Diffusion MR Imaging in Multiple Sclerosis: Technical Aspects and Challenges

Geometric distortions caused by eddy currents. A non–diffusion-weighted image (top left) and 3 diffusion-weighted images with gradients along independent directions are shown before (top row) and after (bottom row) correction by postprocessing. The cranial contents are outlined in red on the non diffusion-weighted image, and this same outline is superimposed on the other images to show the degree of distortion. The correction involves estimating and applying a shift, scaling and shearing along the phase encoding direction of each diffusion-weighted image. The ghost-artifact seen in the diffusion-weighted images is caused by poor fat suppression because of magnetic field inhomogeneity.

Diffusion-weighted MR imaging at 3T: the left column shows 2 transverse sections acquired without sensitivity encoding (SENSE), whereas the right column shows the same sections acquired with a SENSE factor R = 3. Note the much smaller geometric distortion in the SENSE images because of the shorter acquisition time.

T2-weighted images (top row) and q-space probability for zero displacement (bottom row) are shown for a healthy control (right) and a multiple sclerosis patient (left). Both T2-weighted visible lesions and the normal appearing white matter are characterized by lower probability when compared with controls. a.u., arbitrary units.

The q-space approach illustrated using simulated data. First, diffusion weighted MR imaging data are acquired changing the diffusion gradient strength (g) along each direction considered (A). Then, using the relationship q = γ × δ × g, where γ is the gyromagnetic ratio and δ is the pulse duration, the measured signal intensity, S(g), is expressed as function of q (B). The probability P(r, Δ) that a molecule ends up at position r after a time Δ is then calculated as the inverse Fourier transform of S(q), Δ being the separation between the leading edges of the pulses (C). Finally, in one approach to characterizing P, the fast and slow diffusion components are extracted after Gaussian fitting to P(r, Δ) and the peak height of the slow component used as probability for zero displacement. The slow component is thought to reflect the integrity of the myelin sheath and cell membranes.