Lundervold AS, Lundervold A (2019) Arvid lundervold, an overview of deep learning in medical imaging focusing on MRI. Z Med Phys 29(2):102–127. https://doi.org/10.1016/j.zemedi.2018.11.002
Zeng C, Lin Gu, Liu Z, Zhao S (2020) Review of deep learning approaches for the segmentation of multiple sclerosis lesions on brain MRI. Front Neuroinform 14:55
Article
Google Scholar
Yi X, Walia E, Babyn P (2019) Generative adversarial network in medical imaging: a review. Med Image Anal 58:101552
Article
PubMed
Google Scholar
Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D et al (2018) PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 169(7):467–473
Article
PubMed
Google Scholar
Higgins J, Deeks J (2008) Chapter 7: selecting studies and collecting data. In: Cochrane handbook for systematic reviews of interventions. Wiley, Hoboken, NJ
Han C et al (2019) Combining noise-to-image and image-to-image gans: brain mr image augmentation for tumor detection. IEEE Access 7:156966–156977
Article
Google Scholar
Dai X et al (2020) Multimodal MRI synthesis using unified generative adversarial networks. Med Phys 47(12):6343–6354
Article
PubMed
Google Scholar
Sharma A, Hamarneh G (2020) Missing MRI pulse sequence synthesis using multi-modal generative adversarial network. IEEE Trans Med Imaging 39(4):1170–1183
Article
PubMed
Google Scholar
Huang Y, Zheng F, Cong R, Huang W, Scott MR, Shao L (2020) MCMT-GAN: multi-task coherent modality transferable GAN for 3D Brain image synthesis. IEEE Trans Image Process 29:8187–8198
Article
Google Scholar
Xin B, Hu Y, Zheng Y, Liao H (2020) Multi-modality generative adversarial networks with tumor consistency loss for brain MR image synthesis. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI), pp 1803–1807
Liu X, Yu A, Wei X, Pan Z, Tang J (2020) Multimodal MR image synthesis using gradient prior and adversarial learning. IEEE J Sel Top Signal Process 14(6):1176–1188
Article
CAS
Google Scholar
Chong CK, Ho ETW (2021) Synthesis of 3D MRI brain images with shape and texture generative adversarial deep neural networks. IEEE Access 9:64747–64760
Article
Google Scholar
Yang X, Lin Y, Wang Z, Li X, Cheng K-T (2020) Bi-modality medical image synthesis using semi-supervised sequential generative adversarial networks. IEEE J Biomed Health Inform 24(3):855–865
Article
PubMed
Google Scholar
Han C et al (2018) GAN-based synthetic brain MR image generation. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp 734–738
Qu Y, Deng C, Su W, Wang Y, Lu Y, Chen Z (2020) Multimodal brain MRI translation focused on lesions. In: Proceedings of the 2020 12th international conference on machine learning and computing, pp 352–359
Alogna E, Giacomello E, Loiacono D (2020) Brain magnetic resonance imaging generation using generative adversarial networks. In: 2020 IEEE symposium series on computational intelligence (SSCI), pp 2528–2535
Li Y et al (2020) Synthesize CT from paired MRI of the same patient with patch-based generative adversarial network. In: Progress in biomedical optics and imaging—proceedings of SPIE, vol 11314
Gu Y, Peng Y, Li H (2020) AIDS brain MRIs synthesis via generative adversarial networks based on attention-encoder. In: 2020 IEEE 6th international conference on computer and communications (ICCC), pp 629–633
Rejusha TR, KS VK (2021) Artificial MRI image generation using deep convolutional GAN and its comparison with other augmentation methods. In: 2021 international conference on communication, Control and Information Sciences (ICCISc), vol 1, pp 1–6
Rezaei M et al (2020) “Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis. In: Progress in biomedical optics and imaging—proceedings of SPIE, vol 11314
Sohan K, Yousuf MA (2020) 3D bone shape reconstruction from 2D X-ray images using MED generative adversarial network. In: 2020 2nd international conference on advanced information and communication technology (ICAICT), pp 53–58. https://doi.org/10.1109/ICAICT51780.2020.9333477
Dikici E, Bigelow M, White RD, Erdal BS, Prevedello LM (2021) Constrained generative adversarial network ensembles for sharable synthetic medical images. J Med Imaging 8(2):024004
Article
Google Scholar
