier, J., Schad, L.R., Volk, A. (2013). T2*-relaxivity contrast imaging: First results. Magnetic Resonance in Medicine, 69(5), 1430–1437.

  • [22] Schmiedeskamp, H., Straka, M., Newbould, R.D., et al. (2012). Combined spin- and gradient-echo perfusionweighted imaging. Magnetic Resonance in Medicine, 68, 30–40.

  • [23] Lüdemann, L., Warmuth, C., Plotkin, M., Förschler, A., Gutberlet, M., Wust, P., Amthauer, H. (2009). Brain tumor perfusion: Comparison of dynamic contrast enhanced magnetic resonance imaging using T1, T2, and T 2 * ${\rm{T}}_2^*$ contrast, pulsed arterial spin labeling, and H2(15)O positron emission tomography. European Journal of Radiology, 70(3), 465–474.

  • [24] Macíček, O., Jiřík, R., Bartoš, M., et al. (2013). Interleaved DCE and DSC-MRI. In Trends in Biomedical Engineering, Košice, Slovak Republic: Technical University, 177–180.

  • [25] Macíček, O., Jiřík, R., Bartoš, M., et al. (2013). Interleaved Time-Effective DCE-MRI. Magnetic Resonance Materials in Physics, Biology and Medicine, 26 (Suppl. 1), 204–205.

  • [26] Kratochvíla, J., Jiřík, R., Bartoš, M., Standara, M., Starčuk Jr., Z., Taxt, T. (2016). Distributed capillary adiabatic tissue homogeneity model in parametric multichannel blind AIF estimation using DCE-MRI. Magnetic Resonance in Medicine, 75(3), 1355–1365.

  • [27] Li, K.-L., Zhu, X. P., Waterton, J., Jackson, A. (2000). Improved 3D quantitative mapping of blood volume and endothelial permeability in brain tumors. Journal of Magnetic Resonance Imaging, 12(2), 347–357.

  • [28] Parker, G.J.M., Roberts, C., Macdonald, A., et al. (2006). -derived functional form for a populationaveraged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 56(5), 993–1000.

  • [29] Taxt, T., Jiřík, R., Rygh, C.B., et al. (2012). Singlechannel blind estimation of arterial input function and tissue impulse response in DCE-MRI. IEEE Transactions on Biomedical Engineering, 59(4), 1012–1021.

  • [30] Mouridsen, K., Christensen, S., Gyldensted, L., Østergaard, L. (2006). Automatic selection of arterial input function using cluster analysis. Magnetic Resonance in Medicine, 55(3), 524–531.

  • [31] Madsen, M.T. (1992). A simplified formulation of the gamma variate function. Physics in Medicine and Biology, 37(7), 1597–1600.

  • [32] Grüner, R., Bjørnarå, B.T., Moen, G., Taxt, T. (2006). Magnetic resonance brain perfusion imaging with voxel-specific arterial input functions. Journal of Magnetic Resonance Imaging, 23(3), 273–284.

  • [33] Kershaw, L. E., Buckley, D. L. (2006). Precision in measurements of perfusion and microvascular permeability with T1-weighted dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 56(5), 986–992.

  • [34] Schabel, M. C. (2012). A unified impulse response model for DCE-MRI. Magnetic Resonance in Medicine, 68(5), 1632–1646.

  • [35] Liu, G., Sobering, G., Duyn, J., Moonen, C.T.W. (1993). A functional MRI technique combining principles of echo-shifting with a train of observations (PRESTO). Magnetic Resonance in Medicine, 30(6), 764–768.

  • [36] Pedersen, M., Klarhöfer, M., Christensen, S. r., Ouallet, J.-C., Østergaard, L., Dousset, V., Moonen, C. (2004). Quantitative cerebral perfusion using the PRESTO acquisition scheme. Journal of Magnetic Resonance Imaging, 20(6), 930–940.

  • [37] Boxerman, J.L., Hamberg, L.M., Rosen, B.R., Weisskoff, R.M. (1995). MR contrast due to intravascular magnetic susceptibility perturbations. Magnetic Resonance in Medicine, 34(4), 555–566.

  • [38] Kjølby, B.F., Østergaard, L., Kiselev, V.G. (2006). Theoretical model of intravascular paramagnetic tracers effect on tissue relaxation. Magnetic Resonance in Medicine, 56(1), 187–197.

  • [39] Lammertsma, A., Brooks, D.J., Beaney, R.P., et al. (1984). In vivo measurement of regional cerebral haematocrit using positron emission tomography. Journal of Cerebral Blood Flow and Metabolism, 4, 317–322.

  • [40] Šonka, M., Hlaváč, V., Boyle, R. (2008). Image Processing, Analysis, and Machine Vision. Thomson Learning.

  • [41] Bagher-Ebadian, H., Jain, R., Nejad-Davarani, S.P., et al. (2012). Model selection for DCE-T1 studies in glioblastoma. Magnetic Resonance in Medicine, 68(1), 241–251.

  • [42] Harrer, J.U., Parker, G.J.M., Haroon, H., et al. (2004). Comparative study of methods for determining vascular permeability and blood volume in human gliomas. Journal of Magnetic Resonance Imaging, 20, 748–757.

  • [43] Kratochvíla, J., Jiřík, R., Starčuk Jr., Z., Bartoš, M., Standara, M., Taxt, T. (2016). Using multichannel blind AIF estimation with the DCATH model to allow increased temporal sampling interval in DCE-MRI. Magnetic Resonance Materials in Physics, Biology and Medicine, 29 (Suppl. 1), 288–289.

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