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  • Cited by 9
  • Print publication year: 2013
  • Online publication date: May 2013

4 - DCE-MRI: acquisition and analysis techniques

from Section 1 - Techniques

Summary

Introduction

There are increasing opportunities to use dynamic contrast-enhanced (DCE) T1-weighted imaging to characterize tumor and other pathological biology and treatment response, using modern fast sequences that can provide good temporal and spatial resolution combined with good organ coverage [1]. Quantification in MRI is recognized as an important approach to characterize tissue biology. This chapter provides an introduction to the physical concepts of mathematical modeling, image acquisition, and image analysis needed to measure aspects of tissue biology using DCE imaging, in a way that should be accessible for a research-minded clinician.

Quantification in MRI represents a paradigm shift, a new way of thinking about imaging [2]. In qualitative studies, the scanner is a highly sophisticated camera, collecting images that are viewed by an experienced radiologist. In quantitative studies, the scanner is used as a sophisticated measuring device, a scientific instrument able to measure many properties of each tissue voxel (e.g., T1, T2, diffusion tensor,magnetization transfer, metabolite concentration, Ktrans). An everyday example of quantification would be the bathroom scales, used to measure our weight. We expect that the machine output shown on the dial, in kg, will be accurate (i.e., close to the true value), reproducible (i.e., if we make repeated measurements over a short time they will not vary), reliable (the scales always work), and biologically relevant (the quantity of weight does indeed relate to our health). An example of a clinical measurement would be a blood test; we expect it to work reliably every time. This is the aspiration for quantitative MRI: that it should deliver a high-quality measurement that relates only to the patient biology (and not the state of the scanner at the time of measurement).

