ArticleIEEE transactions on medical imaging2021
Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- SASWISE-UE: Segmentation and synthesis with interpretable scalable ensembles for uncertainty estimation.Computers in biology and medicine · 2025Article
- ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer.IEEE transactions on medical imaging · 2024Article
- Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI.NPJ digital medicine · 2023Article
- Radiomics analysis of amide proton transfer-weighted and structural MR images for treatment response assessment in malignant gliomas.NMR in biomedicine · 2023Article
- Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review.Insights into imaging · 2022Review
- Learning-based analysis of amide proton transfer-weighted MRI to identify true progression in glioma patients.NeuroImage. Clinical · 2022Article
- Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging.IEEE transactions on medical imaging · 2021Article
- Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2021Article
- Over-and-Under Complete Convolutional RNN for MRI Reconstruction.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images. However, data quantity and quality remain a key determinant, and a significant limit of the potential applications. In our previous work, we explored the synthesis of anatomic and molecular MR image networks (SAMR) in patients with post-treatment malignant gliomas. In this work, we extend this through a confidence-guided SAMR (CG-SAMR) that synthesizes data from lesion contour information to multi-modal MR images, including T1-weighted ( [Formula: see text]), gadolinium enhanced [Formula: see text] (Gd- [Formula: see text]), T2-weighted ( [Formula: see text]), and fluid-attenuated inversion recovery ( FLAIR ), as well as the molecular amide proton transfer-weighted ( [Formula: see text]) sequence. We introduce a module that guides the synthesis based on a confidence measure of the intermediate results. Furthermore, we extend the proposed architecture to allow training using unpaired data. Extensive experiments on real clinical data demonstrate that the proposed model can perform better than current the state-of-the-art synthesis methods. Our code is available at https://github.com/guopengf/CG-SAMR.
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Registered trials
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