Human brain diffusion-weighted MRI, fiber tractography and validation

Diffusion-weighted MRI acquisition
Magnetic Resonance Imaging diffusion-weighted data (DWI) were collected at Stanford’s Center for Cognitive and Neurobiological Imaging (http://cni.stanford.edu). We collected data in five males, age 37 - 39 using a 3T General Electric Discovery 750 (General Electric Healthcare, Milwaukee, WI) equipped with a 32-channel head coil (Nova Medical, Wilmington, MA). Data collection procedures were approved by the Stanford University Institutional Review Board.

For each subject we acquired two diffusion weighted scans within a single scan session. Water diffusion was measured at 96 different directions across the surface of a sphere as determined by the electro-static repulsion algorithm of Jones, Horsfield, & Simmons (1999). In all subjects, data were acquired at 1.5 mm3 spatial resolution and diffusion gradient strength was set to 2000 s/mm2 (TE 96.8 msec). We used a dual-spin echo diffusion-weighted sequences with full head coverage. Individual data sets were acquired with using two excitations (nex = 2) that were averaged in k-space. We obtained 10 non-diffusion weighted (b=0) images at the beginning of each data set. The signal-to-noise-ratio calculated over repeats of the non-diffusion images was greater than 20 in all data sets. For the subject used as example in the figures we also acquired two data sets with 150 directions at 2 mm3 spatial resolution and b values of 1000, 2000 and 4000 s/mm2 (TE 83.1/93.6/106.9 msec).

MRI images were corrected for spatial distortions due to B0 field inhomogeneity. To do so we measured the B0 magnetic field maps. Field maps were collected in the same slices as the functional data using a 16-shot, gradient-echo spiral-trajectory pulse sequence. Two volumes were successively acquired one with TE set to 9.091 ms and the other with TE increased by 2.272 ms, and the phase difference between the volumes was used as an estimate of the magnetic field. To track slow drifts in the magnetic field (e.g. due to gradient heating), field maps were collected before, after and between the two diffusion scans.

Subjects’ motion was corrected using a rigid body alignment algorithm (SPM). Diffusion-gradients were adjusted to account for the rotation applied to the measurements during motion correction. The dual-spin echo sequence we used does not require eddy current correction because it has a relatively long delay between the RF excitation pulse and image acquisition. This allows for sufficient time for the eddy currents to dephase. Pre-processing are publicly available as part of the vistasoft software distribution (Dougherty, Ben-Shachar, Bammer, Brewer, & Wandell, 2005; http://github.com/vistalab/vistasoft/mrDiffusion).

Anatomical MRI acquisition and tissue segmentation
The white- and gray-matter border was defined using a 0.7 mm3 T1-weighted FSPGR image. White/gray matter tissue contrast was increased by averaging two T1 measurements acquired in the same scan session. An initial segmentation was performed using an automated procedure (Freesurfer) and refined manually (http://www.itksnap.org/pmwiki/pmwiki.php).