Partial fourier partially parallel imaging

Magnetic resonance image reconstruction using equation system solving-based singularity function analysis

This paper presents a method of reconstructing magnetic resonance (MR) images from partial k-space using a so-called singularity function model. To estimate the parameters of the model, we propose a new technique based on equation system solving. The proposed reconstruction method is evaluated on both simulated and real brain MR data. It is also compared with existing techniques. The results show that this new strategy exhibits a great simplicity while offering a good reconstruction quality.

Optimization of acquisition parameters of diffusion-tensor magnetic resonance imaging in the spinal cord

OBJECTIVE

The purpose of this study was to optimize imaging parameters for diffusion tensor imaging (DTI) of the cervical spinal cord using a recently developed sensitivity-encoded (SENSE) imaging technique, which can substantially reduce susceptibility artifacts.

MATERIALS AND METHODS

One hundred twenty sets of DTIs were performed of the cervical spinal cord in 40 normal volunteers, using a SENSE-based echo-planar imaging technique with different parameters (b-values, numbers of diffusion gradient directions, number of excitations, and slice thickness) in a stepwise approach. In step 1, DTI was performed of the cervical spinal cord with different b-values 500, 700, 900 seconds/mm; then with different numbers of diffusion gradient directions 6, 15, 32 in step 2; different number of excitations 1, 3, 5 in step 3; and different slice thicknesses 2, 3, 4 mm in step 4. In each step, 30 sets of DTIs were obtained from 10 volunteers. To determine the optimal imaging parameters, 3 radiologists evaluated the qualities of fractional anisotropy (FA) maps and color FA maps by visual analysis. The number of reconstructed fibers was measured for quantitative analysis. All qualitative and quantitative comparisons were analyzed by statistical methods using the Friedmann test and the Wilcoxon signed rank test.

RESULTS

In step 1, DTIs using a b-value of 900 seconds/mm showed the highest number of reconstructed fibers and the best image quality of FA map and color map. In step 2, the use of 15 or 32 directions demonstrated better quality DTIs than 6 directions. No significant difference was evident between the quality of DTI with 15 directions and that with 32 directions. The scan time of DTI with 15 directions was shorter than with 32 directions. In step 3, as the number of excitations increased, the number of reconstructed fibers increased significantly and the image quality of the FA map and the color map improved significantly. In step 4, the numbers of reconstructed fibers were significantly the highest with a slice thickness of 4 mm.

CONCLUSION

Optimal parameters for DTI in the cervical spinal cord included a b-value of 900 seconds/mm, 15 diffusion gradient directions, 5 excitations, and a slice thickness of 4mm.

MR image reconstruction of sparsely sampled 3D k-space data by projection-onto-convex sets

In many rapid three-dimensional (3D) magnetic resonance (MR) imaging applications, such as when following a contrast bolus in the vasculature using a moving table technique, the desired k-space data cannot be fully acquired due to scan time limitations. One solution to this problem is to sparsely sample the data space. Typically, the central zone of k-space is fully sampled, but the peripheral zone is partially sampled. We have experimentally evaluated the application of the projection-onto-convex sets (POCS) and zero-filling (ZF) algorithms for the reconstruction of sparsely sampled 3D k-space data. Both a subjective assessment (by direct image visualization) and an objective analysis [using standard image quality parameters such as global and local performance error and signal-to-noise ratio (SNR)] were employed. Compared to ZF, the POCS algorithm was found to be a powerful and robust method for reconstructing images from sparsely sampled 3D k-space data, a practical strategy for greatly reducing scan time. The POCS algorithm reconstructed a faithful representation of the true image and improved image quality with regard to global and local performance error, with respect to the ZF images. SNR, however, was superior to ZF only when more than 20% of the data were sparsely sampled. POCS-based methods show potential for reconstructing fast 3D MR images obtained by sparse sampling.