Temporal Periodic Image Registration with Implicit Neural Representations

Mathias Lowes; Kristine Aavild Sørensen; Maxime Sermesant; Rasmus R. Paulsen

cs.CVOct 15, 2026

Temporal Periodic Image Registration with Implicit Neural Representations

Authors: Mathias Lowes, Kristine Aavild Sørensen, Maxime Sermesant, Rasmus R. Paulsen

Organizations: Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark · Novo Nordisk A/S, Copenhagen, Denmark · Inria Université Côte d’Azur, Nice, France

Abstract

Implicit Neural Representations (INRs) have recently gained popularity for their ability to model functions using simple and lightweight networks. These methods have also proven effective in deformable image registration. In this work, we extend INRs for deformable image registration to the domain of temporal image registration, focusing on periodic temporal image sequences. Our approach, Temporal-IDIR, optimizes a single INR to model deformations across all frames in a temporal image sequence simultaneously, allowing for self-regularization through its own deformation predictions. To achieve this, we introduce a temporal consistency loss that penalizes discrepancies between direct source-to-target transformations and those traversing intermediate frames. We evaluate our framework on the DIR-LAB dataset, using the target registration error (TRE) between annotated and moved landmarks as the metric. Here, we achieve a TRE of 1.03 mm, outperforming other INR-based registration methods. Additionally, our framework supports smooth interpolation between time frames by estimating deformations between the given input frames. Code is publicly available at https://github.com/MMLowes/Temporal_INR.

Figures & tables

Figure 1: Registration of multiple landmarks from source frame (left) to target frame (right). For each source point $\mathbf{x}$ the deformation $u(\mathbf{x})$ is calculated to map the point to the target domain $\mathrm{\Phi}_{S\to T}(\mathbf{x})$

Method	TRE [mm] $\downarrow$
VoxelMorph+ [ 1 ]	2.89
GradICON [ 15 ]	1.31
GraphRegNet [ 5 ]	1.39
PTVReg [ 19 ]	0.96
RRN [ 6 ]	0.83
IDIR [ 20 ]	1.07

Table 1: Comparison of mean target registration error (TRE) with other registration methods for landmark registration inspiration to expiration. The results for the other methods are adopted from the literature.

Setting	Direct	Sequential	Combined	Taylor
$\beta=0.0$	1.09	1.52	1.09	1.05
$\beta=0.01$	1.08	1.38	1.08	1.03
$\beta=0.05$	1.09	1.21	1.08	1.04
$\beta=0.1$	1.08	1.16	1.06	1.04
$\beta=0.5$	1.09	1.11	1.08	1.07
$\beta=1.0$	1.10	1.12	1.12	1.10

Table 2: Evaluation of

\beta

-values across the four inference methods in mean registration errors in mm for the landmark registration.

Figure 2: Comparison of interference methods for model with $\beta=0.01$ . (a) cumulative distribution of TRE for all in the dataset. (b) Mean TRE over time.

Figure 3: Landmark movement in the sagittal plane (yz). (a) and (b) illustrates the movement of a single point using 10 time steps and 100 time steps, respectively. (c) illustrates the movement of several points over a respiratory cycle.

Deformable Image Registration

Deformable image registration aligns images by estimating local spatial transformations that account for anatomical change, motion, and differences between scans.

See all 77 papers

Deformation-Free Cross-Domain Image Registration via Position-Encoded Temporal Attention
Feb 17, 2026Yiwen Wang, Jiahao QinTemporal Attention
LUMINR: Intensity-conditioned Meta-learned INRs for Accelerated Medical Image Registration
Oct 16, 2026Damian Rynczak, Marek WodzinskiMedical Image Registration Deformable Image Registration
AGH University of Krakow · Sano - Centre for Computational Personalised Medicine, International Research Foundation
The Right Prior for the Right Deformation: Rethinking Continuous Deformable Image Registration
Aug 17, 2026Hengjie Liu, Chushu Shen, Dan Ruan +1Medical Image Registration Deformable Image Registration
Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA · Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA · Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA +1

Feb 17, 2026J/K move · Enter open · S save

Deformation-Free Cross-Domain Image Registration via Position-Encoded Temporal Attention

Yiwen Wang, Jiahao Qin

Temporal Attention

Open paper AskPDF arXiv

We address the problem of cross-domain image registration, where paired images exhibit coupled geometric misalignment and domain-specific appearance shift. We formalize this as a factorization problem: decomposing each image into a domain-invariant scene representation and a global appearance statistic, such that registration reduces to recombining the scene structure of the moving image with the appearance of the fixed image via Adaptive Instance Normalization (AdaIN). This factorization eliminates the need for explicit deformation field estimation. To exploit temporal coherence in sequential acquisitions, we introduce a position-encoded cross-frame attention mechanism that fuses learnable and sinusoidal position embeddings with multi-head attention over a sliding window of neighboring frames, enriching the scene representation with inter-frame context. We instantiate this framework as GPEReg-Net and evaluate on two benchmarks: FIRE-Reg-256 (retinal fundus, semi-rigid) and HPatches-Reg-256 (synthetic textured patches, affine). GPEReg-Net achieves state-of-the-art performance on both benchmarks (FIRE: SSIM = 0.928, PSNR = 33.47 dB; HPatches: SSIM = 0.450, PSNR = 21.01 dB), surpassing all baselines, including deformation-based methods, while running 1.87x faster than SAS-Net. Code: https://github.com/JiahaoQin/GPEReg-Net.

Abstract

Deformable Image Registration

Explore similar work

Deformation-Free Cross-Domain Image Registration via Position-Encoded Temporal Attention

LUMINR: Intensity-conditioned Meta-learned INRs for Accelerated Medical Image Registration

The Right Prior for the Right Deformation: Rethinking Continuous Deformable Image Registration