A Unified Deep Learning Framework for Motion Correction in Medical Imaging

• URL: http://arxiv.org/abs/2409.14204v3
• Date: Sat, 20 Sep 2025 00:57:50 GMT
• Title: A Unified Deep Learning Framework for Motion Correction in Medical Imaging
• Authors: Jian Wang, Razieh Faghihpirayesh, Danny Joca, Polina Golland, Ali Gholipour,
• Abstract summary: We introduce UniMo, a Unified Motion Correction framework to correct diverse motion in medical imaging.<n>UniMo employs an alternating optimization scheme for a unified loss function to train an integrated model of 1) an equivariant neural network for global motion correction and 2) an encoder-decoder network for local deformations.<n>We trained and tested UniMo to track motion in fetal magnetic resonance imaging, a challenging application due to 1) both large rigid and non-rigid motion, and 2) wide variations in image appearance.
• Score: 6.727558990042319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has shown significant value in image registration, however, current techniques are either limited by the type and range of motion they can handle, or require iterative inference and/or retraining for new imaging data. To address these limitations, we introduce UniMo, a Unified Motion Correction framework that leverages deep neural networks to correct diverse motion in medical imaging. UniMo employs an alternating optimization scheme for a unified loss function to train an integrated model of 1) an equivariant neural network for global rigid motion correction and 2) an encoder-decoder network for local deformations. It features a geometric deformation augmenter that 1) enhances the robustness of global correction by addressing local deformations from non-rigid motion or geometric distortions, and 2) generates augmented data to improve training. UniMo is a hybrid model that uses both image intensities and shapes to achieve robust performance amid appearance variations, and therefore generalizes to multiple imaging modalities without retraining. We trained and tested UniMo to track motion in fetal magnetic resonance imaging, a challenging application due to 1) both large rigid and non-rigid motion, and 2) wide variations in image appearance. We then evaluated the trained model, without retraining, on MedMNIST, lung CT, and BraTS datasets. Results show that UniMo surpassed existing motion correction methods in accuracy, and notably enabled one-time training on a single modality while maintaining high stability and adaptability across unseen datasets. By offering a unified solution to motion correction, UniMo marks a significant advance in medical imaging, especially in applications with combined bulk and local motion. The code is available at: https://github.com/IntelligentImaging/UNIMO

