Iterative Parallel Imaging Reconstruction of Time-Resolved Data using 3D Variational Regularization

Florian Knoll¹, Kristian Bredies², Rudolf Stollberger¹

Constrained iterative image reconstruction of undersampled data from multiple coils has shown its high potential to deliver images with excellent image quality from highly accelerated measurements. To eliminate aliasing artifacts, regularization methods are facilitated, which introduce a-priori knowledge about the structure of the desired solution. Usually, regularization is applied only in 2D, and data is reconstructed slice by slice. While this approach reduces the size of the problem and therefore the amount of memory that is needed in the computation, it neglects the potential of introducing a-priori information in the third dimension. This work introduces an approach which treats a whole 3D volume of images as a single data set, and also includes 3D regularization. Results are presented for undersampled spiral angiography data from the 2010 ISMRM image reconstruction challenge.