Unrolled and rapid motion-compensated reconstruction for cardiac CINE MRI

Abstract

In recent years Motion-Compensated MR reconstruction (MCMR) has emerged as a promising approach for cardiac MR (CMR) imaging reconstruction. MCMR estimates cardiac motion and incorporates this information in the reconstruction. However, two obstacles prevent the practical use of MCMR in clinical situations: First, inaccurate motion estimation often leads to inferior CMR reconstruction results. Second, the motion estimation frequently leads to a long processing time for the reconstruction. In this work, we propose a learning-based and unrolled MCMR framework that can perform precise and rapid CMR reconstruction. We achieve accurate reconstruction by developing a joint optimization between the motion estimation and reconstruction, in which a deep learning-based motion estimation framework is unrolled within an iterative optimization procedure. With progressive iterations, a mutually beneficial interaction can be established in which the reconstruction quality is improved with more accurate motion estimation. Further, we propose a groupwise motion estimation framework to speed up the MCMR process. A registration template based on the cardiac sequence average is introduced, while the motion estimation is conducted between the cardiac frames and the template. By applying this framework, cardiac sequence registration can be accomplished with linear time complexity. Experiments on 43 in-house acquired 2D CINE datasets indicate that the proposed unrolled MCMR framework can deliver artifacts-free motion estimation and high-quality CMR reconstruction even for imaging acceleration rates up to 20x. We compare our approach with state-of-the-art reconstruction methods and it outperforms them quantitatively and qualitatively in all adapted metrics across all acceleration rates.

Keywords: Cardiac CINE reconstruction; Deep learning; Linear complexity registration; Motion-compensated reconstruction; Unrolled optimization.