Reconstruction-driven motion estimation for motion-compensated MR CINE
imaging
- URL: http://arxiv.org/abs/2302.02504v1
- Date: Sun, 5 Feb 2023 22:51:27 GMT
- Title: Reconstruction-driven motion estimation for motion-compensated MR CINE
imaging
- Authors: Jiazhen Pan, Wenqi Huang, Daniel Rueckert, Thomas K\"ustner, Kerstin
Hammernik
- Abstract summary: Motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions.
In this work, we propose a deep learning-based framework to address the MCMR problem efficiently.
Experiments on 43 in-house acquired 2D CINE datasets indicate that the proposed MCMR framework can deliver artifact-free motion estimation.
- Score: 8.317520045034412
- License: http://creativecommons.org/licenses/by/4.0/
- 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 deep learning-based
framework to address the MCMR problem efficiently. 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. We discard the canonical
motion-warping loss (similarity measurement between motion-warped images and
target images) to estimate the motion, but drive the motion estimation process
directly by the final reconstruction performance. The higher reconstruction
quality is achieved without using any smoothness loss terms and without
iterative processing between motion estimation and reconstruction. Therefore,
we avoid non-trivial loss weighting factors tuning and time-consuming iterative
processing. Experiments on 43 in-house acquired 2D CINE datasets indicate that
the proposed MCMR framework can deliver artifact-free motion estimation and
high-quality MR images even for imaging accelerations up to 20x. The proposed
framework is compared to SOTA non-MCMR and MCMR methods and outperforms these
methods qualitatively and quantitatively in all applied metrics across all
experiments with different acceleration rates.
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