ArticleNature communications2026
Adaptive Riemannian optimization for multi-scale diffeomorphic matching.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Comparative evaluation of VoxelMorpand conventional deformable image registration algorithms for thoracic 4D-CT in radiotherapy.Journal of applied clinical medical physics · 2026Article
- Beyond the LUMIR challenge: The pathway to foundational registration models.Medical image analysis · 2026Article
- Brainana: an end-to-end preprocessing framework for macaque neuroimaging.bioRxiv : the preprint server for biology · 2026Article
- The feasibility of [Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Adaptive Riemannian optimization for multi-scale diffeomorphic matching.Nature communications · 2026Article
- Accelerating PREFUL MRI: Comparison of Registration Methods and Impact of Time Series Reduction.NMR in biomedicine · 2026Article
- Comparison of Signal- and Volume-Based Ventilation-Weighted Assessment Using 3D FLORET UTE MRI in Patients With Various Pulmonary Disease.Magnetic resonance in medicine · 2026Article
- Coil Sketching for Fast and Efficient 4D Lung MRI Reconstruction.Magnetic resonance in medicine · 2026Article
- Cohesin prevents local mixing of condensed euchromatic domains in living human cells.bioRxiv : the preprint server for biology · 2026Article
- Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas.Scientific reports · 2026Article
- PolyPose: Deformable 2D/3D Registration via Polyrigid Transformations.Advances in neural information processing systems · 2025Article
- Article
- Fast segmentation with the NextBrain histological atlas.bioRxiv : the preprint server for biology · 2025Article
- Gaussian primitives for deformable image registration.Physics and imaging in radiation oncology · 2025Article
- Streamlining 4D Cardiac Image Workflows: Open-Source Tools for Segmentation, Registration, and Visualization.Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference) · 2025Article
- MultiMorph: On-demand Atlas Construction.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- Fast segmentation with the NextBrain histological atlas.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Image matching is a fundamental task in quantitative biomedical and biological image analyses, enabling researchers to compare, integrate, and interpret imaging data across subjects, time points, modalities, and experimental conditions. Existing state-of-the-art registration methods are slow due to inefficient implementations and poor convergence rates because of the ill-conditioned nature of the optimization problem. Deep learning methods offer fast inference but require extensive training time, substantial inference memory, and fail to generalize across long-tailed distributions or diverse image modalities, necessitating costly retraining. We address these challenges by proposing FireANTs, a training-free, GPU-accelerated, multi-scale adaptive Riemannian optimization algorithm for fast and accurate dense diffeomorphic image matching. FireANTs more than doubles the speed of the community standard ANTs registration tool on a CPU, and is two orders of magnitude faster on a GPU. On the GPU, FireANTs performs competitively with deep learning methods on inference runtime while consuming up to 10 × less memory. FireANTs demonstrates robustness on a wide variety of matching problems across modalities, species, and organs, without any domain-specific training or tuning. Our framework allows hyperparameter grid search studies with less resources and time compared to traditional and deep learning registration algorithms alike.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.