Al-Tahan H, Mohsenzadeh Y (2021) Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder. PLoS Comput Biol 17(3):e1008775
Article
CAS
PubMed
PubMed Central
Google Scholar
Li D, Du C, Wang S, Wang H, He H (2021) Multi-subject data augmentation for target subject semantic decoding with deep multi-view adversarial learning. Inf Sci 547:1025–1044
Article
Google Scholar
Ma B et al (2020) MRI image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hippocampal subfields segmentation. Comput Med Imaging Gr 86:101800
Article
Google Scholar
Zhang S, Cao P, Dou L, Yang J, Zhao D (2020) An auto-encoding generative adversarial networks for generating brain network. In: The fourth international symposium on image computing and digital medicine, pp 14–18
Segato V, Corbetta MD, Marzo LP, Momi ED (2021) Data augmentation of 3D brain environment using deep convolutional refined auto-encoding alpha GAN. IEEE Trans Med Robot Bionics 3(1):269–272
Article
Google Scholar
Yang H, Qian P, Fan C (2020) an indirect multimodal image registration and completion method guided by image synthesis. Comput Math Methods Med 2020:2684851
PubMed
PubMed Central
Google Scholar
Kazemifar S et al (2019) MRI-only brain radiotherapy: assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach. Radiother Oncol 136:56–63
Article
PubMed
Google Scholar
Barile B, Marzullo A, Stamile C, Durand-Dubief F, Sappey-Marinier D (2021) Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis. Comp Methods Prog Biomed 206:106113
Article
Google Scholar
Hirte AU, Platscher M, Joyce T, Heit JJ, Tranvinh E, Federau C (2021) Realistic generation of diffusion-weighted magnetic resonance brain images with deep generative models. Magn Reson Imaging 81:60–66
Article
PubMed
Google Scholar
Jw S et al (2021) Synthetic generation of DSC-MRI-derived relative CBV maps from DCE MRI of brain tumors. Magn Reson Med 85(1):469–479
Article
CAS
Google Scholar
Finck T et al (2020) Deep-learning generated synthetic double inversion recovery images improve multiple sclerosis lesion detection. Invest Radiol 55(5):318–323
Article
PubMed
Google Scholar
Kazuhiro K et al (2018) Generative adversarial networks for the creation of realistic artificial brain magnetic resonance images. Tomography 4(4):159–163
Article
PubMed
PubMed Central
Google Scholar
Deepak S, Ameer PM (2020) MSG-GAN based synthesis of brain MRI with meningioma for data augmentation. In: 2020 IEEE international conference on electronics, computing and communication technologies (CONECCT), pp 1–6
Li G, Lv J, Wang C (2021) A modified generative adversarial network using spatial and channel-wise attention for CS-MRI reconstruction. IEEE Access 9:83185–83198
Article
Google Scholar
Ge C, Gu IY, Jakola AS Yang J (2019) Cross-modality augmentation of brain Mr images using a novel pairwise generative adversarial network for enhanced glioma classification. In: 2019 IEEE international conference on image processing (ICIP), pp 559–563
Hongtao Z, Shinomiya Y, Yoshida S (2020) 3D brain MRI reconstruction based on 2D super-resolution technology. In: 2020 IEEE international conference on systems, man, and cybernetics (SMC), pp 18–23
Zhang X, Yang Y, Wang H, Ning S, Wang H (2019) Deep neural networks with broad views for parkinson’s disease screening. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM) pp 1018–1022
Qiao K, Chen J, Wang L, Zhang C, Tong L, Yan B (2020) BigGAN-based bayesian reconstruction of natural images from human brain activity. Neuroscience 444:92–105
Article
CAS
PubMed
Google Scholar
Koike Y et al (2019) Feasibility of synthetic computed tomography generated with an adversarial network for multi-sequence magnetic resonance-based brain radiotherapy. J Radiat Res 61(1):92–103
Article
PubMed Central
Google Scholar
Hu S, Lei B, Wang S, Wang Y, Feng Z, Shen Y (2021) Bidirectional mapping generative adversarial networks for brain MR to PET synthesis. IEEE Trans Med Imaging 41:145–157
Article
PubMed
Google Scholar
Abu-Srhan A, Almallahi I, Abushariah MA, Mahafza W (2021) Al-Kadi, paired-unpaired unsupervised attention guided GAN with transfer learning for bidirectional brain MR-CT synthesis. Comput Biol Med 136:104763
Article
PubMed
