Cite

[1] Jerosch-Herold, M. (2010). Quantification of myocardial perfusion by cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance, 12(1), 57.10.1186/1532-429X-12-57Search in Google Scholar

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

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

[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), 913–931.10.1002/nbm.2833Search in Google Scholar

[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), 1146–1151.10.1161/01.STR.0000014208.05597.33Search in Google Scholar

[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), 1262–1276.10.1002/jmri.22795Search in Google Scholar

[7] Sourbron, S.P., Buckley, D.L. (2013). Classic models for dynamic contrast-enhanced MRI. NMR in Biomedicine, 26(8), 1004–1027.10.1002/nbm.2940Search in Google Scholar

[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), 223–232.10.1002/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-SSearch in Google Scholar

[9] Tofts, P.S. (1997). Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. Journal of Magnetic Resonance Imaging, 7(1), 91–101.10.1002/jmri.1880070113Search in Google Scholar

[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, 420–429.10.1002/mrm.20161Search in Google Scholar

[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), 1365–1377.10.1097/00004647-199812000-00011Search in Google Scholar

[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), 1519–1538.10.1088/0031-9155/46/5/313Search in Google Scholar

[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), 505–513.10.1016/j.mri.2014.02.003Search in Google Scholar

[14] Østergaard, L. (2005). Principles of cerebral perfusion imaging by bolus tracking. Journal of Magnetic Resonance Imaging, 22(6), 710–717.10.1002/jmri.20460Search in Google Scholar

[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), 24–34.10.2214/AJR.12.9543Search in Google Scholar

[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), 845–853.10.1002/1522-2594(200006)43:6<845::AID-MRM10>3.0.CO;2-JSearch in Google Scholar

[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), 615–625.10.1002/mrm.21931Search in Google Scholar

[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), 672–681.10.1002/mrm.22042Search in Google Scholar

[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), 732–743.10.1038/jcbfm.2013.10Search in Google Scholar

[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), 193–203.10.1007/s10334-007-0082-2Search in Google Scholar

[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), 1430–1437.10.1002/mrm.24383Search in Google Scholar

[22] Schmiedeskamp, H., Straka, M., Newbould, R.D., et al. (2012). Combined spin- and gradient-echo perfusionweighted imaging. Magnetic Resonance in Medicine, 68, 30–40.10.1002/mrm.23195Search in Google Scholar

[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 T2*${\rm{T}}_2^*$ contrast, pulsed arterial spin labeling, and H2(15)O positron emission tomography. European Journal of Radiology, 70(3), 465–474.10.1016/j.ejrad.2008.02.012Search in Google Scholar

[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.Search in Google Scholar

[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.Search in Google Scholar

[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), 1355–1365.10.1002/mrm.25619Search in Google Scholar

[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), 347–357.10.1002/1522-2586(200008)12:2<347::AID-JMRI19>3.0.CO;2-7Search in Google Scholar

[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), 993–1000.10.1002/mrm.21066Search in Google Scholar

[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), 1012–1021.10.1109/TBME.2011.2182195Search in Google Scholar

[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), 524–531.10.1002/mrm.20759Search in Google Scholar

[31] Madsen, M.T. (1992). A simplified formulation of the gamma variate function. Physics in Medicine and Biology, 37(7), 1597–1600.10.1088/0031-9155/37/7/010Search in Google Scholar

[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), 273–284.10.1002/jmri.20505Search in Google Scholar

[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), 986–992.10.1002/mrm.21040Search in Google Scholar

[34] Schabel, M. C. (2012). A unified impulse response model for DCE-MRI. Magnetic Resonance in Medicine, 68(5), 1632–1646.10.1002/mrm.24162Search in Google Scholar

[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), 764–768.10.1002/mrm.19103006178139461Search in Google Scholar

[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), 930–940.10.1002/jmri.2020615558570Search in Google Scholar

[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), 555–566.10.1002/mrm.19103404128524024Search in Google Scholar

[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), 187–197.10.1002/mrm.2092016724299Search in Google Scholar

[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, 317–322.10.1038/jcbfm.1984.476332115Search in Google Scholar

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

[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), 241–251.10.1002/mrm.23211329266722127934Search in Google Scholar

[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, 748–757.10.1002/jmri.2018215503330Search in Google Scholar

[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.Search in Google Scholar

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