Abstract #2615

Quantitative Evaluation of 3D Variational Regularized Reconstruction of Undersampled Diffusion Tensor Imaging

Florian Knoll¹, Rafael O'Halloran², Kristian Bredies³, Rudolf Stollberger¹, Roland Bammer²

¹Institute of Medical Engineering, Graz University of Technology, Graz, Austria; ²Radiology, Stanford University, Palo Alto, CA, United States; ³Department of Mathematics and Scientific Computing, University of Graz, Graz, Styria, Austria

Diffusion Tensor Imaging is a demanding application requiring the acquisition of many image volumes to extract the desired tensor parameters. k-space undersampling is a straightforward method that can be used to reduce the total scan time, however, if the undersampled data is reconstructed with conventional methods such as gridding, artifacts result. Parallel imaging and compressed sensing are successful in reducing undersampling but it is not clear what effect nonlinear regularization terms have with respect to quantitative evaluation of the images, as performed in Diffusion Tensor Imaging. Here the quantitative accuracy of a 3D spiral acquisition using nonlinear regularization is evaluated in a simulated atlas-based DTI phantom.