Related papers

• Retrospective motion correction in MRI using disentangled embeddings [8.082774010683968]
  Motion artifacts, though diverse, share underlying patterns that can be disentangled and exploited.<n>We propose a hierarchical vector-quantized (VQ) variational auto-encoder that learns a disentangled embedding of motion-to-clean image features.<n>We demonstrate the approach on simulated whole-body motion artifacts and observe robust correction across varying motion severity.
  arXiv  Detail & Related papers  (2025-11-11T15:44:37Z)
• Improving Generalization of Medical Image Registration Foundation Model [12.144724550118756]
  This paper incorporates Sharpness-Aware Minimization into foundation models to enhance generalization and robustness in medical image registration.<n> Experimental results show that foundation models integrated with SAM achieve significant improvements in cross-dataset registration performance.
  arXiv  Detail & Related papers  (2025-05-10T06:14:09Z)
• DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images [4.117232425638352]
  DIMA (DIffusing Motion Artifacts) is a novel framework that leverages diffusion models to enable unsupervised motion artifact correction in brain MRI.<n>Our two-phase approach first trains a diffusion model on unpaired motion-affected images to learn the distribution of motion artifacts.<n>This model then generates realistic motion artifacts on clean images, creating paired datasets suitable for supervised training of correction networks.
  arXiv  Detail & Related papers  (2025-04-09T10:43:38Z)
• MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training [62.843316348659165]
  Deep learning-based image matching algorithms have dramatically outperformed humans in rapidly and accurately finding large amounts of correspondences. We propose a large-scale pre-training framework that utilizes synthetic cross-modal training signals to train models to recognize and match fundamental structures across images. Our key finding is that the matching model trained with our framework achieves remarkable generalizability across more than eight unseen cross-modality registration tasks.
  arXiv  Detail & Related papers  (2025-01-13T18:37:36Z)
• One-shot Human Motion Transfer via Occlusion-Robust Flow Prediction and Neural Texturing [21.613055849276385]
  We propose a unified framework that combines multi-scale feature warping and neural texture mapping to recover better 2D appearance and 2.5D geometry. Our model takes advantage of multiple modalities by jointly training and fusing them, which allows it to robust neural texture features that cope with geometric errors.
  arXiv  Detail & Related papers  (2024-12-09T03:14:40Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• 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 movement.<n>Current state-of-the-art (SOTA) MoCo algorithms often rely on extensive high-quality MR images to pre-train neural networks.<n>We propose Moner, an unsupervised MoCo method that jointly reconstructs artifact-free MR images and estimates accurate motion from rigid motion-corrupted k-space data.
  arXiv  Detail & Related papers  (2024-09-25T13:27:29Z)
• Motion-Oriented Compositional Neural Radiance Fields for Monocular Dynamic Human Modeling [10.914612535745789]
  This paper introduces Motion-oriented Compositional Neural Radiance Fields (MoCo-NeRF) MoCo-NeRF is a framework designed to perform free-viewpoint rendering of monocular human videos.
  arXiv  Detail & Related papers  (2024-07-16T17:59:01Z)
• ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration [6.217733993535475]
  Deformable image registration plays a crucial role in medical imaging, aiding in disease diagnosis and image-guided interventions.<n>Traditional iterative methods are slow, while deep learning (DL) accelerates solutions but faces usability and precision challenges.<n>This study introduces a pyramid network with the enhanced motion Transformer (ModeTv2) operator, showcasing superior pairwise optimization (PO) akin to traditional methods.
  arXiv  Detail & Related papers  (2024-03-25T08:09:22Z)
• Spectral Motion Alignment for Video Motion Transfer using Diffusion Models [54.32923808964701]
  Spectral Motion Alignment (SMA) is a framework that refines and aligns motion vectors using Fourier and wavelet transforms. SMA learns motion patterns by incorporating frequency-domain regularization, facilitating the learning of whole-frame global motion dynamics. Extensive experiments demonstrate SMA's efficacy in improving motion transfer while maintaining computational efficiency and compatibility across various video customization frameworks.
  arXiv  Detail & Related papers  (2024-03-22T14:47:18Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction [50.248694764703714]
  Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization. We propose Greedy LEarning for Accelerated MRI reconstruction, an efficient training strategy for high-dimensional imaging settings.
  arXiv  Detail & Related papers  (2022-07-18T06:01:29Z)
• Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring [87.97330195531029]
  We propose a Neural Maximum A Posteriori (NeurMAP) estimation framework for training neural networks to recover blind motion information and sharp content from unpaired data. The proposed NeurMAP is an approach to existing deblurring neural networks, and is the first framework that enables training image deblurring networks on unpaired datasets.
  arXiv  Detail & Related papers  (2022-04-26T08:09:47Z)
• MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent [0.0]
  This paper extends the Voxelmorph approach in three different ways. To improve the performance in case of small as well as large deformations, supervision of the model at different resolutions has been integrated using a multi-scale UNet. On the task of registration of brain MRIs, the proposed method achieved significant improvements over ANTs and VoxelMorph.
  arXiv  Detail & Related papers  (2022-03-08T18:07:47Z)
• Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration [33.72954116727303]
  We present a novel reinforcement learning-based framework that performs step-wise registration of medical images with large deformations. We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
  arXiv  Detail & Related papers  (2021-12-14T14:08:56Z)
• DiffuseMorph: Unsupervised Deformable Image Registration Along Continuous Trajectory Using Diffusion Models [31.826844124173984]
  We present a novel approach of diffusion model-based probabilistic image registration, called DiffuseMorph. Our model learns the score function of the deformation between moving and fixed images. Our method can provide flexible and accurate deformation with a capability of topology preservation.
  arXiv  Detail & Related papers  (2021-12-09T08:41:23Z)
• Image-specific Convolutional Kernel Modulation for Single Image Super-resolution [85.09413241502209]
  In this issue, we propose a novel image-specific convolutional modulation kernel (IKM) We exploit the global contextual information of image or feature to generate an attention weight for adaptively modulating the convolutional kernels. Experiments on single image super-resolution show that the proposed methods achieve superior performances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-16T11:05:10Z)
• Learning a Model-Driven Variational Network for Deformable Image Registration [89.9830129923847]
  VR-Net is a novel cascaded variational network for unsupervised deformable image registration. It outperforms state-of-the-art deep learning methods on registration accuracy. It maintains the fast inference speed of deep learning and the data-efficiency of variational model.
  arXiv  Detail & Related papers  (2021-05-25T21:37:37Z)
• Encoding Robustness to Image Style via Adversarial Feature Perturbations [72.81911076841408]
  We adapt adversarial training by directly perturbing feature statistics, rather than image pixels, to produce robust models. Our proposed method, Adversarial Batch Normalization (AdvBN), is a single network layer that generates worst-case feature perturbations during training.
  arXiv  Detail & Related papers  (2020-09-18T17:52:34Z)
• A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding [57.1077544780653]
  We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end.
  arXiv  Detail & Related papers  (2020-06-26T08:34:54Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)

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.