ArticleNature biomedical engineering2025
A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- A foundation model for acute abdomen diagnosis stratification and triage on noncontrast computed tomography.Nature communications · 2026Article
- Foundation models in biomedical imaging: turning hype into reality.Nature biomedical engineering · 2026Review
- On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction.Magnetic resonance in medicine · 2026Article
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- Large-scale multi-sequence pretraining for generalizable MRI analysis in versatile clinical applications.Nature biomedical engineering · 2026Article
- Orientation-Aware Diffusion Super-Resolution for 3T-Like Fetal MRI from Routine 1.5T Scans.Proceedings of machine learning research · 2026Article
- Leveraging Self-Supervised Learning for Non-Invasive Intra-Cardiac Magnetic Resonance Oximetry Assessment.medRxiv : the preprint server for health sciences · 2026Article
- A multi-modal foundation model for brain disease diagnosis and medical imaging.Patterns (New York, N.Y.) · 2026Article
- A universal foundation model for grounded biomedical image interpretation.Nature communications · 2026Article
- Developmental human brain connectome from fetal stage to early childhood.Developmental cognitive neuroscience · 2026Review
- Universal and transferable attacks on pathology foundation models using microscopic perturbations.Light, science & applications · 2026Article
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- Development and validation of a versatile foundation model for cine cardiac magnetic resonance image analysis.Communications medicine · 2026Article
- Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- DEEP-DISORDER: Motion Correction in 3D MRI via Segment Reconstruction and Registration.NMR in biomedicine · 2026Article
- Spatiotemporal dynamics of the human cortical functional hierarchy across the lifespan.Nature communications · 2026Article
- Vision-language framework for multi-sequence brain magnetic resonance imaging.medRxiv : the preprint server for health sciences · 2026Article
- Real-world unified denoising for multi-organ fast MRI: a large-scale prospective validation.NPJ digital medicine · 2026Article
- A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images.Nature communications · 2026Article
- More than meets the eye: decoding glioma genomes in MRI pixel patterns.European radiology · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In structural magnetic resonance (MR) imaging, motion artefacts, low resolution, imaging noise and variability in acquisition protocols frequently degrade image quality and confound downstream analyses. Here we report a foundation model for the motion correction, resolution enhancement, denoising and harmonization of MR images. Specifically, we trained a tissue-classification neural network to predict tissue labels, which are then leveraged by a 'tissue-aware' enhancement network to generate high-quality MR images. We validated the model's effectiveness on a large and diverse dataset comprising 2,448 deliberately corrupted images and 10,963 images spanning a wide age range (from foetuses to elderly individuals) acquired using a variety of clinical scanners across 19 public datasets. The model consistently outperformed state-of-the-art algorithms in improving the quality of MR images, handling pathological brains with multiple sclerosis or gliomas, generating 7-T-like images from 3 T scans and harmonizing images acquired from different scanners. The high-quality, high-resolution and harmonized images generated by the model can be used to enhance the performance of models for tissue segmentation, registration, diagnosis and other downstream tasks.
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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.