Combining phase and magnitude information for contrast agent quantification in dynamic contrast‐enhanced MRI using statistical modeling

The purpose of this study was to investigate, using simulations, a method for improved contrast agent (CA) quantification in DCE‐MRI.

[1]  Ferdinand Schweser,et al.  Toward online reconstruction of quantitative susceptibility maps: Superfast dipole inversion , 2013, Magnetic resonance in medicine.

[2]  Maximilian F. Reiser,et al.  Contrast agents as a biological marker in magnetic resonance imaging of the liver: conventional and new approaches , 2012, Abdominal Imaging.

[3]  Ferdinand Schweser,et al.  Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism? , 2011, NeuroImage.

[4]  Pascal Spincemaille,et al.  In vivo quantification of contrast agent concentration using the induced magnetic field for time-resolved arterial input function measurement with MRI. , 2008, Medical physics.

[5]  Yue Cao,et al.  The promise of dynamic contrast-enhanced imaging in radiation therapy. , 2011, Seminars in radiation oncology.

[6]  J. Mintorovitch,et al.  Comparison of Magnetic Properties of MRI Contrast Media Solutions at Different Magnetic Field Strengths , 2005, Investigative radiology.

[7]  T E Conturo,et al.  Arterial input functions from MR phase imaging , 1996, Magnetic resonance in medicine.

[8]  A. Jackson,et al.  Experimentally‐derived functional form for a population‐averaged high‐temporal‐resolution arterial input function for dynamic contrast‐enhanced MRI , 2006, Magnetic resonance in medicine.

[9]  R. Gupta,et al.  Polynomial modeling and reduction of RF body coil spatial inhomogeneity in MRI , 1993, IEEE Trans. Medical Imaging.

[10]  R. Deichmann,et al.  Influence of RF spoiling on the stability and accuracy of T1 mapping based on spoiled FLASH with varying flip angles , 2009, Magnetic resonance in medicine.

[11]  Geoff J M Parker,et al.  The effect of blood inflow and B1‐field inhomogeneity on measurement of the arterial input function in axial 3D spoiled gradient echo dynamic contrast‐enhanced MRI , 2011, Magnetic resonance in medicine.

[12]  Thanh Binh Nguyen,et al.  Determination of the venous output function from MR signal phase: Feasibility for quantitative DCE‐MRI in human brain , 2010, Magnetic resonance in medicine.

[13]  Steven P Sourbron,et al.  Classic models for dynamic contrast‐enhanced MRI , 2013, NMR in biomedicine.

[14]  T. Foster,et al.  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. , 1984, Medical physics.

[15]  M. Viergever,et al.  Measuring the arterial input function with gradient echo sequences , 2003, Magnetic resonance in medicine.

[16]  Yi Wang,et al.  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 , 2009, Magnetic resonance in medicine.

[17]  Karin Markenroth Bloch,et al.  Cerebral perfusion information obtained by dynamic contrast‐enhanced phase‐shift magnetic resonance imaging: comparison with model‐free arterial spin labelling , 2010, Clinical Physiology and Functional Imaging.

[18]  Mark Bydder,et al.  Magnetic Resonance: An Introduction to Ultrashort TE (UTE) Imaging , 2003, Journal of computer assisted tomography.

[19]  D L Buckley,et al.  Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability , 2012, Physics in medicine and biology.

[20]  J. Schenck.  The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. , 1996, Medical physics.

[21]  Marco van Vulpen,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 , 2011, Magnetic resonance in medicine.

[22]  D. Louis Collins,et al.  A new improved version of the realistic digital brain phantom , 2006, NeuroImage.

[23]  Baris Turkbey,et al.  Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. , 2012, AJR. American journal of roentgenology.

[24]  P. Tofts.  Modeling tracer kinetics in dynamic Gd‐DTPA MR imaging , 1997, Journal of magnetic resonance imaging : JMRI.

[25]  Yi Wang,et al.  Background field removal by solving the Laplacian boundary value problem , 2014, NMR in biomedicine.

[26]  R M Henkelman,et al.  Gd‐DTPA relaxivity depends on macromolecular content , 2000, Magnetic resonance in medicine.

[27]  Hai-Ling Margaret Cheng,et al.  T1 measurement of flowing blood and arterial input function determination for quantitative 3D T1‐weighted DCE‐MRI , 2007, Journal of magnetic resonance imaging : JMRI.

[28]  E M Haacke,et al.  Accurate determination of spin‐density and T1 in the presence of RF‐field inhomogeneities and flip‐angle miscalibration , 1998, Magnetic resonance in medicine.

[29]  R. Wirestam,et al.  Phase-based arterial input functions in humans applied to dynamic contrast-enhanced MRI: potential usefulness and limitations , 2011, Magnetic Resonance Materials in Physics, Biology and Medicine.

[30]  V. Kiselev,et al.  Theoretical model of intravascular paramagnetic tracers effect on tissue relaxation , 2006, Magnetic resonance in medicine.

[31]  R M Weisskoff,et al.  Water diffusion and exchange as they influence contrast enhancement , 1997, Journal of magnetic resonance imaging : JMRI.

[32]  A. Padhani,et al.  Reproducibility of quantitative dynamic MRI of normal human tissues , 2002, NMR in biomedicine.

[33]  N. J. Shah,et al.  Magnetic field dependence of the distribution of NMR relaxation times in the living human brain , 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.

[34]  Richard Bowtell,et al.  Investigating the effect of blood susceptibility on phase contrast in the human brain , 2010, NeuroImage.

[35]  Jun Yu,et al.  Uncertainty estimation in dynamic contrast‐enhanced MRI , 2013, Magnetic resonance in medicine.

[36]  D. Parker,et al.  Uncertainty and bias in contrast concentration measurements using spoiled gradient echo pulse sequences , 2008, Physics in medicine and biology.

[37]  R. Wirestam,et al.  Effects of inflow and radiofrequency spoiling on the arterial input function in dynamic contrast‐enhanced MRI: A combined phantom and simulation study , 2011, Magnetic resonance in medicine.

[38]  K. Uğurbil,et al.  Magnetic field and tissue dependencies of human brain longitudinal 1H2O relaxation in vivo , 2007, Magnetic resonance in medicine.

[39]  Paul Strauss,et al.  Magnetic Resonance Imaging Physical Principles And Sequence Design , 2016 .

[40]  Yi Wang,et al.  Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: Validation and application to brain imaging , 2010, Magnetic resonance in medicine.

[41]  R. Bowtell,et al.  Application of a Fourier‐based method for rapid calculation of field inhomogeneity due to spatial variation of magnetic susceptibility , 2005 .

[42]  T. Conturo,et al.  Mr imaging of cerebral perfusion by phase‐angle reconstruction of bolus paramagnetic‐induced frequency shifts , 1992, Magnetic resonance in medicine.

[43]  C Thomsen,et al.  Prolonged bone marrow T1-relaxation in acute leukaemia. In vivo tissue characterization by magnetic resonance imaging. , 1987, Magnetic resonance imaging.

[44]  Jaladhar Neelavalli,et al.  Removing background phase variations in susceptibility‐weighted imaging using a fast, forward‐field calculation , 2009, Journal of magnetic resonance imaging : JMRI.

[45]  J. Sijbers,et al.  Signal and noise estimation from magnetic resonance images , 1998 .

[46]  F W Wehrli,et al.  Magnetic susceptibility measurement of insoluble solids by NMR: Magnetic susceptibility of bone. , 1997, Magnetic resonance in medicine.