Google Scholar
Conte GM et al (2021) Generative adversarial networks to synthesize missing T1 and FLAIR MRI sequences for use in a multisequence brain tumor segmentation model. Radiology 299(2):313–323
Article
PubMed
Google Scholar
La Rosa F, Yu T, Barquero G, Thiran JP, Granziera C, Cuadra MB (2021) MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion segmentation in multiple sclerosis patients. Comput Biol Med 132:104297
Article
PubMed
Google Scholar
Tang B et al (2021) Dosimetric evaluation of synthetic CT image generated using a neural network for MR-only brain radiotherapy. J Appl Clin Med Phys 22(3):55–62
Article
PubMed
PubMed Central
Google Scholar
Gu Y, Zheng Q (2021) A transfer deep generative adversarial network model to synthetic brain CT generation from MR images. Wirel Commun Mobile Comput 2021:1–10
Cheng D, Qiu N, Zhao F, Mao Y, Li C (2021) Research on the modality transfer method of brain imaging based on generative adversarial network. Front Neurosci 15:655019
Article
PubMed
PubMed Central
Google Scholar
Lei Y et al (2020) Multi-modality MRI arbitrary transformation using unified generative adversarial networks. In: Progress in biomedical optics and imaging—proceedings of SPIE, vol 11313
Chen H, Qin Z, Ding Y, Lan T (2019) Brain tumor segmentation with generative adversarial nets. In: 2019 2nd international conference on artificial intelligence and big data (ICAIBD), pp 301–305
Tokuoka Y, Suzuki S, Sugawara Y (2019) An inductive transfer learning approach using cycle-consistent adversarial domain adaptation with application to brain tumor segmentation. In: Proceedings of the 2019 6th international conference on biomedical and bioinformatics engineering, pp 44–48
Asma-Ull H, Yun ID, Han D (2020) Data efficient segmentation of various 3d medical images using guided generative adversarial networks. IEEE Access 8:102022–102031
Article
Google Scholar
Yu B, Zhou L, Wang L, Fripp J, Bourgeat P (2018) 3D cGAN based cross-modality MR image synthesis for brain tumor segmentation. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp 626–630
Wu W, Lu Y, Mane R, Guan C (2020) Deep learning for neuroimaging segmentation with a novel data augmentation strategy. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp 1516–1519
Hamghalam M, Wang T, Lei B (2020) High tissue contrast image synthesis via multistage attention-GAN: application to segmenting brain MR scans. Neural Netw 132:43–52
Article
PubMed
Google Scholar
Hamghalam M, Lei B, Wang T (2020) High tissue contrast MRI synthesis using multi-stage attention-GAN for glioma segmentation. In: AAAI—AAAI conference on artificial intelligence, pp 4067–4074
Lee H, Jo J, Lim H, Lee S (2020) Study on optimal generative network for synthesizing brain tumor-segmented MR images. Mathematical Problems in Engineering, 2020
Carver EN, Dai Z, Liang E, Snyder J, Wen N (2021) Improvement of multiparametric MR image segmentation by augmenting the data with generative adversarial networks for glioma patients. Front Comput Neurosci 14:107
Article
Google Scholar
Kossen T et al (2021) Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks. Comput Biol Med 131:104254
Article
PubMed
Google Scholar
Chen Y, Yang X, Cheng K, Li Y, Liu Z, Shi Y (2020) Efficient 3D neural networks with support vector machine for hippocampus segmentation. In: 2020 international conference on artificial intelligence and computer engineering (ICAICE), pp 337–341
Jang J, Lee HH, Park JA, Kim H (2021) Unsupervised anomaly detection using generative adversarial networks in 1H-MRS of the brain. J Magn Reson 325:106936
Article
CAS
PubMed
Google Scholar
Hamghalam M, Wang T, Qin J, Lei B (2020) Transforming intensity distribution of brain lesions via conditional gans for segmentation. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI), pp 1–4
Xi N (2019) Semi-supervised attentive mutual-info generative adversarial network for brain tumor segmentation. In: 2019 international conference on image and vision computing New Zealand (IVCNZ), pp 1–7
Thirumagal E, Saruladha K (2020) Design of FCSE-GAN for dissection of brain tumour in MRI. In: 2020 international conference on smart technologies in computing, electrical and electronics (ICSTCEE), pp 1–6
