Advantages of using multiple-echo image combination and asymmetric triangular phase masking in magnetic resonance venography at 3 T.

The present work explores the possibility of localizing veins with magnetic resonance venography using susceptibility weighted imaging. It also seeks new approaches, directed by the spatial specificity of activated brain regions, that have sufficient precision for practical use in functional MRI studies. A 3D flow compensated multiple gradient echo sequence, featuring optimized T2* weighting within a reasonable time of acquisition (11 min) and a small voxel size (0.5x0.5x1 mm3), was used to acquire MR images at 3 T. Post-processing consisted of homodyne filtering, linear phase scaling and magnitude masking prior to minimum intensity projection (mIP). The multiple echo approach provided a satisfactory (48+/-7%) increase in signal-to-noise ratio with respect to conventional methods. Specific features of the blood oxygenation level-dependent phase effect were simulated and used for designing and exploring different phase masking methods in relation to vessel morphology and MRI voxel geometry. As with simulations, the best results were obtained with an asymmetric triangular phase masking, featuring an improved venographic contrast without any increase in the full-width at half-maximum. The multiple echo approach provided satisfactory vessel localization capacity by using asymmetric triangular phase masking and a 4-mm-thick mIP. The venographic contrast obtained enabled the detection of vessels with diameter down to approximately 500 microm, suggesting the applicability of the proposed method as an additional technique in fMRI studies.

[1]  C. Springer,et al.  Bulk magnetic susceptibility shifts in nmr studies of compartmentalized samples: use of paramagnetic reagents , 1990, Magnetic resonance in medicine.

[2]  Nan-kuei Chen,et al.  Functional MRI with variable echo time acquisition , 2003, NeuroImage.

[3]  J R Reichenbach,et al.  High-Resolution MR Venography at 3.0 Tesla , 2000, Journal of computer assisted tomography.

[4]  G. Glover,et al.  Neuroimaging at 1.5 T and 3.0 T: Comparison of oxygenation‐sensitive magnetic resonance imaging , 2001, Magnetic resonance in medicine.

[5]  J. Bodurka,et al.  Current-induced magnetic resonance phase imaging. , 1999, Journal of magnetic resonance.

[6]  E. Haacke,et al.  High‐resolution BOLD venographic imaging: a window into brain function , 2001, NMR in biomedicine.

[7]  Christian W A Pfirrmann,et al.  Imaging of patellar cartilage with a 2D multiple-echo data image combination sequence. , 2005, AJR. American journal of roentgenology.

[8]  A. Rhoton,et al.  Microsurgical anatomy of the deep venous system of the brain. , 1984, Neurosurgery.

[9]  Ravi S. Menon Postacquisition suppression of large‐vessel BOLD signals in high‐resolution fMRI , 2002, Magnetic resonance in medicine.

[10]  Mark Jenkinson,et al.  Fast, automated, N‐dimensional phase‐unwrapping algorithm , 2003, Magnetic resonance in medicine.

[11]  Lothar R. Schad,et al.  High-resolution venography of the brain using magnetic resonance imaging , 1998, Magnetic Resonance Materials in Physics, Biology and Medicine.

[12]  X Golay,et al.  Comparison of the dependence of blood R2 and R  2* on oxygen saturation at 1.5 and 4.7 Tesla , 2003, Magnetic resonance in medicine.

[13]  S. Holland,et al.  NMR relaxation times in the human brain at 3.0 tesla , 1999, Journal of magnetic resonance imaging : JMRI.

[14]  Robert Turner,et al.  How Much Cortex Can a Vein Drain? Downstream Dilution of Activation-Related Cerebral Blood Oxygenation Changes , 2002, NeuroImage.

[15]  J. Duyn,et al.  EPI‐BOLD fMRI of human motor cortex at 1.5 T and 3.0 T: Sensitivity dependence on echo time and acquisition bandwidth , 2004, Journal of magnetic resonance imaging : JMRI.

[16]  K. Uğurbil,et al.  Experimental determination of the BOLD field strength dependence in vessels and tissue , 1997, Magnetic resonance in medicine.

[17]  D. Yablonskiy,et al.  Water proton MR properties of human blood at 1.5 Tesla: Magnetic susceptibility, T1, T2, T  *2 , and non‐Lorentzian signal behavior , 2001, Magnetic resonance in medicine.

[18]  Gary H. Glover,et al.  Comparison of fMRI activation at 3 and 1.5 T during perceptual, cognitive, and affective processing , 2003, NeuroImage.

[19]  Y. Wang,et al.  Blood oxygen saturation assessment in vivo using T2 * estimation , 1998, Magnetic resonance in medicine.

[20]  Yu-Chung N. Cheng,et al.  Susceptibility weighted imaging (SWI) , 2004, Zeitschrift fur medizinische Physik.

[21]  Essa Yacoub,et al.  Signal and noise characteristics of Hahn SE and GE BOLD fMRI at 7 T in humans , 2005, NeuroImage.

[22]  Christopher M Collins,et al.  Numerical calculations of the static magnetic field in three-dimensional multi-tissue models of the human head. , 2002, Magnetic resonance imaging.

[23]  K. Iramina,et al.  Neuronal current distribution imaging using magnetic resonance , 1999, IEEE International Magnetics Conference.

[24]  Yingbiao Xu,et al.  The role of voxel aspect ratio in determining apparent vascular phase behavior in susceptibility weighted imaging. , 2006, Magnetic resonance imaging.

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

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

[27]  Peter C M van Zijl,et al.  Theoretical and experimental investigation of the VASO contrast mechanism , 2006, Magnetic resonance in medicine.

[28]  E M Haacke,et al.  Predicting BOLD signal changes as a function of blood volume fraction and resolution , 2001, NMR in biomedicine.

[29]  D. Le Bihan,et al.  Direct and fast detection of neuronal activation in the human brain with diffusion MRI. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[30]  S. Posse,et al.  Analytical model of susceptibility‐induced MR signal dephasing: Effect of diffusion in a microvascular network , 1999, Magnetic resonance in medicine.

[31]  S. Ogawa,et al.  Oxygenation‐sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields , 1990, Magnetic resonance in medicine.

[32]  J R Reichenbach,et al.  In vivo measurement of changes in venous blood‐oxygenation with high resolution functional MRI at 0.95 Tesla by measuring changes in susceptibility and velocity , 1998, Magnetic resonance in medicine.

[33]  José P Marques,et al.  Using forward calculations of the magnetic field perturbation due to a realistic vascular model to explore the BOLD effect , 2008, NMR in biomedicine.

[34]  D. Tank,et al.  Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Ravi S. Menon,et al.  Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. , 1993, Biophysical journal.