mlsanityData to 14 Oct 2026

Generative Adversarial Networks for Brain Images Synthesis: A Review

arXiv 2305.15421 ↗First posted 16 May 2023eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

In medical imaging, image synthesis is the estimation process of one image (sequence, modality) from another image (sequence, modality). Since images with different modalities provide diverse biomarkers and capture various features, multi-modality imaging is crucial in medicine. While multi-screening is expensive, costly, and time-consuming to report by radiologists, image synthesis methods are capable of artificially generating missing modalities. Deep learning models can automatically capture and extract the high dimensional features. Especially, generative adversarial network (GAN) as one of the most popular generative-based deep learning methods, uses convolutional networks as generators, and estimated images are discriminated as true or false based on a discriminator network. This review provides brain image synthesis via GANs. We summarized the recent developments of GANs for cross-modality brain image synthesis including CT to PET, CT to MRI, MRI to PET, and vice versa.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 15 of its 39 references that are in the map. First posted on arXiv 16 May 2023.

Builds on

15 of 39 references.

References · 15

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2016Least Squares Generative Adversarial Networkscited by 1,001
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  10. 2021Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetcited by 19
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3 more
  1. 2021MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Diseasecited by 16
  2. 2020GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesiscited by 15
  3. 2021CNNs and GANs in MRI-based cross-modality medical image estimationcited by 2

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Citing papers per year: 2024: 2; 2025: 2.

4 papers.

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  3. 2024Predicting Hypoxia in Brain Tumors from Multiparametric MRIcited by 0
  4. 2025F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirementcited by 0