Diffusion MR imaging in multiple sclerosis: technical aspects and challenges

Abstract

Diffusion tensor (DT) MR imaging has frequently been applied in multiple sclerosis (MS) because of its ability to detect and quantify disease-related changes of the tissue microstructure within and outside T2-visible lesions. DT MR imaging data collection places high demands on scanner hardware and, though the acquisition and postprocessing can be relatively straightforward, numerous challenges remain in improving the reproducibility of this technique. Although there are some issues concerning image quality, echo-planar imaging is the most widely used acquisition scheme for diffusion imaging studies. Once the DT is estimated, indexes conveying the size, shape, and orientation of the DT can be calculated and further analyzed by using either histogram- or region-of-interest-based analyses. Because the orientation of the DT reflects the orientation of the axonal fibers of the brain, the pathways of the major white matter tracts can also be visualized. The DT model of diffusion, however, is not sufficient to characterize the diffusion properties of the brain when complex populations of fibers are present in a single voxel, and new ways to address this issue have been proposed. Two developments have enabled considerable improvements in the application of DT MR imaging: high magnetic field strengths and multicoil receiver arrays with parallel imaging. This review critically discusses models, acquisition, and postprocessing approaches that are currently available for DT MR imaging, as well as their limitations and possible improvements, to provide a better understanding of the strengths and weaknesses of this technique and a background for designing diffusion studies in MS.

Fig 1.

Geometric distortions inherent in echo-planar imaging (EPI), evident from the comparison between a fast spin-echo (FSE) T2-weighted image (left) and the b = 0 image of a pulsed gradient SE-EPI experiment, after rigid (middle) and nonlinear transformation (right) to match the anatomy of the FSE T2-weighted image. The cranial contents are outlined in red on the FSE image, and the same outline is superimposed on the other images to show the degree of distortion.

Fig 2.

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.

Fig 3.

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.

Fig 4.

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.

Fig 5.

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.

Fig 6.

Q-ball image (axial orientation, inset) with an enlargement of the area shown. The glyph at each voxel depicts the local diffusion orientation distribution function (ODF), which can resolve multiple intravoxel diffusion orientations. There is an intersection between the left-right fibers (shown in red) and the anteroposterior fibers (green). Superior-inferior fibers are shown in blue. Image kindly provided by Dr. David Tuch.