Full paper
Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using statistical modeling
Patrik Brynolfsson,
Jun Yu,
Ronnie Wirestam,
Mikael Karlsson,
Anders Garpebring,
Patrik Brynolfsson
Department of Radiation Physics, Umeå University, Umeå, Sweden
Search for more papers by this authorJun Yu
Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
Search for more papers by this authorRonnie Wirestam
Department of Medical Radiation Physics, Lund University, Lund, Sweden
Search for more papers by this authorMikael Karlsson
Department of Radiation Physics, Umeå University, Umeå, Sweden
Search for more papers by this authorCorresponding Author
Anders Garpebring
Department of Radiation Physics, Umeå University, Umeå, Sweden
CJ Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands
Correspondence to: Anders Garpebring, Ph.D., Department of Radiation Physics, Umeå University, SE-90187 Umeå, Sweden. E-mail: [email protected]Search for more papers by this authorPatrik Brynolfsson,
Jun Yu,
Ronnie Wirestam,
Mikael Karlsson,
Anders Garpebring,
Patrik Brynolfsson
Department of Radiation Physics, Umeå University, Umeå, Sweden
Search for more papers by this authorJun Yu
Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
Search for more papers by this authorRonnie Wirestam
Department of Medical Radiation Physics, Lund University, Lund, Sweden
Search for more papers by this authorMikael Karlsson
Department of Radiation Physics, Umeå University, Umeå, Sweden
Search for more papers by this authorCorresponding Author
Anders Garpebring
Department of Radiation Physics, Umeå University, Umeå, Sweden
CJ Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands
Correspondence to: Anders Garpebring, Ph.D., Department of Radiation Physics, Umeå University, SE-90187 Umeå, Sweden. E-mail: [email protected]Search for more papers by this authorAbstract
Purpose
The purpose of this study was to investigate, using simulations, a method for improved contrast agent (CA) quantification in DCE-MRI.
Methods
We developed a maximum likelihood estimator that combines the phase signal in the DCE-MRI image series with an additional CA estimate, e.g. the estimate obtained from magnitude data. A number of simulations were performed to investigate the ability of the estimator to reduce bias and noise in CA estimates. Noise levels ranging from that of a body coil to that of a dedicated head coil were investigated at both 1.5T and 3T.
Results
Using the proposed method, the root mean squared error in the bolus peak was reduced from 2.24 to 0.11 mM in the vessels and 0.16 to 0.08 mM in the tumor rim for a noise level equivalent of a 12-channel head coil at 3T. No improvements were seen for tissues with small CA uptake, such as white matter.
Conclusion
Phase information reduces errors in the estimated CA concentrations. A larger phase response from higher field strengths or higher CA concentrations yielded better results. Issues such as background phase drift need to be addressed before this method can be applied in vivo. Magn Reson Med 74:1156–1164, 2015. © 2014 Wiley Periodicals, Inc.
REFERENCES
- 1 Sourbron SP, Buckley DL. Classic models for dynamic contrast-enhanced MRI. NMR Biomed 2013; 26: 1004–1027.
- 2 Verma S, Turkbey B, Muradyan N, Rajesh A, Cornud F, Haider MA, Choyke PL, Harisinghani M. Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. AJR Am J Roentgenol 2012; 198: 1277–1288.
- 3 Cao Y. The promise of dynamic contrast-enhanced imaging in radiation therapy. Semin Radiat Oncol 2011; 21: 147–156.
- 4 Sommer WH, Sourbron S, Huppertz A, Ingrisch M, Reiser MF, Zech CJ. Contrast agents as a biological marker in magnetic resonance imaging of the liver: conventional and new approaches. Abdom Imaging 2012; 37: 164–179.
- 5 Sourbron SP, Buckley DL. Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys Med Biol 2012; 57: R1–R33.
- 6 Cheng H-LM. T1 measurement of flowing blood and arterial input function determination for quantitative 3D T1-weighted DCE-MRI. J Magn Reson Imaging 2007; 25: 1073–1078.
- 7 Schabel M, Parker D. Uncertainty and bias in contrast concentration measurements using spoiled gradient echo pulse sequences. Phys Med Biol 2008; 53: 2345–2373.
