A novel technique for modeling susceptibility-based contrast mechanisms for arbitrary microvascular geometries: The finite perturber method

Recently, we demonstrated that vessel geometry is a significant determinant of susceptibility-induced contrast in MRI. This is especially relevant for susceptibility-contrast enhanced MRI of tumors with their characteristically abnormal vessel morphology. In order to better understand the biophysics of this contrast mechanism, it is of interest to model how various factors, including microvessel morphology contribute to the measured MR signal, and was the primary motivation for developing a novel computer modeling approach called the Finite Perturber Method (FPM). The FPM circumvents the limitations of traditional fixed-geometry approaches, and enables us to study susceptibility-induced contrast arising from arbitrary microvascular morphologies in 3D, such as those typically observed with brain tumor angiogenesis. Here we describe this new modeling methodology and some of its applications. The excellent agreement of the FPM with theory and the extant susceptibility modeling data, coupled with its computational efficiency demonstrates its potential to transform our understanding of the factors that engender susceptibility contrast in MRI.

[1]  Yu-Chung N. Cheng,et al.  Magnetic Resonance Imaging: Physical Principles and Sequence Design , 1999 .

[2]  P. Lantos,et al.  The vasculature of experimental brain tumours Part 2. A quantitative assessment of morphological abnormalities , 1981, Journal of the Neurological Sciences.

[3]  J. Gore,et al.  Intravascular susceptibility contrast mechanisms in tissues , 1994, Magnetic resonance in medicine.

[4]  B. Rosen,et al.  Perfusion imaging with NMR contrast agents , 1990, Magnetic resonance in medicine.

[5]  B. Rosen,et al.  Microscopic susceptibility variation and transverse relaxation: Theory and experiment , 1994, Magnetic resonance in medicine.

[6]  B. Rosen,et al.  Susceptibility contrast imaging of cerebral blood volume: Human experience , 1991, Magnetic resonance in medicine.

[7]  V. Kiselev Transverse relaxation effect of MRI contrast agents: A crucial issue for quantitative measurements of cerebral perfusion , 2005, Journal of magnetic resonance imaging : JMRI.

[8]  B. Rosen,et al.  Functional mapping of the human visual cortex by magnetic resonance imaging. , 1991, Science.

[9]  Mark Jenkinson,et al.  Perturbation method for magnetic field calculations of nonconductive objects , 2004, Magnetic resonance in medicine.

[10]  A S Greene,et al.  Imaging system for three-dimensional mapping of cerebrocortical capillary networks in vivo. , 1993, Microvascular research.

[11]  E F Halpern,et al.  Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. , 1994, Radiology.

[12]  K. Zierler Theoretical Basis of Indicator‐Dilution Methods For Measuring Flow and Volume , 1962 .

[13]  V G Kiselev On the theoretical basis of perfusion measurements by dynamic susceptibility contrast MRI , 2001, Magnetic resonance in medicine.

[14]  John S Leigh,et al.  Quantifying arbitrary magnetic susceptibility distributions with MR , 2004, Magnetic resonance in medicine.

[15]  E. Haacke,et al.  Theory of NMR signal behavior in magnetically inhomogeneous tissues: The static dephasing regime , 1994, Magnetic resonance in medicine.

[16]  E. M. Lifshitz,et al.  Electrodynamics of continuous media , 1961 .

[17]  Bruce R. Rosen,et al.  MR Diffusion Imaging of the Human Brain , 1990, Journal of computer assisted tomography.

[18]  Xenophon Papademetris,et al.  Rapid calculations of susceptibility-induced magnetostatic field perturbations for in vivo magnetic resonance , 2006, Physics in medicine and biology.

[19]  S. Ogawa,et al.  Magnetic resonance imaging of blood vessels at high fields: In vivo and in vitro measurements and image simulation , 1990, Magnetic resonance in medicine.

[20]  Mark S. Cohen,et al.  Contrast agents and cerebral hemodynamics , 1991, Magnetic resonance in medicine.

[21]  A P Pathak,et al.  Utility of simultaneously acquired gradient‐echo and spin‐echo cerebral blood volume and morphology maps in brain tumor patients , 2000, Magnetic resonance in medicine.

[22]  K. J. Binns,et al.  Analysis and computation of electric and magnetic field problems , 1973 .

[23]  R. Weisskoff,et al.  MRI susceptometry: Image‐based measurement of absolute susceptibility of MR contrast agents and human blood , 1992, Magnetic resonance in medicine.

[24]  Samuel Fox,et al.  41st ANNUAL MEETING , 1964 .

[25]  B R Rosen,et al.  Mr contrast due to intravascular magnetic susceptibility perturbations , 1995, Magnetic resonance in medicine.

[26]  A P Pathak,et al.  MR‐derived cerebral blood volume maps: Issues regarding histological validation and assessment of tumor angiogenesis , 2001, Magnetic resonance in medicine.

[27]  J. R. Baker,et al.  The intravascular contribution to fmri signal change: monte carlo modeling and diffusion‐weighted studies in vivo , 1995, Magnetic resonance in medicine.

[28]  B. Rosen,et al.  MR Contrast Due to Microscopically Heterogeneous Magnetic Susceptibility: Numerical Simulations and Applications to Cerebral Physiology , 1991, Magnetic resonance in medicine.

[29]  R Weissleder,et al.  Monocrystalline iron oxide nanocompounds (MION): Physicochemical properties , 1993, Magnetic resonance in medicine.

[30]  T Kubota,et al.  Tumor vascularity in the brain: evaluation with dynamic susceptibility-contrast MR imaging. , 1993, Radiology.

[31]  Scott D Rand,et al.  The effect of brain tumor angiogenesis on the in vivo relationship between the gradient‐echo relaxation rate change (ΔR2*) and contrast agent (MION) dose , 2003, Journal of magnetic resonance imaging : JMRI.

[32]  Peter A. Bandettini,et al.  Effects of biophysical and physiologic parameters on brain activation‐induced R2* and R2 changes: Simulations using a deterministic diffusion model , 1995, Int. J. Imaging Syst. Technol..