Summary: Diffusion-tensor MR imaging of the brain is an objective method that can measure diffusion of water in tissue noninvasively. Five adult volunteers participated in this study that was performed to evaluate the potential of gradient-and spin-echo readout for diffusion-tensor imaging by comparing it with single-shot spin-echo echo-planar imaging. Gradient-and spin-echo readout provides comparable measures of water diffusion to single-shot spinecho echo-planar readout with significantly less geometrical distortion at the expense of a longer imaging time.Diffusion-tensor MR imaging of the brain has been proposed as a noninvasive technique that provides microstructural and physiological information regarding brain tissue in vivo by measuring the diffusion of water (1, 2). Most current applications of diffusion-tensor MR imaging implement ultrafast single-shot echo-planar readouts, which effectively freeze physiological effects (3-5) despite severe geometrical distortions caused by local magnetic field inhomogeneity. Gradient-and spinecho (GRASE) MR imaging has less distortion compared with single-shot echo-planar imaging because the multiple refocusing RF pulses implemented in GRASE MR imaging are responsible for reducing errors originating from static field inhomogeneity (6). For this technical note, we compared the signal-to-noise ratio (SNR), isotropic apparent diffusion coefficient (ADCi), fractional anisotropy (FA), and geometrical distortions from diffusion-tensor MR imaging by use of GRASE and echo-planar readout techniques. Description of the Technique MR Imaging and Data ProcessingMR imaging studies were performed on a 1.5-T MR system (ACS-NT; Philips Medical Systems, Best, The Netherlands) using a quadrature head coil operating in receive mode. The ᭧ American Society of Neuroradiology whole-brain MR imaging protocol included axial T1-weighted spin-echo 517/14/2 (TR/TE/excitations) imaging and axial diffusion-tensor imaging using peripheral gating. The diffusion tensor MR imaging was performed using single-shot spin-echo echo-planar and GRASE readouts. Diffusion sensitization was applied sequentially in six different non-colinear directions (G xx , G yy , G zz , G xy , G xz , G yz ). The parameters for single-shot spin-echo echo-planar diffusion-tensor imaging were 3429-5538/96/4 (TR/effective TE/excitations), with a b value of 600 s·mm Ϫ2 . The parameters for GRASE diffusion-tensor imaging with four spin echoes each comprising five gradient echoes were 4000-4615/119 (TR/TE eff ), with a b value of 600 s·mm Ϫ2 . To eliminate phase errors that originated during the diffusion preparation period, a dephasing gradient and a 90-degree RF pulse before the GRASE readout were used (7). The T1-weighted spin-echo sequence and both the diffusiontensor sequences were matched for total brain coverage (number of sections, 18; section thickness, 5 mm; intersection gap, 1 mm; field of view, 23 cm). The GRASE experiment was performed twice, once with four excitations (4-GRASE) and once with 10 excitations (10-GRASE)....

No abstract

Patient motion and image distortion induced by eddy currents cause artifacts in maps of diffusion parameters computed from diffusion-weighted (DW) images. A novel and comprehensive approach to correct for spatial misalignment of DW imaging (DWI) volumes acquired with different strengths and orientations of the diffusion sensitizing gradients is presented. This approach uses a mutual information-based registration technique and a spatial transformation model containing parameters that correct for eddy current-induced image distortion and rigid body motion in three dimensions. All parameters are optimized simultaneously for an accurate and fast solution to the registration problem. The images can also be registered to a normalized template with a single interpolation step without additional computational cost. Following registration, the signal amplitude of each DWI volume is corrected to account for size variations of the object produced by the distortion correction, and the b-matrices are properly recalculated to account for any rotation applied during registration. Both qualitative and quantitative results show that this approach produces a significant improvement of diffusion tensor imaging (DTI) data acquired in the human brain. MRI measurements of water diffusion provide important information about compositional, structural, and organizational features of biological tissues. Most clinical MRI diffusion studies are performed by acquiring single-shot echo-planar images (EPI) with diffusion sensitizing gradients of different strengths and orientations. In diffusion tensor imaging (DTI) (1), at least seven images must be acquired: one image with no diffusion sensitization, and six diffusion-weighted (DW) images with diffusion sensitization in noncollinear directions. DTI analyses, in addition to other approaches used to extract diffusion information from MRI data (2-5), require the different DW images to be spatially coregistered.Unfortunately, eddy current-induced image distortions and patient motion during prolonged acquisitions cause misalignment of the DW images. Eddy currents are significantly reduced, but not eliminated, by actively shielded gradients (6) and preemphasis correction schemes (7) in modern magnets. The methods that have been proposed to reduce residual eddy current-induced distortions in DW images are based on either field maps or images.In a field map-based correction scheme, such as that presented by Jezzard et al. (8), one measures the magnetic field produced by the eddy currents and then corrects the distortion using the field map and theoretical models of how field inhomogeneities distort the images. The major obstacle to implementation is the difficulty of rapidly acquiring reliable field maps.In an image-based registration scheme, one uses a cost function Q to measure how well the images are spatially aligned. First, a target image is chosen as a reference for all other images in the data set (source images). Because it is usually less distorted and has a higher signal-to-noise ratio (...

Magnetic resonance diffusion imaging is potentially an important tool for the noninvasive characterization of normal and pathological tissue. The technique, however, is prone to a number of artifacts that can severely affect its ability to provide clinically useful information. In this study, the problem of eddy current-induced geometric distortions that occur in diffusion images acquired with echo planar sequences was addressed. These geometric distortions produce artifacts in computed maps of diffusion parameters and are caused by misalignments in the individual diffusion-weighted images that comprise the diffusion data set. A new approach is presented to characterize and calibrate the eddy current effects, enabling the eddy current distortions to be corrected in sets of interleaved (or snapshot) echo planar diffusion images. Correction is achieved by acquiring one-dimensional field maps in the read and phase encode direction for each slice and each diffusion step. The method is then demonstrated through the correction of distortions in diffusion images of the human brain. It is shown that by using the eddy current correction scheme outlined, the eddy current-induced artifacts in the diffusion-weighted images are almost completely eliminated. In addition, there is a significant improvement in the quality of the resulting diffusion tensor maps.