References
Jackson, A, Buckley, DL, Parker, GJ.Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Oncology. Berlin: Springer, 2004.
Tofts, PS.Quantitative MRI of the Brain: Measuring Changes Caused by Disease. New York: Wiley, 2003.
Tofts, PS, Brix, G, Buckley, DL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999;10:223–32.
Leach, MO, Brindle, KM, Evelhoch, JL, et al. The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer 2005;92:1599–610.
Tofts, PS, Kermode, AG.Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med 1991;17:357–67.
Naish, JH, McGrath, DM, Bains, LJ, et al. Comparison of dynamic contrast-enhanced MRI and dynamic contrast-enhanced CT biomarkers in bladder cancer. Magn Reson Med 2011; 66: 219–26.
Yang, C, Stadler, WM, Karczmar, GS, et al. Comparison of quantitative parameters in cervix cancer measured by dynamic contrast-enhanced MRI and CT.Magn Reson Med 2010;63:1601–9.
Tofts, PS, Shuter, B, Pope, JM.Ni-DTPA doped agarose gel–a phantom material for Gd-DTPA enhancement measurements. Magn Reson Imaging 1993;11:125–33.
Shuter, B, Tofts, PS, Wang, SC, Pope, JM.The relaxivity of Gd-EOB-DTPA and Gd-DTPA in liver and kidney of the Wistar rat. Magn Reson Imaging 1996;14:243–53.
Stanisz, GJ, Henkelman, RM.Gd-DTPA relaxivity depends on macromolecular content. Magn Reson Med 2000;44:665–7.
Spees, WM, Yablonskiy, DA, Oswood, MC, Ackerman, JJ.Water proton MR properties of human blood at 1.5 Tesla: magnetic susceptibility, T(1), T(2), T*(2), and non-Lorentzian signal behavior. Magn Reson Med 2001;45:533–42.
Boron, WF, Boulpaep, EL.Medical Physiology. Philadelphia: Saunders, 2008.
Roberts, C, Hughes, S, Naish, JH, et al. Individually Measured Hematocrit in DCE-MRI studies. Proc Intl Soc Magn Reson Med, Montreal, Canada, 2011; 1078.
Teorell, T.Kinetics of distribution of substances admitted to the body. I. The extravascular modes of administration. Arch Int Pharmacodyn Ther 1937;57:205–25.
Kety, SS.The theory and applications of the exchange of inert gas at the lungs and tissues. Pharmacol Rev 1951;3:1–41.
Weinmann, HJ, Laniado, M, Mutzel, W.Pharmacokinetics of GdDTPA/dimeglumine after intravenous injection into healthy volunteers. Physiol Chem Phys Med NMR 1984;16:167–72.
Parker, GJ, Roberts, C, Macdonald, A,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;56:993–1000.
Horsfield, MA, Thornton, JS, Gill, A, et al. A functional form for injected MRI Gd-chelate contrast agent concentration incorporating recirculation, extravasation and excretion. Phys Med Biol 2009;54:2933–49.
Tofts, PS.Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 1997;7:91–101.
Sourbron, SP, Buckley, DL.On the scope and interpretation of the Tofts models for DCE-MRI. Magn Reson Med 2011;66:735–45.
Sourbron, SP, Buckley, DL.Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys Med Biol 2012; 57:R1–33.
Pries, AR, Ley, K, Gaehtgens, P.Generalization of the Fahraeus principle for microvessel networks. Am J Physiol 1986;251:H1324–32.
Crystal, GJ, Downey, HF, Bashour, FA.Small vessel and total coronary blood volume during intracoronary adenosine. Am J Physiol 1981;241:H194–201.
Sakai, F, Nakazawa, K, Tazaki, Y, et al. Regional cerebral blood volume and hematocrit measured in normal human volunteers by single-photon emission computed tomography. J Cereb Blood Flow Metab 1985;5:207–13.
Rempp, KA, Brix, G, Wenz, F, et al.Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging. Radiology 1994;193:637–41.
Gaehtgens, P.Flow of blood through narrow capillaries: rheological mechanisms determining capillary hematocrit and apparent viscosity. Biorheology 1980;17:183–9.
Tofts, PS, Cutajar, M, Mendichovszky, IA, Gordon, I.Accurate and precise measurement of renal filtration and vascular parameters using DCE-MRI and a 3-compartment model. Proc Intl Soc Magn Reson Med, Stockholm, Sweden, 2010; 326.
Tofts, PS, Cutajar, M, Mendichovszky, IA, Peters, AM, Gordon, I.Precise measurement of renal filtration and vascular parameters using a two-compartment model for dynamic contrast-enhanced MRI of the kidney gives realistic normal values. Eur Radiol 2012;22:1320–30.
Lawrence, KS, Lee, TY.An adiabatic approximation to the tissue homogeneity model for water exchange in the brain: I. Theoretical derivation. J Cereb Blood Flow Metab 1998;18:1365–77.
Donaldson, SB, West, CM, Davidson, SE, et al. A comparison of tracer kinetic models for T1-weighted dynamic contrast-enhanced MRI: application in carcinoma of the cervix. Magn Reson Med 2010;63:691–700.
Hatabu, H, Tadamura, E, Levin, DL, et al. Quantitative assessment of pulmonary perfusion with dynamic contrast-enhanced MRI.Magn Reson Med 1999;42:1033–8.
Ohno, Y, Hatabu, H, Murase, K, et al. Quantitative assessment of regional pulmonary perfusion in the entire lung using three-dimensional ultrafast dynamic contrast-enhanced magnetic resonance imaging: preliminary experience in 40 subjects. J Magn Reson Imaging 2004;20:353–65.
Jerosch-Herold, M.Quantification of myocardial perfusion by cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2010;12:57.
Naish, JH, Kershaw, LE, Buckley, DL, et al. Modeling of contrast agent kinetics in the lung using T1-weighted dynamic contrast-enhanced MRI. Magn Reson Med 2009;61:1507–14.
Brix, G, Kiessling, F, Lucht, R, et al.Microcirculation and microvasculature in breast tumors: pharmacokinetic analysis of dynamic MR image series. Magn Reson Med 2004;52:420–9.
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;62:205–17.
