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.

翻译：在心脏CINE成像中，运动补偿磁共振重建（MCMR）是一种通过整合帧间运动信息来处理高度欠采样采集的有效方法。本研究针对MCMR问题提出了一种新颖的解决视角，并为该领域提供了更集成、高效的解决方案。与当前最先进的MCMR方法将原始问题分解为运动估计和重建两个子优化问题不同，我们将该问题表述为具有单一优化目标的整体框架。本方法的独特之处在于：运动估计直接由最终目标——重建所驱动，而非通过经典的运动形变损失（即运动形变图像与目标图像之间的相似性度量）进行优化。通过将运动估计与重建的目标对齐，我们消除了因伪影干扰的运动估计所导致的误差传播重建缺陷。此外，我们无需施加任何正则化/平滑性损失项即可实现高质量重建与真实运动估计，从而规避了非平凡权重因子调优的难题。我们在两个数据集上评估了所提方法：1）用于回顾性研究的内部采集2D CINE数据集；2）用于前瞻性研究的公开OCMR心脏数据集。实验结果表明，即使在高达20倍的成像加速条件下，所提出的MCMR框架仍能实现无伪影的运动估计与高质量磁共振图像重建，在所有实验的定性与定量评估中均优于当前最先进的非MCMR及MCMR方法。代码已开源：https://github.com/JZPeterPan/MCMR-Recon-Driven-Motion。