ArticleIEEE transactions on medical imaging2023
One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation.
Article in IEEE transactions on medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Seeing the Risk in Virtual Fat-Suppressed Spine MRI: External Benchmarking and Uncertainty-Guided Selective Risk Analysis.Journal of imaging informatics in medicine · 2026Article
- AI-Powered Gradient Echo Plural Contrast Imaging (AI-GEPCI)-A Comprehensive Neurological Protocol From a Single MRI Scan.Journal of magnetic resonance imaging : JMRI · 2026Article
- Random forest imputation and genomic prediction for missing egg production time-series data in yellow-feathered broiler breeders.BMC genomics · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- Hierarchical Multi-Scale Feature Fusion Network with Implicit Neural Representation and Mamba for Cross-Modality MRI Synthesis.Sensors (Basel, Switzerland) · 2026Article
- Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI.Nature communications · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.bioRxiv : the preprint server for biology · 2026Article
- AI-powered Gradient Echo Plural Contrast Imaging (AI-GEPCI) - a Comprehensive Multiparametric Neurological Protocol from a Single MRI Scan.medRxiv : the preprint server for health sciences · 2026Article
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- AI-powered segmentation and prognosis with missing MRI in pediatric brain tumors.NPJ precision oncology · 2026Article
- Unsupervised generative AI for enhancing brain tumor segmentation in multi-center, incomplete real-world data scenarios.NPJ precision oncology · 2025Article
- Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review.Artificial intelligence and applications (Commerce, Calif.) · 2025Article
- Laplacian filter attention with style transfer GAN for brain tumor MRI imputation.Scientific reports · 2025Article
- Toward general text-guided multimodal brain MRI synthesis for diagnosis and medical image analysis.Cell reports. Medicine · 2025Article
- T1-contrast enhanced MRI generation from multi-parametric MRI for glioma patients with latent tumor conditioning.Medical physics · 2025Article
- Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.Japanese journal of radiology · 2025Review
- Beyond the lungs: extending the field-of-view in chest CT with latent diffusion models.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Similarity and quality metrics for MR image-to-image translation.Scientific reports · 2025Article
- Robust deep MRI contrast synthesis using a prior-based and task-oriented 3D network.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- CT synthesis with deep learning for MR-only radiotherapy planning: a review.Biomedical engineering letters · 2024Review
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6 authors.
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Abstract
Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each contrast provides complementary information. However, the availability of each imaging contrast may vary amongst patients, which poses challenges to radiologists and automated image analysis algorithms. A general approach for tackling this problem is missing data imputation, which aims to synthesize the missing contrasts from existing ones. While several convolutional neural networks (CNN) based algorithms have been proposed, they suffer from the fundamental limitations of CNN models, such as the requirement for fixed numbers of input and output channels, the inability to capture long-range dependencies, and the lack of interpretability. In this work, we formulate missing data imputation as a sequence-to-sequence learning problem and propose a multi-contrast multi-scale Transformer (MMT), which can take any subset of input contrasts and synthesize those that are missing. MMT consists of a multi-scale Transformer encoder that builds hierarchical representations of inputs combined with a multi-scale Transformer decoder that generates the outputs in a coarse-to-fine fashion. The proposed multi-contrast Swin Transformer blocks can efficiently capture intra- and inter-contrast dependencies for accurate image synthesis. Moreover, MMT is inherently interpretable as it allows us to understand the importance of each input contrast in different regions by analyzing the in-built attention maps of Transformer blocks in the decoder. Extensive experiments on two large-scale multi-contrast MRI datasets demonstrate that MMT outperforms the state-of-the-art methods quantitatively and qualitatively.
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