Özbey M, Çukur T (2020) T1-weighted contrast-enhanced synthesis for multi-contrast MRI segmentation. In: 28th signal processing and communications applications conference (SIU), pp 1–4. https://doi.org/10.1109/SIU49456.2020.9302109
Pradhan N, Dhaka VS, Rani G et al (2020) Transforming view of medical images using deep learning. Neural Comput Appl 32:15043–15054. https://doi.org/10.1007/s00521-020-04857-z
Article
Google Scholar
Zhuang P, Chapman B, Li R, Koyejo S (2019) Synthetic power analyses: empirical evaluation and application to cognitive neuroimaging. In: 2019 53rd asilomar conference on signals, systems, and computers, pp 1192–1196
Das J, Patel R, Pankajakshan V (2019) Brain tumor segmentation using discriminator loss. In: 2019 National conference on communications (NCC), pp 1–6
Bernal J, Valverde S, Kushibar K, Cabezas M, Oliver A, Llado X (2021) Generating longitudinal atrophy evaluation datasets on brain magnetic resonance images using convolutional neural networks and segmentation priors. Neuroinformatics 19(3):477–492
Article
PubMed
Google Scholar
Tang Z, Liu X, Li Y, Yap P, Shen D (2020) Multi-atlas brain parcellation using squeeze-and-excitation fully convolutional networks. IEEE Trans Image Process 29:6864–6872
Article
Google Scholar
Rezaei M, Yang H, Harmuth K, Meinel C (2019) Conditional generative adversarial refinement networks for unbalanced medical image semantic segmentation. In: 2019 IEEE winter conference on applications of computer vision (WACV), pp 1836–1845
Tao L, Fisher J, Anaya E, Li X, Levin CS (2021) Pseudo CT image synthesis and bone segmentation from MR images using adversarial networks with residual blocks for mr-based attenuation correction of brain PET data. IEEE Trans Radiat Plasma Med Sci 5(2):193–201
Article
Google Scholar
Mahapatra D, Ge Z (2019) Training data independent image registration with gans using transfer learning and segmentation information. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019)0, pp 709–713
Hou Y, Li T, Zhang Q, Yu H, Ge H (2021) Brain tumor segmentation based on knowledge distillation and adversarial training. In: 2021 international joint conference on neural networks (IJCNN), pp 1–7
Wu X, Bi L, Fulham M, Feng DD, Zhou L, Kim J (2021) Unsupervised brain tumor segmentation using a symmetric-driven adversarial network. Neurocomputing 455:242–254
Article
Google Scholar
Cheng G, Ji H, He L (2021) Correcting and reweighting false label masks in brain tumor segmentation. Med Phys 48(1):169–177
Article
PubMed
Google Scholar
Yuan W, Wei J, Wang J, Ma Q, Tasdizen T (2020) Unified generative adversarial networks for multimodal segmentation from unpaired 3D medical images. Med Image Anal 64:101731
Article
PubMed
Google Scholar
Liu J, Yin P, Wang X, Yang W, Cheng K (2019) Glioma subregions segmentation with a discriminative adversarial regularized 3D unet. In: ACM international conference proceeding series, pp 269–273
Liu Y et al (2020) A 3D fully convolutional neural network with top-down attention-guided refinement for accurate and robust automatic segmentation of amygdala and its subnuclei. Front Neurosci 14:260
Article
PubMed
PubMed Central
Google Scholar
Shi Y, Cheng K, Liu Z (2019) Hippocampal subfields segmentation in brain MR images using generative adversarial networks. Biomed Eng Online 18(1):5
Article
PubMed
PubMed Central
Google Scholar
Tong N, Gou S, Yang S, Cao M, Sheng K (2019) Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images. Med Phys 46(6):2669–2682
Article
PubMed
Google Scholar
Kang J, Lu W, Zhang W (2020) Fusion of brain PET and MRI images using tissue-aware conditional generative adversarial network with joint loss. IEEE Access 8:6368–6378
Article
Google Scholar
Ge C, Gu IY, Jakola AS, Yang J (2020) Enlarged training dataset by pairwise gans for molecular-based brain tumor classification. IEEE Access 8:22560–22570
Article
Google Scholar
Lin W (2020) Synthesizing missing data using 3D reversible GAN for alzheimer's disease. In: Proceedings of the 2020 international symposium on artificial intelligence in medical sciences, pp 208–213
