Meeting Banner
Abstract #2541

Vessel Adapted Regularization for Iterative Reconstruction in MR Angiography

Jana Hutter1, 2, Robert Grimm1, Christoph Forman1, 2, Joachim Hornegger1, 2, Peter Schmitt3

1Pattern recognition lab, Universitt Erlangen-Nrnberg, Erlangen, Germany; 2Erlangen graduate school in avanced optical technologies, Erlangen, Germany; 3MR Application & Workflow Development, Siemens AG, Healthcare Sector, Erlangen, Germany

Iterative methods such as Compressed Sensing involve regularization terms to stabilize and accelerate the optimization. Smoothness and sparsity assumptions are widely used as regularization, but the special structure of angiographic data is not yet part of the regularization. Our new method takes the localization and brightness of vessels, adapted to each individual vessel, into account. The information is extracted using an ellipsoid-based segmentation approach and included into the reconstruction with a modified Gaussian penalty map. This approach proved to be beneficial for the reconstruction time as well as for the general image quality.