Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimation

TL;DR

This paper introduces a unified, reconstruction-driven approach to motion-compensated MR CINE reconstruction that improves image quality and motion estimation accuracy, especially under high acceleration factors, by integrating motion estimation into a single optimization.

Contribution

The paper presents a novel single-optimization framework for MCMR that directly links motion estimation to reconstruction, eliminating the need for separate sub-optimizations and regularization terms.

Findings

Outperforms state-of-the-art methods in artifact reduction and image quality.

Effective at accelerations up to 20x with high-quality motion estimation.

Achieves superior qualitative and quantitative results across datasets.

Abstract

In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames. In this work, we propose a novel perspective for addressing the MCMR problem and a more integrated and efficient solution to the MCMR field. Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i.e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization. Our approach is unique in that the motion estimation is directly driven by the ultimate goal, reconstruction, but not by the canonical motion-warping loss (similarity measurement between motion-warped images and target images). We align the objectives of motion estimation and reconstruction, eliminating the drawbacks of artifacts-affected motion estimation and therefore error-propagated reconstruction. Further, we can deliver high-quality reconstruction and realistic motion without applying any regularization/smoothness loss terms, circumventing the non-trivial weighting factor tuning. We evaluate our method on two datasets: 1) an in-house acquired 2D CINE dataset for the retrospective study and 2) the public OCMR cardiac dataset for the prospective study. The conducted experiments indicate that the proposed MCMR framework can deliver artifact-free motion estimation and high-quality MR images even for imaging accelerations up to 20x, outperforming SOTA non-MCMR and MCMR methods in both qualitative and quantitative evaluation across all experiments. The code is available at https://github.com/JZPeterPan/MCMR-Recon-Driven-Motion.