Brix, G, Zwick, S, Kiessling, F, Griebel, J.Pharmacokinetic analysis of tissue microcirculation using nested models: multimodel inference and parameter identifiability. Med Phys 2009;36:2923–33.
Johnson, JA, Wilson, TA.A model for capillary exchange. Am J Physiol 1966;210:1299–303.
Koh, TS, Zeman, V, Darko, J, et al.The inclusion of capillary distribution in the adiabatic tissue homogeneity model of blood flow. Phys Med Biol 2001;46:1519–38.
Tofts, PS.QA: quality assurance, accuracy, precision and phantoms. In: Tofts, P, editor, Quantitative MRI of the Brain: Measuring Changes Caused by Disease. Chichester: John Wiley, 2003;55–81.
Buonaccorsi, GA, O'Connor, , JP, Caunce, , A, et al. Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data. Magn Reson Med 2007;58:1010–19.
Henderson, E, Rutt, BK, Lee, TY.Temporal sampling requirements for the tracer kinetics modeling of breast disease. Magn Reson Imaging 1998;16:1057–73.
Kostler, H, Ritter, C, Lipp, M, et al.Prebolus quantitative MR heart perfusion imaging. Magn Reson Med 2004;52:296–9.
Risse, F, Semmler, W, Kauczor, HU, Fink, C.Dual-bolus approach to quantitative measurement of pulmonary perfusion by contrast-enhanced MRI. J Magn Reson Imaging 2006;24:1284–90.
Korporaal, JG, van den Berg, CA, van Osch, MJ, et al. Phase-based arterial input function measurements in the femoral arteries for quantification of dynamic contrast-enhanced (DCE) MRI and comparison with DCE-CT. Magn Reson Med 2011; 66:1267–74.
Yankeelov, TE, Luci, JJ, Lepage, M, et al. Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. Magn Reson Imaging 2005;23:519–29.
Roberts, C, Little, R, Watson, Y, et al. The effect of blood inflow and B(1)-field inhomogeneity on measurement of the arterial input function in axial 3D spoiled gradient echo dynamic contrast-enhanced MRI. Magn Reson Med 2011;65:108–19.
Buckley, DL, Shurrab, AE, Cheung, CM, et al. Measurement of single kidney function using dynamic contrast-enhanced MRI: comparison of two models in human subjects. J Magn Reson Imaging 2006;24:1117–23.
Barker, GJ, Simmons, A, Arridge, SR, Tofts, PS.A simple method for investigating the effects of non-uniformity of radiofrequency transmission and radiofrequency reception in MRI. Br J Radiol 1998;71:59–67.
Brookes, JA, Redpath, TW, Gilbert, FJ, Murray, AD, Staff, RT.Accuracy of T1 measurement in dynamic contrast-enhanced breast MRI using two- and three-dimensional variable flip angle fast low-angle shot. J Magn Reson Imaging 1999;9:163–71.
Parker, GJ, Barker, GJ, Tofts, PS.Accurate multislice gradient echo T(1) measurement in the presence of non-ideal RF pulse shape and RF field nonuniformity. Magn Reson Med 2001;45:838–45.
Dowell, NG, Tofts, PS.Fast, accurate, and precise mapping of the RF field in vivo using the 180 degrees signal null. Magn Reson Med 2007;58:622–30.
Tofts, PS, Berkowitz, B, Schnall, MD.Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumors using a permeability model. Magn Reson Med 1995;33:564–8.
Fritz-Hansen, T, Rostrup, E, Larsson, HB, Sondergaard, L, Ring, P, Henriksen, O.Measurement of the arterial concentration of Gd-DTPA using MRI: a step toward quantitative perfusion imaging. Magn Reson Med 1996;36:225–31.
O'Connor, JP, Jayson, GC, Jackson, A,et al. Enhancing fraction predicts clinical outcome following first-line chemotherapy in patients with epithelial ovarian carcinoma. Clin Cancer Res 2007;13:6130–5.
Donaldson, SB, Buckley, DL, O'Connor, JP, et al. Enhancing fraction measured using dynamic contrast-enhanced MRI predicts disease-free survival in patients with carcinoma of the cervix. Br J Cancer 2010;102:23–6.
Mills, SJ, Soh, C, O'Connor, JP, et al. Tumour enhancing fraction (EnF) in glioma: relationship to tumour grade. Eur Radiol 2009;19:1489–98.
Bagher-Ebadian, H, Jain, R, Nejad-Davarani, SP, et al. Model selection for DCE-T1 studies in glioblastoma. Magn Reson Med 2011;68:241–51.
Rose, CJ, Mills, SJ, O'Connor, JP, et al. Quantifying spatial heterogeneity in dynamic contrast-enhanced MRI parameter maps. Magn Reson Med 2009;62:488–99.
Canuto, HC, McLachlan, C, Kettunen, MI, et al. Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent. Magn Reson Med 2009;61:1218–24.
Tofts, PS, Davies, GR, Dehmeshki, J.Histograms: measuring subtle diffuse disease. In: Tofts, P, editor. Quantitative MRI of the Brain: Measuring Changes Caused by Disease. Chichester: John Wiley, 2003;581–610.
Tofts, PS, Benton, CE, Weil, RS, et al. Quantitative analysis of whole-tumor Gd enhancement histograms predicts malignant transformation in low-grade gliomas. J Magn Reson Imaging 2007;25:208–14.
Dehmeshki, J, Ruto, AC, Arridge, S, et al. Analysis of MTR histograms in multiple sclerosis using principal components and multiple discriminant analysis. Magn Reson Med 2001;46:600–9.
Tofts PS, Stoyanova R. Fast modelling of slow DCE data from prostate: rate constant (kep) and extracellular extravascular space (EES: ve) both distinguish hypoxic regions in the tumour. European Society for Magnetic Resonance in Medicine and Biology Congress Leipzig 2011; 27.
Buckley, DL, Roberts, C, Parker, GJ, Logue, JP, Hutchinson, CE.Prostate cancer: evaluation of vascular characteristics with dynamic contrast-enhanced T1-weighted MR imaging–initial experience. Radiology 2004;233:709–15.
Hodgson, RJ, Barnes, T, Connolly, S, et al. Changes underlying the dynamic contrast-enhanced MRI response to treatment in rheumatoid arthritis. Skeletal Radiol 2008;37:201–7.