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Object: To develop an efficient, automated method to correct through-slice signal loss in gradient-echo EPI at 3T., Material and Methods: The optimal choice of two z-shim values for signal recovery was determined from simulations and experiments. The specific required z-shim values are determined using a rapid calibration method that combines information about the slice profile with a sparse set of measurements. The proposed correction method was implemented for a language fMRI study which suffers from signal loss near the auditory canals, and tested on 12 volunteers., Results: Using a square root sum of squares combination of two z-shim values full signal restoration (to within 2% of the correct value) was achieved in 96% of all correctable brain pixels for 3 mm slices, and partial correction in pixels outside this range. In all subjects, language processing activation was recovered in the inferior and lateral areas of the left temporal lobe which was not detectable with conventional fMRI., Conclusion: The careful choice of two z-shim values by the proposed method achieves through-slice signal loss correction for the majority of pixels in the brain for 3 mm slices at 3T.

Cardiac pulsatility causes a nonrigid motion of the brain. In multi-shot diffusion imaging this leads to spatially varying phase changes that must be corrected. A conjugate gradient based reconstruction is presented that includes phase changes measured using two-dimensional navigator echoes, coil sensitivity information, navigator-determined weightings, and data from multiple coils and averages.A multi-shot echo planar sequence was used to image brain regions where pulsatile motion is not uniform. Reduced susceptibility artifacts were observed compared to a clinical single-shot sequence. In a higher slice, fiber directions derived from single-shot data show distortions from anatomical scans by as much as 7 mm compared to less than 2 mm for our multi-shot reconstructions. The reduced distortions imply that phase encoding can be applied in the shorter left-right direction, enabling time savings through the use of a rectangular field of view. Higher resolution diffusion imaging in the spine permits visualization of a nerve root., ((c) 2006 Wiley-Liss, Inc.)

Methods utilizing Padé approximants are investigated for implementation with magnetic resonance imaging data and are presented both for direct image reconstruction and for feature extraction. Padé approximants are a numerical tool that can be used to accelerate the convergence of a slowly converging sequence by estimating the fully converged sequence values from early data points. Padé approximants can be calculated directly from k-space data by solving a set of linear matrix equations to produce signal values for any desired location in the image domain. This gives an estimate of the fully converged signal intensity at each pixel location in the image, raising the possibility of reconstructing a better estimate of the object from a reduced data set. These methods have been tested on phantom and human data both for image reconstruction and for feature extraction. In image reconstruction, considerable convergence acceleration can be achieved, with steep intensity boundaries reproduced in keeping with higher resolution reconstructions and oscillatory truncation artifact characteristic of Fourier reconstruction removed. The convergence acceleration is variable and there is the possibility of fine detail suppression when insufficient data are included. The use of Padé methods as a tool for feature extraction has shown good agreement with extraction from high-resolution reference data. In this approach the edge information comes intrinsically from Padé reconstruction.