# A Unified Deep Learning Framework for Motion Correction in Medical Imaging

arXiv: https://arxiv.org/abs/2409.14204 · HTML: https://mlsanity.com/paper/2409.14204/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 151 references that are in the map.
- First posted on arXiv 21 Sep 2024.

## Builds on (most cited first, 30 of 33)

- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [DiffuseMorph: Unsupervised Deformable Image Registration Using Diffusion Model](https://mlsanity.com/paper/2112.05149/) (2021)
- [Spherical Linear Interpolation and Text-Anchoring for Zero-shot Composed Image Retrieval](https://mlsanity.com/paper/2405.00571/) (2024)
- [Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration](https://mlsanity.com/paper/1803.05982/) (2018)
- [Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging](https://mlsanity.com/paper/2212.08479/) (2022)
- [SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI](https://mlsanity.com/paper/2206.10802/) (2022)
- [A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints](https://mlsanity.com/paper/2304.09941/) (2023)
- [Deformable Slice-to-Volume Registration for Motion Correction in Fetal Body MRI](https://mlsanity.com/paper/1906.08827/) (2019)
- [Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion](https://mlsanity.com/paper/2109.12384/) (2021)
- [Slice-to-volume medical image registration: a survey](https://mlsanity.com/paper/1702.01636/) (2017)
- [Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review](https://mlsanity.com/paper/2305.06739/) (2023)
- [Equivariant Filters for Efficient Tracking in 3D Imaging](https://mlsanity.com/paper/2103.10255/) (2021)
- [Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network](https://mlsanity.com/paper/2006.12700/) (2020)
- [Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging](https://mlsanity.com/paper/1909.11625/) (2019)
- [Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis](https://mlsanity.com/paper/2007.01441/) (2020)
- [ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space](https://mlsanity.com/paper/2308.08830/) (2023)
- [SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI](https://mlsanity.com/paper/2312.13534/) (2023)
- [PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI](https://mlsanity.com/paper/1611.07289/) (2016)
- [BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration](https://mlsanity.com/paper/2405.14019/) (2024)
- [Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning](https://mlsanity.com/paper/2011.14134/) (2020)
- [SlerpFace: Face Template Protection via Spherical Linear Interpolation](https://mlsanity.com/paper/2407.03043/) (2024)
- [Fetal-BET: Brain Extraction Tool for Fetal MRI](https://mlsanity.com/paper/2310.01523/) (2023)
- [Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.05569/) (2022)
- [HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI](https://mlsanity.com/paper/2406.20042/) (2024)
- [Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data](https://mlsanity.com/paper/2202.05863/) (2022)
- [MetaMorph: Learning Metamorphic Image Transformation With Appearance Changes](https://mlsanity.com/paper/2303.04849/) (2023)

## Abstract (arXiv)

Deep learning has shown significant value in medical image registration for motion correction; 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 uses deep neural networks to correct various types of motion in medical imaging. UniMo uses an alternating optimization scheme with 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 motion correction by addressing local deformations, whether caused by non-rigid motion or geometric distortions, and 2) generates augmented data to improve training. As a hybrid model that uses both image intensities and shapes, UniMo is robust to appearance variations and generalizes to various imaging modalities without retraining. We trained and tested UniMo for motion tracking in fetal magnetic resonance imaging, which is challenging due to 1) both large rigid and non-rigid motion and 2) large variations in image appearance. We then tested the trained model, without retraining, on three public datasets: MedMNIST, lung CT, and BraTS. 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 multiple unseen imaging datasets. By offering a unified solution to motion correction, UniMo marks a significant advance in challenging applications with a mixture of bulk motion and local deformations. Code is available at https://github.com/IntelligentImaging/UNIMO