# TLRN: Temporal Latent Residual Networks For Large Deformation Image Registration

arXiv: https://arxiv.org/abs/2407.11219 · HTML: https://mlsanity.com/paper/2407.11219/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [DeepFLASH: An Efficient Network for Learning-based Medical Image Registration](https://mlsanity.com/paper/2004.02097/) (2020)
- [Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior](https://mlsanity.com/paper/2206.03830/) (2022)
- [CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy](https://mlsanity.com/paper/2301.13098/) (2023)
- [NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces](https://mlsanity.com/paper/2303.07115/) (2023)
- [Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI](https://mlsanity.com/paper/1907.13524/) (2019)
- [Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix](https://mlsanity.com/paper/2011.01741/) (2020)
- [Multimodal Learning To Improve Cardiac Late Mechanical Activation Detection From Cine MR Images](https://mlsanity.com/paper/2402.18507/) (2024)
- [Temporal Registration in In-Utero Volumetric MRI Time Series](https://mlsanity.com/paper/1608.03907/) (2016)

## Built on by (most cited first, 7 of 7)

- [TimeFlow: Temporal Conditioning for Longitudinal Brain MRI Registration and Aging Analysis](https://mlsanity.com/paper/2501.08667/) (2025)
- [Capturing Longitudinal Changes in Brain Morphology Using Temporally Parameterized Neural Displacement Fields](https://mlsanity.com/paper/2504.09514/) (2025)
- [Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model](https://mlsanity.com/paper/2507.02003/) (2025)
- [CardioMorphNet: Cardiac Motion Prediction Using a Shape-Guided Bayesian Recurrent Deep Network](https://mlsanity.com/paper/2508.20734/) (2025)
- [Learning to Unify Deformable Shape and Texture Representations for Cardiac Video Classification](https://mlsanity.com/paper/2607.07518/) (2026)
- [Generative Brownian Bridge Diffusion In Motion Space For Enhanced Myocardial Strain Analysis](https://mlsanity.com/paper/2608.01677/) (2026)
- [Geodesic-informed Generative Diffusion Model For Topology-preserved Image Video Generation](https://mlsanity.com/paper/2609.08153/) (2026)

## Abstract (arXiv)

This paper presents a novel approach, termed em Temporal Latent Residual Network (TLRN), to predict a sequence of deformation fields in time-series image registration. The challenge of registering time-series images often lies in the occurrence of large motions, especially when images differ significantly from a reference (e.g., the start of a cardiac cycle compared to the peak stretching phase). To achieve accurate and robust registration results, we leverage the nature of motion continuity and exploit the temporal smoothness in consecutive image frames. Our proposed TLRN highlights a temporal residual network with residual blocks carefully designed in latent deformation spaces, which are parameterized by time-sequential initial velocity fields. We treat a sequence of residual blocks over time as a dynamic training system, where each block is designed to learn the residual function between desired deformation features and current input accumulated from previous time frames. We validate the effectivenss of TLRN on both synthetic data and real-world cine cardiac magnetic resonance (CMR) image videos. Our experimental results shows that TLRN is able to achieve substantially improved registration accuracy compared to the state-of-the-art. Our code is publicly available at https://github.com/nellie689/TLRN.