The combination of parallel imaging with partial Fourier acquisition has greatly improved the performance of diffusion-weighted single-shot EPI and is the preferred method for acquisitions at low to medium magnetic field strength such as 1.5 or 3 Tesla. Increased off-resonance effects and reduced transverse relaxation times at 7 Tesla, however, generate more significant artifacts than at lower magnetic field strength and limit data acquisition. Additional acceleration of k-space traversal using a multi-shot approach, which acquires a subset of k-space data after each excitation, reduces these artifacts relative to conventional single-shot acquisitions. However, corrections for motion-induced phase errors are not straightforward in accelerated, diffusion-weighted multi-shot EPI because of phase aliasing. In this study, we introduce a simple acquisition and corresponding reconstruction method for diffusion-weighted multi-shot EPI with parallel imaging suitable for use at high field. The reconstruction uses a simple modification of the standard SENSE algorithm to account for shot-to-shot phase errors; the method is called Image Reconstruction using Image-space Sampling functions (IRIS). Using this approach, reconstruction from highly aliased in vivo image data using 2-D navigator phase information is demonstrated for human diffusion-weighted imaging studies at 7 Tesla. The final reconstructed images show submillimeter in-plane resolution with no ghosts and much reduced blurring and off-resonance artifacts.

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Image noise in diffusion tensor MRI (DT-MRI) causes errors in the measured tensor and hence variance in the estimated fiber orientation. Uncertainty in fiber orientation has been described using a circular "cone of uncertainty" (CU) around the principal eigenvector of the DT. The CU has proved to be a useful construct for quantifying and visualizing the variability of DT-MRI parameters and fiber tractography. The assumption of circularity of the CU has not been tested directly, however. In this work, bootstrap analysis and simple theoretical arguments were used to show that the CU is elliptical and multivariate normal in the vast majority of white matter (WM) voxels for typical measurement conditions. The dependence of the cone angle on the signal-to-noise ratio (SNR) and eigenvalue contrast was established. The major and minor cone axes are shown to be coincident with the second and third eigenvectors of the tensor, respectively, in the limit of many uniformly spaced diffusionencoding directions. The deviation between the major cone axis and the second eigenvector was quantified for typical sets of diffusion-weighting ( Diffusion tensor MRI (DT-MRI) provides information on water molecular diffusion based on a series of images with diffusion weighting applied in at least six noncollinear directions (1,2). Given the effective DT, it is possible to characterize tissue microstructure using the directional dependence of diffusion in vivo (3,4). In addition, this directional information has been exploited to estimate the paths of fiber bundles in the brain in order to infer axonal connectivity (5). In this case, the principal eigenvector (i.e., the estimated direction of maximum diffusivity), v 1 , is taken to be parallel to the local fiber bundle. In the presence of image noise, however, perturbations of the DT field introduce errors in the estimated diffusion anisotropy (6 -8) and fiber direction (9,10).The directional uncertainty in v 1 has been characterized by the "cone of uncertainty" (CU) (11,12). This was defined as a circular cone with axis along the expectation value of v 1 and cone angle equal to the uncertainty (i.e., confidence interval) in the orientation of v 1 . The CU is particularly useful for visualizing the uncertainty in fiber orientation and predicting error in MR fiber tractography. However, Lazar and Alexander (13) and Lazar et al. (14) showed that errors in MR tractography typically have an elliptical distribution, and noted that an elliptical CU is expected in voxels that lack axially symmetric diffusion. They measured the dispersion of fiber-tracking errors in a plane perpendicular to the fiber axis at some distance from the seed points. They noted a strong correlation between the direction of the greatest dispersion of tracking errors and the second eigenvector of the tensor (corresponding to the second-largest diffusivity). The same relationship existed between the direction of the smallest dispersion of tracking errors and the third eigenvector of the tensor (corresponding to the small...

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This study evaluated the quantitative measurements of three-dimensional (3D) volume images using multidetector row computed tomography (MDCT) and one skull specimen. Twenty-one linear distances were measured five times each by vernier caliper. A dry human skull was imaged with MDCT for various acquisition parameters at slice thicknesses of 1.25, 2.50, 3.75, and 5.00 mm for the different acquisition modes of axial and spiral scan. The distance of each corresponding item displayed on 3D rendered images was measured seven times by an uninvolved observer using a 3D software tool. Data analysis was performed to determine if there were any statistically significant differences in acquisition parameters. No significant image differences were found among the scan modes for each slice thickness. For a given scan mode, acquisition slice thickness was the important factor for quantitative measurement of 3D rendered CT images.