- 8 Garpebring A, Wirestam R, Ostlund N, Karlsson M. Effects of inflow and radiofrequency spoiling on the arterial input function in dynamic contrast-enhanced MRI: a combined phantom and simulation study. Magn Reson Med 2011; 65: 1670–1679.
- 9 Roberts C, Little R, Watson Y, Zhao S, Buckley DL, Parker GJM. 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: 108–119.
- 10 Preibisch C, Deichmann R. Influence of RF spoiling on the stability and accuracy of T1 mapping based on spoiled FLASH with varying flip angles. Magn Reson Med 2009; 61: 125–135.
- 11 Donahue KM, Weisskoff RM, Burstein D. Water diffusion and exchange as they influence contrast enhancement. J Magn Reson Imaging 1997; 7: 102–110.
- 12 Stanisz GJ, Henkelman RM. Gd-DTPA relaxivity depends on macromolecular content. Magn Reson Med 2000; 44: 665–667.
- 13 Akbudak E, Conturo TE. Arterial input functions from MR phase imaging. Magn Reson Med 1996; 36: 809–815.
- 14 Neelavalli J, Cheng Y-CN, Jiang J, Haacke EM. Removing background phase variations in susceptibility-weighted imaging using a fast, forward-field calculation. J Magn Reson Imaging 2009; 29: 937–948.
- 15 Marques JP, Bowtell R. Application of a Fourier-based method for rapid calculation of field inhomogeneity due to spatial variation of magnetic susceptibility. Concepts Magn Reson Part B Magn Reson Eng 2005; 25B: 65–78.
- 16 De Rochefort L, Nguyen T, Brown R, Spincemaille P, Choi G, Weinsaft J, Prince MR, Wang Y. In vivo quantification of contrast agent concentration using the induced magnetic field for time-resolved arterial input function measurement with MRI. Med Phys 2008; 35: 5328–5339.
- 17 Foottit C, Cron GO, Hogan MJ, Nguyen TB, Cameron I. Determination of the venous output function from MR signal phase: feasibility for quantitative DCE-MRI in human brain. Magn Reson Med 2010; 63: 772–781.
- 18 Korporaal JG, van den Berg CA, van Osch MJ, Groenendaal G, van Vulpen M, van der Heide UA. 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: 1267–1274.
- 19 Garpebring A, Wirestam R, Yu J, Asklund T, Karlsson M. Phase-based arterial input functions in humans applied to dynamic contrast-enhanced MRI: potential usefulness and limitations. MAGMA 2011; 24: 233–245.
- 20 Conturo TE, Barker PB, Mathews VP, Monsein LH, Bryan RN. MR imaging of cerebral perfusion by phase-angle reconstruction of bolus paramagnetic-induced frequency shifts. Magn Reson Med 1992; 27: 375–390.
- 21 Petridou N, Wharton SJ, Lotfipour A, Gowland P, Bowtell R. Investigating the effect of blood susceptibility on phase contrast in the human brain. Neuroimage 2010; 50: 491–498.
- 22 Wirestam R, Lindgren E, van Westen D, Bloch KM, Ståhlberg F, Knutsson L. Cerebral perfusion information obtained by dynamic contrast-enhanced phase-shift magnetic resonance imaging: comparison with model-free arterial spin labelling. Clin Physiol Funct Imaging 2010; 30: 375–379.
- 23 De Rochefort L, Liu T, Kressler B, Liu J, Spincemaille P, Lebon V, Wu J, Wang Y. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. Magn Reson Med 2010; 63: 194–206.
- 24 Liu T, Spincemaille P, de Rochefort L, Kressler B, Wang Y. Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magn Reson Med 2009; 61: 196–204.
- 25 Schweser F, Deistung A, Sommer K, Reichenbach JR. Toward online reconstruction of quantitative susceptibility maps: superfast dipole inversion. Magn Reson Med 2013; 69: 1582–1594.
- 26 Aubert-Broche B, Evans AC, Collins L. A new improved version of the realistic digital brain phantom. Neuroimage 2006; 32: 138–145.
