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.

Related papers

• Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge [56.28872161153236]
  Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis.<n>The efficacy of these models is highly dependent on the availability of high-quality, artifact-free images.<n>To promote research in this domain, we organized the MICCAI CMRxMotion challenge.
  arXiv  Detail & Related papers  (2025-07-25T11:12:21Z)
• ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer [58.49950218437718]
  We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech. The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture. To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-03-27T16:39:40Z)
• Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation [31.431342571006123]
  Motion correction (MoCo) in radial MRI is a challenging problem due to the unpredictability of subject's motion. We propose Moner, an unsupervised MoCo method that jointly solves artifact-free MR images and accurate motion from rigid motion-corrupted k-space data.
  arXiv  Detail & Related papers  (2024-09-25T13:27:29Z)
• Simultaneous Map and Object Reconstruction [66.66729715211642]
  We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR. We take inspiration from recent novel view synthesis methods and pose the reconstruction problem as a global optimization. By careful modeling of continuous-time motion, our reconstructions can compensate for the rolling shutter effects of rotating LiDAR sensors.
  arXiv  Detail & Related papers  (2024-06-19T23:53:31Z)
• Attention-aware non-rigid image registration for accelerated MR imaging [10.47044784972188]
  We introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We extract local visual representations to build similarity maps between the registered image pairs at multiple resolution levels. We demonstrate that our model derives reliable and consistent motion fields across different sampling trajectories.
  arXiv  Detail & Related papers  (2024-04-26T14:25:07Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction [31.404228406642194]
  The key to dynamic or multi-contrast magnetic resonance imaging (MRI) reconstruction lies in exploring inter-frame or inter-contrast information. We propose a two-stage MRI reconstruction pipeline to address these limitations. Our proposed method significantly outperforms previous state-of-the-art accelerated MRI reconstruction methods.
  arXiv  Detail & Related papers  (2023-09-25T02:51:00Z)
• Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction [54.19448988321891]
  We propose an end-to-end deep learning framework that utilizes T1-weighted images (T1WIs) as auxiliary modalities to expedite T2WIs' acquisitions. We employ Optimal Transport (OT) to synthesize T2WIs by aligning T1WIs and performing cross-modal synthesis. We prove that the reconstructed T2WIs and the synthetic T2WIs become closer on the T2 image manifold with iterations increasing.
  arXiv  Detail & Related papers  (2023-05-04T12:20:51Z)
• Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging [8.095696087978977]
  Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable potential. We propose a learning-based self-supervised framework for MCMR to efficiently deal with non-rigid motion corruption in cardiac MR imaging.
  arXiv  Detail & Related papers  (2022-09-08T09:34:12Z)
• Towards performant and reliable undersampled MR reconstruction via diffusion model sampling [67.73698021297022]
  DiffuseRecon is a novel diffusion model-based MR reconstruction method. It guides the generation process based on the observed signals. It does not require additional training on specific acceleration factors.
  arXiv  Detail & Related papers  (2022-03-08T02:25:38Z)
• Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data [3.690756997172894]
  Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain. Current motion correction approaches for fetal fMRI choose a single 3D volume from a specific acquisition timepoint. We propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction.
  arXiv  Detail & Related papers  (2022-02-11T19:11:16Z)
• ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer [60.27951773998535]
  We propose a recurrent transformer model, namely textbfReconFormer, for MRI reconstruction. It can iteratively reconstruct high fertility magnetic resonance images from highly under-sampled k-space data. We show that it achieves significant improvements over the state-of-the-art methods with better parameter efficiency.
  arXiv  Detail & Related papers  (2022-01-23T21:58:19Z)
• Video Face Super-Resolution with Motion-Adaptive Feedback Cell [90.73821618795512]
  Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN) In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but effective block, which can efficiently capture the motion compensation and feed it back to the network in an adaptive way.
  arXiv  Detail & Related papers  (2020-02-15T13:14:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.