Brain Diffusion Tensor MR Imaging

The development of MR imaging techniques during the past decade has enabled researchers to use MR imaging as a noninvasive tool for evaluating structural and physiologic states in biologic tissues by measuring the diffusion process of water molecules. More recently, diffusion tensor MR imaging (DTI) technique based on the dependency of molecular diffusion on the orientation of white matter fiber tracts has been used to analyze the trajectory, shape, fiber structure, location, topology and connectivity of neuronal fiber pathways in living humans. Numerous efforts have been made by MR physicists, brain scientists, and medical doctors to advance MR techniques and computer-based algorithms which result in more accurate quantification of diffusion tensor and the generation of white matter fiber tract maps and to determine the pathophysiology of brain disease by DTI and useful clinical applications of DTI. In this article, we describe the tensor theory used to characterize molecular diffusion in white matter and a process of measuring tensor elements using diffusion-sensitive MR images to fiber mapping. We then provide review of current literature and some clinical examples that have been published and are on-going.

SENSE (Sensitivity Encoding) for Diffusion Tensor Imaging of the Brain

PURPOSE: The sensitivity encoding (SENSE) technique is increasingly being used with clinical MRI scanners. The object of this study is to compare the normative human data and image quality of the diffusion tensor imaging (DTI) with sensitivity encoding (SENSE) and standard single-shot EPI techniques. MATERIALS AND METHODS: 16 normal volunteers underwent single-shot echo-planar DTI with both standard and SENSE sequences using a 1.5 T Philips Intera MR scanner (TR/TE=6755/74 or 5871/66 ms, echo train length 127 or 67, NEX=3, matrix=128x128, FOV=220x220 mm, slice thickness=4 mm, b value=600 s/mm2, six orthogonal diffusion gradients). The diffusion tensor-encoded MR images were transferred to a PC workstation and analyzed using in-house software. The fractional anisotropy (FA) and apparent diffusion coefficient (ADC) maps were calculated. The presence of artifacts (ghost susceptibility, eddy current) was graded with a two- or three-point scale. The ADC and FA values were measured in the major white matter tract and gray matter nuclei. The signal-to-noise ratio was also measured. Fisher's exact test and the Mann-Whitney test were used for the statistical analysis. RESULTS: With SENSE, the acquisition time was reduced from 2 min 57 sec to 1 min 22 sec for DTI. Susceptibility artifacts (around the brain stem and temporal base) and eddy current artifacts were significantly reduced on the SENSE DTI as compared with those on the standard DTI (p<0.05). No ghost artifacts were observed on the SENSE DTI, whereas such artifacts were observed in 14 cases (87.5%) on the standard DTI. The ADC value was not significantly different between the SENSE DTI and the standard DTI, whereas the FA values in the cerebral cortex and white matter were significantly higher on the SENSE DTI than on the standard DTI (p<0.05). The signal-to-noise ratio was 8.44 on the standard DTI and 11.40 on the standard DTI. CONCLUSION: The use of SENSE DTI significantly reduces the geometric distortion caused by artifacts, shortens the acquisition time, and allows a relatively high SNR to be maintained, but tends to erroneously increase the FA value of the tissue. Therefore, DTI with SENSE may provide better white matter fiber tracking and diffusivity indices when the imaging parameters for SENSE are optimized.