ArticleIEEE transactions on medical imaging2018
Multimodal MR Synthesis via Modality-Invariant Latent Representation.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed, 326 citations in OpenAlex.
- Enhancing automated fracture detection in paediatric wrist X-rays with paired and unpaired cast suppression methods.International journal of computer assisted radiology and surgery · 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
- Multimodal Cross-Attention Fusion of B-Mode Ultrasound and Strain Elastography for Tumor Segmentation in Robotics-Assisted Surgery.IEEE transactions on medical robotics and bionics · 2026Article
- From Static Diagnosis to Dynamic Guidance : Evolution of Artificial Intelligence in Pediatric Neuroimaging.Journal of Korean Neurosurgical Society · 2026Article
- A methodological assessment of information complementarity between MRI and GAN-based synthetic PET for multimodal medical image analysis.Scientific reports · 2026Article
- Multi-parametric MRI synthesis for glioblastoma from quantitative MR fingerprinting: Quantitative synthetic neural network (QS-Net).Medical physics · 2026Article
- Hierarchical Multi-Scale Feature Fusion Network with Implicit Neural Representation and Mamba for Cross-Modality MRI Synthesis.Sensors (Basel, Switzerland) · 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
- US-ATHC: Unsupervised Multi-Class Glioma Segmentation via Adaptive Thresholding and Clustering.Biomedicines · 2026Article
- Unified Cross-Modal Medical Image Synthesis With Hierarchical Mixture of Product-of-Experts.IEEE transactions on pattern analysis and machine intelligence · 2026Article
- Dense121GAN: transfer learning-enhanced conditional generative adversarial network with DenseNet121 for reliable and efficient segmentation in medical and industrial imaging.PeerJ. Computer science · 2026Article
- Development of AI-based dopamine transporter (DAT) image generation technique using early phase [18F]-FP-CIT PET imaging.PloS one · 2026Article
- Generation of multimodal realistic computational phantoms as a test-bed for validating deep learning-based cross-modality synthesis techniques.Medical & biological engineering & computing · 2026Article
- Multimodal magnetic resonance imaging synthesis via cross-modal feature fusion and hierarchical feature extraction.Quantitative imaging in medicine and surgery · 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
- Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives.Bioengineering (Basel, Switzerland) · 2025Review
- Enhancing pix2pix with Swin Transformer for Cross Modal Brain CT-MR synthesis.Research square · 2025Article
- Cross-Modality Image Translation of 3 Tesla Magnetic Resonance Imaging to 7 Tesla Using Generative Adversarial Networks.Human brain mapping · 2025Article
- Brain tumor enhancement prediction from pre-contrast conventional weighted images using synthetic multiparametric mapping and generative artificial intelligence.Quantitative imaging in medicine and surgery · 2025Article
- Robust deep MRI contrast synthesis using a prior-based and task-oriented 3D network.Imaging neuroscience (Cambridge, Mass.) · 2025Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
We propose a multi-input multi-output fully convolutional neural network model for MRI synthesis. The model is robust to missing data, as it benefits from, but does not require, additional input modalities. The model is trained end-to-end, and learns to embed all input modalities into a shared modality-invariant latent space. These latent representations are then combined into a single fused representation, which is transformed into the target output modality with a learnt decoder. We avoid the need for curriculum learning by exploiting the fact that the various input modalities are highly correlated. We also show that by incorporating information from segmentation masks the model can both decrease its error and generate data with synthetic lesions. We evaluate our model on the ISLES and BRATS data sets and demonstrate statistically significant improvements over state-of-the-art methods for single input tasks. This improvement increases further when multiple input modalities are used, demonstrating the benefits of learning a common latent space, again resulting in a statistically significant improvement over the current best method. Finally, we demonstrate our approach on non skull-stripped brain images, producing a statistically significant improvement over the previous best method. Code is made publicly available at https://github.com/agis85/multimodal_brain_synthesis.
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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.