A method for improving the performance of gradient systems for diffusion-weighted MRI

The MR signal is sensitive to diffusion. This effect can be increased by the use of large, balanced bipolar gradients. The gradient systems of MR scanners are calibrated at installation and during regular servicing visits. Because the measured apparent diffusion constant (ADC) depends on the square of the amplitude of the diffusion sensitizing gradients, errors in the gradient calibration are exaggerated. If the error is varying among the different gradient axes, it will affect the estimated degree of anisotropy. To assess the gradient calibration accuracy in a whole‐body MRI scanner, ADC values were calculated for a uniform water phantom along each gradient direction while monitoring the temperature. Knowledge of the temperature allows the expected diffusion constant of water to be calculated independent of the MRI measurement. It was found that the gradient axes (±x, ±y, ±z) were calibrated differently, resulting in offset ADC values. A method is presented to rescale the amplitude of each of the six principal gradient axes within the MR pulse sequence. The scaling factor is the square root of the ratio of the expected and observed diffusion constants. In addition, fiber tracking results in the human brain were noticeably affected by improving the gradient system calibration. Magn Reson Med 58:763–768, 2007. © 2007 Wiley‐Liss, Inc.

[1]  David S. Martin,et al.  Diffusion and Perfusion Magnetic Resonance Imaging: Applications to Functional MRI , 1996 .

[2]  R. Mills,et al.  Self-diffusion in normal and heavy water in the range 1-45.deg. , 1973 .

[3]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

[4]  Stefan Skare,et al.  A Model-Based Method for Retrospective Correction of Geometric Distortions in Diffusion-Weighted EPI , 2002, NeuroImage.

[5]  E. Purcell,et al.  Effects of Diffusion on Free Precession in Nuclear Magnetic Resonance Experiments , 1954 .

[6]  Huawei Zhao,et al.  Use of spherical harmonic deconvolution methods to compensate for nonlinear gradient effects on MRI images , 2004, Magnetic resonance in medicine.

[7]  Kalvis M. Jansons,et al.  Persistent angular structure: new insights from diffusion magnetic resonance imaging data , 2003 .

[8]  Daniel C. Alexander,et al.  Camino: Open-Source Diffusion-MRI Reconstruction and Processing , 2006 .

[9]  Martin O Leach,et al.  A complete distortion correction for MR images: I. Gradient warp correction , 2005, Physics in medicine and biology.

[10]  D. Alexander,et al.  Motion Correction in Diffusion Magnetic Resonance Imaging , 2005 .

[11]  V. Wedeen,et al.  Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo , 2003, Magnetic resonance in medicine.

[12]  P. Basser,et al.  The b matrix in diffusion tensor echo‐planar imaging , 1997, Magnetic resonance in medicine.

[13]  C Baldock,et al.  Test liquids for quantitative MRI measurements of self‐diffusion coefficient in vivo , 2000, Magnetic resonance in medicine.

[14]  Keith R. Matthews,et al.  Elementary Linear Algebra , 1998 .

[15]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[16]  D. Le Bihan,et al.  Artifacts and pitfalls in diffusion MRI , 2006, Journal of magnetic resonance imaging : JMRI.

[17]  P. Grenier,et al.  MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. , 1986, Radiology.

[18]  Geoffrey J M Parker,et al.  A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements , 2003, Journal of magnetic resonance imaging : JMRI.

[19]  Deming Wang,et al.  Geometric distortion in clinical MRI systems Part II: correction using a 3D phantom. , 2004, Magnetic resonance imaging.

[20]  Deming Wang,et al.  Geometric distortion in clinical MRI systems Part I: evaluation using a 3D phantom. , 2004, Magnetic resonance imaging.