ArticleMagnetic resonance in medicine2021
Three-dimensional self-attention conditional GAN with spectral normalization for multimodal neuroimaging synthesis.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Generating Synthetic MR Spectroscopic Imaging Data with Generative Adversarial Networks to Train Machine Learning Models.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Acute myeloid leukemia classification using ReLViT and detection with YOLO enhanced by adversarial networks on bone marrow images.Scientific reports · 2025Article
- Toward general text-guided multimodal brain MRI synthesis for diagnosis and medical image analysis.Cell reports. Medicine · 2025Article
- The deep learning-based physical education course recommendation system under the internet of things.Heliyon · 2024Article
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- TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction.Medical image analysis · 2024Article
- Turning brain MRI into diagnostic PET:Medical image analysis · 2024Article
- Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.Journal of magnetic resonance imaging : JMRI · 2024Article
- MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis.BMC medical imaging · 2024Article
- Clinical validation of enhanced CT imaging for distal radius fractures through conditional Generative Adversarial Networks (cGAN).PloS one · 2024Article
- One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation.IEEE transactions on medical imaging · 2023Article
- Weakly supervised perivascular spaces segmentation with salient guidance of Frangi filter.Magnetic resonance in medicine · 2023Article
- Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions.Journal of applied clinical medical physics · 2023Review
- Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models.Medical image analysis · 2023Article
- Artifact-free fat-water separation in Dixon MRI using deep learning.Journal of big data · 2023Article
- Generative AI for brain image computing and brain network computing: a review.Frontiers in neuroscience · 2023Review
- Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.European journal of nuclear medicine and molecular imaging · 2022Review
- Article
- FDG-PET to T1 Weighted MRI Translation with 3D Elicit Generative Adversarial Network (E-GAN).Sensors (Basel, Switzerland) · 2022Article
- Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning.Cancers · 2022Article
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4 authors.
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Abstract
purposeTo develop a new 3D generative adversarial network that is designed and optimized for the application of multimodal 3D neuroimaging synthesis.
methodsWe present a 3D conditional generative adversarial network (GAN) that uses spectral normalization and feature matching to stabilize the training process and ensure optimization convergence (called SC-GAN). A self-attention module was also added to model the relationships between widely separated image voxels. The performance of the network was evaluated on the data set from ADNI-3, in which the proposed network was used to predict PET images, fractional anisotropy, and mean diffusivity maps from multimodal MRI. Then, SC-GAN was applied on a multidimensional diffusion MRI experiment for superresolution application. Experiment results were evaluated by normalized RMS error, peak SNR, and structural similarity.
resultsIn general, SC-GAN outperformed other state-of-the-art GAN networks including 3D conditional GAN in all three tasks across all evaluation metrics. Prediction error of the SC-GAN was 18%, 24% and 29% lower compared to 2D conditional GAN for fractional anisotropy, PET and mean diffusivity tasks, respectively. The ablation experiment showed that the major contributors to the improved performance of SC-GAN are the adversarial learning and the self-attention module, followed by the spectral normalization module. In the superresolution multidimensional diffusion experiment, SC-GAN provided superior predication in comparison to 3D Unet and 3D conditional GAN.
conclusionIn this work, an efficient end-to-end framework for multimodal 3D medical image synthesis (SC-GAN) is presented. The source code is also made available at https://github.com/Haoyulance/SC-GAN.
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