The B‐matrix must be rotated when correcting for subject motion in DTI data

To estimate diffusion tensor MRI (DTI) measures, such as fractional anisotropy and fiber orientation, reliably, a large number of diffusion‐encoded images is needed, preferably cardiac gated to reduce pulsation artifacts. However, the concomitant longer acquisition times increase the chances of subject motion adversely affecting the estimation of these measures. While correcting for motion artifacts improves the accuracy of DTI, an often overlooked step in realigning the images is to reorient the B‐matrix so that orientational information is correctly preserved. To the best of our knowledge, most research groups and software packages currently omit this reorientation step. Given the recent explosion of DTI applications including, for example, neurosurgical planning (in which errors can have drastic consequences), it is important to investigate the impact of neglecting to perform the B‐matrix reorientation. In this work, a systematic study to investigate the effect of neglecting to reorient the B‐matrix on DTI data during motion correction is presented. The consequences for diffusion fiber tractography are also discussed. Magn Reson Med, 61:1336–1349, 2009. © 2009 Wiley‐Liss, Inc.

[1]  Won-Jin Moon,et al.  How does distortion correction correlate with anisotropic indices? A diffusion tensor imaging study. , 2006, Magnetic resonance imaging.

[2]  A. Anderson Theoretical analysis of the effects of noise on diffusion tensor imaging , 2001, Magnetic resonance in medicine.

[3]  Derek K. Jones Tractography Gone Wild: Probabilistic Fibre Tracking Using the Wild Bootstrap With Diffusion Tensor MRI , 2008, IEEE Transactions on Medical Imaging.

[4]  Manabu Kinoshita,et al.  Fiber-tracking does not accurately estimate size of fiber bundle in pathological condition: initial neurosurgical experience using neuronavigation and subcortical white matter stimulation , 2005, NeuroImage.

[5]  S. Skare,et al.  Noise considerations in the determination of diffusion tensor anisotropy. , 2000, Magnetic resonance imaging.

[6]  J. Dubois,et al.  Optimized diffusion gradient orientation schemes for corrupted clinical DTI data sets , 2006, Magnetic Resonance Materials in Physics, Biology and Medicine.

[7]  Klaas Nicolay,et al.  Reproducibility of Quantitative Cerebral T2 Relaxometry, Diffusion Tensor Imaging, and 1H Magnetic Resonance Spectroscopy at 3.0 Tesla , 2007, Investigative radiology.

[8]  N. Papadakis,et al.  Minimal gradient encoding for robust estimation of diffusion anisotropy. , 2000, Magnetic resonance imaging.

[9]  Usha Sinha,et al.  Geometric distortion correction of high‐resolution 3 T diffusion tensor brain images , 2005, Magnetic resonance in medicine.

[10]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[11]  Ashok Panigrahy,et al.  Affine and polynomial mutual information coregistration for artifact elimination in diffusion tensor imaging of newborns. , 2004, Magnetic resonance imaging.

[12]  S Skare,et al.  Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI. , 2000, Journal of magnetic resonance.

[13]  N G Papadakis,et al.  Gradient preemphasis calibration in diffusion‐weighted echo‐planar imaging , 2000, Magnetic resonance in medicine.

[14]  Derek K. Jones,et al.  The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study † , 2004, Magnetic resonance in medicine.

[15]  N J Pelc,et al.  Analysis and generalized correction of the effect of spatial gradient field distortions in diffusion‐weighted imaging , 2003, Magnetic resonance in medicine.

[16]  A. Connelly,et al.  Anisotropic noise propagation in diffusion tensor MRI sampling schemes , 2003, Magnetic resonance in medicine.

[17]  N G Papadakis,et al.  A comparative study of acquisition schemes for diffusion tensor imaging using MRI. , 1999, Journal of magnetic resonance.

[18]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[19]  Murat Aksoy,et al.  Single‐step nonlinear diffusion tensor estimation in the presence of microscopic and macroscopic motion , 2008, Magnetic resonance in medicine.

[20]  Mark E Bastin,et al.  Effects of random subject rotation on optimised diffusion gradient sampling schemes in diffusion tensor MRI. , 2008, Magnetic resonance imaging.

[21]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[22]  Stefan Klöppel,et al.  The cortical motor threshold reflects microstructural properties of cerebral white matter , 2008, NeuroImage.

[23]  P. Basser,et al.  Comprehensive approach for correction of motion and distortion in diffusion‐weighted MRI , 2004, Magnetic resonance in medicine.

[24]  M. Horsfield,et al.  Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[25]  Jerry L. Prince,et al.  Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T , 2007, NeuroImage.

[26]  M E Bastin,et al.  On the use of water phantom images to calibrate and correct eddy current induced artefacts in MR diffusion tensor imaging. , 2000, Magnetic resonance imaging.

[27]  Bennett A Landman,et al.  Diffusion tensor imaging at low SNR: nonmonotonic behaviors of tensor contrasts. , 2008, Magnetic resonance imaging.

[28]  Chris A Clark,et al.  White matter fiber tracking in patients with space-occupying lesions of the brain: a new technique for neurosurgical planning? , 2003, NeuroImage.

[29]  Peter J. Basser,et al.  A normal distribution for tensor-valued random variables: applications to diffusion tensor MRI , 2003, IEEE Transactions on Medical Imaging.

[30]  C. Pierpaoli,et al.  Characterization of and correction for eddy current artifacts in echo planar diffusion imaging , 1998, Magnetic resonance in medicine.

[31]  Nathan Intrator,et al.  Variational multiple-tensor fitting of fiber-ambiguous diffusion-weighted magnetic resonance imaging voxels. , 2008, Magnetic resonance imaging.

[32]  Isabelle Bloch,et al.  Distortion correction and robust tensor estimation for MR diffusion imaging , 2002, Medical Image Anal..

[33]  P. Basser,et al.  Parametric and non-parametric statistical analysis of DT-MRI data. , 2003, Journal of magnetic resonance.

[34]  Wen Qin,et al.  Diffusion tensor tractography in patients with cerebral tumors: a helpful technique for neurosurgical planning and postoperative assessment. , 2005, European journal of radiology.

[35]  D. Parker,et al.  Elimination of eddy current artifacts in diffusion‐weighted echo‐planar images: The use of bipolar gradients , 1997, Magnetic resonance in medicine.

[36]  Daniel C Alexander,et al.  Optimal acquisition orders of diffusion‐weighted MRI measurements , 2007, Journal of magnetic resonance imaging : JMRI.

[37]  Jerry L Prince,et al.  Effects of signal‐to‐noise ratio on the accuracy and reproducibility of diffusion tensor imaging–derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T , 2007, Journal of magnetic resonance imaging : JMRI.

[38]  Derek K. Jones,et al.  “Squashing peanuts and smashing pumpkins”: How noise distorts diffusion‐weighted MR data , 2004, Magnetic resonance in medicine.

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