Meeting Banner
Abstract #3855

Improved Accelerated Model-based Parameter Quantification with Total-Generalized-Variation Regularization.

Oliver Maier1, Matthias Schloegl1, Andreas Lesch1, Andreas Petrovic1, Martin Holler2, Kristian Bredies2, Thomas Pock3, and Rudolf Stollberger1

1Institute of Medical Engineering, Graz University of Technology, Graz, Austria, 2Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria, 3Insitute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria

Incorporating TGV-regularization to accelerated model-based parameter-quantification can lead to improved image quality, however, adds non-differentiability to the problem which poses a problem for commonly used first order optimization methods like non-linear Conjugate-Gradient. The proposed method overcomes this limitation by handling the problem within a Gauss-Newton-framework and applying a Primal-Dual-algorithm to solve the inner TGV-regularized problem. Numerical simulations exhibit high agreement to references for four different parameter mapping problems up to 18-fold acceleration. In-vivo results for T1-VFA and T2-MESE models strengthen these findings. The proposed method offers huge acceleration potential for model-based parameter-quantification with similar quantification quality as fully sampled data.

This abstract and the presentation materials are available to members only; a login is required.

Join Here