Edge-preserving MRI image synthesis via adversarial network with iterative multi-scale fusion
Magnetic resonance imaging (MRI) is a major imaging technique for studying
neuroanatomy. By applying different pulse sequences and parameters, different modalities …
neuroanatomy. By applying different pulse sequences and parameters, different modalities …
Multi-modal MRI image synthesis via GAN with multi-scale gate mergence
B Zhan, D Li, X Wu, J Zhou… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Multi-modal magnetic resonance imaging (MRI) plays a critical role in clinical diagnosis and
treatment nowadays. Each modality of MRI presents its own specific anatomical features …
treatment nowadays. Each modality of MRI presents its own specific anatomical features …
D2FE-GAN: Decoupled dual feature extraction based GAN for MRI image synthesis
Magnetic resonance imaging (MRI) technique can generate various tissue contrasts by
using different pulse sequences and parameters. However, obtaining multiple different …
using different pulse sequences and parameters. However, obtaining multiple different …
Ea-GANs: edge-aware generative adversarial networks for cross-modality MR image synthesis
Magnetic resonance (MR) imaging is a widely used medical imaging protocol that can be
configured to provide different contrasts between the tissues in human body. By setting …
configured to provide different contrasts between the tissues in human body. By setting …
Multimodal MR image synthesis using gradient prior and adversarial learning
In magnetic resonance imaging (MRI), several images can be obtained using different
imaging settings (eg T1, T2, DWI, and Flair). These images have similar anatomical …
imaging settings (eg T1, T2, DWI, and Flair). These images have similar anatomical …
Common feature learning for brain tumor MRI synthesis by context-aware generative adversarial network
Abstract Multi-modal structural Magnetic Resonance Image (MRI) provides complementary
information and has been used widely for diagnosis and treatment planning of gliomas …
information and has been used widely for diagnosis and treatment planning of gliomas …
LR-cGAN: Latent representation based conditional generative adversarial network for multi-modality MRI synthesis
Objective This work aims to synthesize a real-like missing MRI modality using multiple
modalities those already obtained, thus providing more abundant diagnostic information …
modalities those already obtained, thus providing more abundant diagnostic information …
Multimodal MRI synthesis using unified generative adversarial networks
Purpose Complementary information obtained from multiple contrasts of tissue facilitates
physicians assessing, diagnosing and planning treatment of a variety of diseases. However …
physicians assessing, diagnosing and planning treatment of a variety of diseases. However …
Multi-modality generative adversarial networks with tumor consistency loss for brain mr image synthesis
Magnetic Resonance (MR) images of different modalities can provide complementary
information for clinical diagnosis, but whole modalities are often costly to access. Most …
information for clinical diagnosis, but whole modalities are often costly to access. Most …
Multi-scale transformer network with edge-aware pre-training for cross-modality MR image synthesis
Cross-modality magnetic resonance (MR) image synthesis can be used to generate missing
modalities from given ones. Existing (supervised learning) methods often require a large …
modalities from given ones. Existing (supervised learning) methods often require a large …