Meeting Banner
Abstract #0209

A Robust Reconstruction Method for Quantitative Perfusion MRI: Application to Brain Dynamic Susceptibility Contrast (DSC) Imaging

Cagdas Ulas1,2, Pedro A. Gomez1,2, Jonathan I. Sperl2, Christine Preibisch3, Marion I. Menzel2, Axel Haase4, and Bjoern H. Menze1

1Department of Computer Science, Technische Universität München, Munich, Germany, 2GE Global Research, Munich, Germany, 3Department of Neuroradiology, Technische Universität München, Munich, Germany, 4Zentralinstitut für Medizintechnik, Technische Universität München, Munich, Germany

We propose a robust reconstruction model for dynamic perfusion magnetic resonance imaging (MRI) from undersampled k-space data. Our method is based on a joint penalization of the pixel-wise incoherence on temporal differences and patch-wise dissimilarities between spatio-temporal neighborhoods of perfusion image series. We evaluate our method on dynamic susceptibility contrast (DSC)–MRI brain perfusion datasets and demonstrate that the proposed reconstruction model can achieve up to 8-fold acceleration by yielding improved spatial reconstructions and providing highly accurate matching of perfusion time-intensity curves, thus leading to more precise quantification of clinically relevant perfusion parameters over two existing reconstruction methods.

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

Join Here