- 27 Parker GJM, Roberts C, Macdonald A, Buonaccorsi GA, Cheung S, Buckley DL, Jackson A, Watson Y, Davies K, Jayson GC. 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.
- 28 Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 1997; 7: 91–101.
- 29 Tincher M, Meyer CR, Gupta R, Williams DM. Polynomial modeling and reduction of RF body coil spatial inhomogeneity in MRI. IEEE Trans Med Imaging 1993; 12: 361–365.
- 30 Haacke EM, Brown RW, Thompson MR, Venkatesan R. Magnetic resonance imaging: physical principles and sequence design. Hoboken, NJ: Wiley; 1999.
- 31 Sijbers J. Signal and noise estimation from magnetic resonance images. Antwerp, Belgium: University of Antwerp; 1998.
- 32 Van Osch MJ, Vonken EP, Viergever MA, van der Grond J, Bakker CJ. Measuring the arterial input function with gradient echo sequences. Magn Reson Med 2003; 49: 1067–1076.
- 33 Beld E, Simonis FFJ, Korporaal JG, van der Heide UA, van den Berg CAT. Automated correction method allowing phase-based detection of contrast enhancement in DCE-MRI. In Proceedings of the 23rd Annual Meeting of ISMRM, Milan, Italy, 2014. p. 527.
- 34 Zhou D, Liu T, Spincemaille P, Wang Y. Background field removal by solving the Laplacian boundary value problem. NMR Biomed 2014; 27: 312–319.
- 35 Schweser F, Deistung A, Lehr BW, Reichenbach JR. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism? Neuroimage 2011; 54: 2789–2807.
- 36 Schenck J. The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. Med Phys 1996; 23: 815–850.
- 37 Oros-Peusquens AM, Laurila M, Shah NJ. Magnetic field dependence of the distribution of NMR relaxation times in the living human brain. MAGMA 2008; 21: 131–147.
- 38 Rooney WD, Johnson G, Li X, Cohen ER, Kim S-G, Ugurbil K, Springer CS. Magnetic field and tissue dependencies of human brain longitudinal 1H2O relaxation in vivo. Magn Reson Med 2007; 57: 308–318.
- 39 Rohrer M, Bauer H, Mintorovitch J. Comparison of magnetic properties of MRI contrast media solutions at different magnetic field strengths. Invest Radiol 2005; 40: 715–724.
- 40 Kjølby BF, Østergaard L, Kiselev VG. Theoretical model of intravascular paramagnetic tracers effect on tissue relaxation. Magn Reson Med 2006; 56: 187–197.
- 41 Bottomley P, Foster T, Argersinger R, Pfeifer L. A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1–100 MHz: dependence on tissue type, NMR frequency, temperature, species, excision, and age. Med Phys 1984; 11: 425–428.
- 42 Hopkins JA, Wehrli FW. Magnetic susceptibility measurement of insoluble solids by NMR: magnetic susceptibility of bone. Magn Reson Med 1997; 37: 494–500.
- 43 Venkatesan R, Lin W, Haacke EM. Accurate determination of spin-density and T1 in the presence of RF-field inhomogeneities and flip-angle miscalibration. Magn Reson Med 1998; 40: 592–602.
- 44 Padhani AR, Hayes C, Landau S, Leach MO. Reproducibility of quantitative dynamic MRI of normal human tissues. NMR Biomed 2002; 15: 143–153.
- 45 Robson MD, Gatehouse PD, Bydder M, Bydder GM. Magnetic resonance: an introduction to ultrashort TE (UTE) imaging. J Comput Assist Tomogr 2003; 27: 825–846.
- 46 Thomsen C, Sørensen PG, Karle H, Christoffersen P, Henriksen O. Prolonged bone marrow T1-relaxation in acute leukaemia. In vivo tissue characterization by magnetic resonance imaging. Magn Reson Imaging 1987; 5: 251–257.
- 47 Garpebring A, Brynolfsson P, Yu J, Wirestam R, Johansson A, Asklund T, Karlsson M. Uncertainty estimation in dynamic contrast-enhanced MRI. Magn Reson Med 2013; 69: 992–1002.
