ArticleIEEE transactions on medical imaging2022
SynthMorph: Learning Contrast-Invariant Registration Without Acquired Images.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
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Who cites it
93 citing papers in PubMed.
- Correlation of FAPI PET Uptake with Immunohistochemistry in Explanted Lungs from Patients with Advanced Interstitial Lung Disease.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Trial
- Brain distortion and cerebrospinal fluid shifts in spontaneous intracranial hypotension.Fluids and barriers of the CNS · 2026Article
- Beyond the LUMIR challenge: The pathway to foundational registration models.Medical image analysis · 2026Article
- Brain parenchyma deformation and strain in patients with spontaneous intracranial hypotension.Fluids and barriers of the CNS · 2026Article
- Deep Learning Based Cross-Modality Histological Brain Section Registration in Multiple Species Using Synthetic Images.bioRxiv : the preprint server for biology · 2026Article
- Validation of five automated structural MRI quality assessment tools against expert ratings.Neuroradiology · 2026Article
- An MRI Atlas of Regional Brain Vulnerability to Metastatic Disease.medRxiv : the preprint server for health sciences · 2026Article
- Adaptive Riemannian optimization for multi-scale diffeomorphic matching.Nature communications · 2026Article
- Automated Coregistered Segmentation for Volumetric Analysis of Multiparametric Renal MRI.Magnetic resonance in medicine · 2026Article
- Joint zonated quantification of multiple parameters in hepatic lobules.Scientific reports · 2026Article
- Anatomically constrained and curated cerebellar tractography (ACCURAT): an open framework and a pathway-specific neuroanatomical reference.bioRxiv : the preprint server for biology · 2026Article
- Quantitative assessment of flow between cerebrospinal and interstitial fluid compartments in humans.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- On the accuracy of image registration in portable low-field 3D brain MRI.Research square · 2026Article
- VesSynth: Tubes Are All You Need for Robust Cross-Scale Cross-Modal 3D Vessel Segmentation.bioRxiv : the preprint server for biology · 2026Article
- A human subcortical connectome at 400 μm resolution.bioRxiv : the preprint server for biology · 2026Article
- Memory efficient training for 3D brain image registration networks using PatchMorph.Scientific reports · 2026Article
- Efficient cosine-windowed cross-correlation for intermediate deformable image registration.Scientific reports · 2026Article
- deepmriprep: voxel-based morphometry preprocessing via deep neural networks.Nature computational science · 2026Article
- Enhancing generative networks for chest anomaly localization through automatic registration-based unpaired-to-pseudo-paired training data translation.Scientific reports · 2026Article
- Optimal Deep Brain Stimulation Locations for Gilles de la Tourette Syndrome.medRxiv : the preprint server for health sciences · 2026Article
33 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at doic https://w3id.org/synthmorph.
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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.