Faculty Bibliography

Nonlinear parallel imaging reconstruction using an iterative regularized Gauss Newton method (IRGN) has shown its potential in several applications [1]. This technique determines both the coil sensitivities and the image from undersampled multi-coil data. It enables high acceleration factors without pronounced local enhancement of noise. The numerical implementation of this sophisticated method requires data normalization steps which are usually performed individually for each slice and echo. In this study it was investigated if this type of reconstruction is applicable for quantitative imaging despite the complex reconstruction including image individual normalization. For that purpose high resolution multi-echo imaging with different acceleration factors was used for the quantification of the transverse relaxation time (T2).

OBJECTIVE: Subsampling of radially encoded MRI acquisitions in combination with sparsity promoting methods opened a door to significantly increased imaging speed, which is crucial for many important clinical applications. In particular, it has been shown recently that total variation (TV) regularization efficiently reduces undersampling artifacts. The drawback of the method is the long reconstruction time which makes it impossible to use in daily clinical practice, especially if the TV optimization problem has to be solved repeatedly to select a proper regularization parameter. MATERIALS AND METHODS: The goal of this work was to show that for the case of MR Angiography, TV filtering can be performed as a post-processing step, in contrast to the common approach of integrating TV penalties in the image reconstruction process. With this approach, it is possible to use TV algorithms with data fidelity terms in image space, which can be implemented very efficiently on graphic processing units (GPUs). The combination of a special radial sampling trajectory and a full 3D formulation of the TV minimization problem is crucial for the effectiveness of the artifact elimination process. RESULTS AND CONCLUSION: The computation times of GPU-TV show that interactive elimination of undersampling artifacts is possible even for large volume data sets, in particular allowing the interactive determination of the regularization parameter. Results from phantom measurements and in vivo angiography data sets show that 3D TV, together with the proposed sampling trajectory, leads to pronounced improvements in image quality. However, while artifact removal was very efficient for angiography data sets in this work, it cannot be expected that the proposed method of TV post-processing will work for arbitrary types of scans.