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

TL;DR

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.

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.

Figures (8)

• Figure 1: The difference between the proposed MCMR framework (bottom) and the conventional MCMR work (top) is shown. The conventional approaches divide the original MCMR problem into two sub-optimization problems: motion estimation and reconstruction. Its motion estimation is optimized by minimizing the intermediate motion-warping loss (brightness similarity measurement between motion-warped images and target images) and if deep learning is used, the motion prediction back-propagation is only exerted on the motion estimation part. In contrast, we develop a deep learning-based framework that predicts the motion from the perspective of our ultimate goal: reconstruction. We discard using any intermediate motion-warping loss. The back-propagation is performed through the whole pipeline and reconstruction-driven motion estimation is established.
• Figure 2: Architecture of the proposed method: Motion-compensated MR reconstruction (MCMR) framework with a Motion Estimation Block (refer to \ref{['motion']}) and a complex-valued Motion-Compensated Reconstruction Block (refer to \ref{['recon']}). The motion estimation learning process is directly driven by the final reconstruction performance. A pre-processing reconstruction is implemented (Reconstruction Initialization, refer to \ref{['CGSENSE']}) prior to the proposed method to alleviate the reconstruction difficulty.
• Figure 3: Reconstruction error maps between reconstructed and reference image for using different neighboring frames amount $K$ on a test sample with acceleration rate $R=20$.
• Figure 4: The $y-t$ plane of a sample's reconstruction at $R=16$ in terms of $\lambda$ and the number of neighboring frames $K$. The corresponding averaged PSNR of the $y-t$ plane of all test samples is shown at the bottom. The best score is marked with blue. The red arrow points to larger residual errors.
• Figure 5: The reconstruction results at acceleration rate $R=8$ with motion estimated using $\mathcal{L}_w$, $\mathcal{L}_r$ (proposed) and motion estimated from fully-sampled ($R=1$) CMR images (reference motion). The color-wheel-encoded colorwheel motion field, reconstructed images and the corresponding error maps are shown. The red arrow points to larger residual errors.