Pan Y, Liu M, Lian C, Xia Y, Shen D (2020) Spatially-constrained fisher representation for brain disease identification with incomplete multi-modal neuroimages. IEEE Trans Med Imaging 39(9):2965–2975
Article
PubMed
PubMed Central
Google Scholar
Chen T, Song X, Wang C (2018) Preserving-texture generative adversarial networks for fast multi-weighted MRI. IEEE Access 6:71048–71059
Article
Google Scholar
Yu W, Lei B, Ng MK, Cheung AC, Shen Y, Wang S (2021) Tensorizing GAN with high-order pooling for alzheimer’s disease assessment. IEEE Trans Neural Netw Learn Syst
Zhou X et al (2021) Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning. Alzheimer’s Res Ther 13(1):1–11
Article
Google Scholar
Yerukalareddy DR, Pavlovskiy E (2021) Brain tumor classification based on mr images using GAN as a pre-trained model. In: 2021 IEEE ural-siberian conference on computational technologies in cognitive science, genomics and biomedicine (CSGB), pp 380–384
Ghassemi N, Shoeibi A, Rouhani M (2020) Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control 57:101678
Article
Google Scholar
Budianto T, Nakai T, Imoto K, Takimoto T, Haruki K (2020) Dual-encoder bidirectional generative adversarial networks for anomaly detection. In: 2020 19th IEEE international conference on machine learning and applications (ICMLA), pp 693–700
Gao X, Shi F, Shen D, Liu M (2021) Task-induced pyramid and attention GAN for multimodal brain image imputation and classification in alzheimers disease. IEEE J Biomed Health Inform
Sandhiya B, Priyatharshini R, Ramya B, Monish S, Raja GRS (2021) Reconstruction, identification and classification of brain tumor using gan and faster regional-CNN. In: 2021 3rd international conference on signal processing and communication (ICPSC), pp 238–242
Han C et al (2021) MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction. BMC Bioinform 22:31
Article
CAS
Google Scholar
Lin W et al (2021) Bidirectional mapping of brain MRI and PET with 3D reversible GAN for the diagnosis of alzheimer’s disease. Front Neurosci 15:646013
Article
PubMed
PubMed Central
Google Scholar
McKenna MC, Murad A, Huynh W, Lope J, Bede P (2020) Differential diagnosis of frontotemporal dementia, alzheimer’s disease, and normal aging using a multi-scale multi-type feature generative adversarial deep neural network on structural magnetic resonance images. Front Neurosci 14:853
Article
Google Scholar
Kaur S, Aggarwal H, Rani R (2021) Diagnosis of parkinson’s disease using deep CNN with transfer learning and data augmentation. Multimed Tools Appl 80(7):10113–10139
Article
Google Scholar
Ge C, Gu IY, Jakola AS, Yang J (2020) Deep semi-supervised learning for brain tumor classification. BMC Med Imaging 20(1):87
Article
PubMed
PubMed Central
Google Scholar
Han C et al (2019) Learning more with less: Conditional PGGAN-based data augmentation for brain metastases detection using highly-rough annotation on MR images. In: The conference on information and knowledge management, pp 119–127
Kazemifar S et al (2020) Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors. J Appl Clin Med Phys 21(5):76–86
Article
PubMed
PubMed Central
Google Scholar
Rezaei M, Yang H, Meinel C (2018) Generative adversarial framework for learning multiple clinical tasks. In: 2018 digital image computing: techniques and applications (DICTA), pp 1–8
Nguyen B, Feldman A, Bethapudi S, Jennings A, Willcocks CG (2021) Unsupervised region-based anomaly detection in brain MRI with adversarial image inpainting. In: 2021 IEEE 18th international symposium on biomedical imaging (ISBI), pp 1127–1131
Datta S, Dandapat S, Deka B (2022) A deep framework for enhancement of diagnostic information in CSMRI reconstruction. Biomed Signal Process Control 71:103117
Article
Google Scholar
Pham C-H et al (2019) Simultaneous super-resolution and segmentation using a generative adversarial network: application to neonatal brain MRI. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pp 991–994
Delannoy Q et al (2020) SegSRGAN: super-resolution and segmentation using generative adversarial networks—Application to neonatal brain MRI. Comput Biol Med 120:103755
Article
PubMed
Google Scholar
Lv J, Zhu J, Yang G (2021) Which GAN A comparative study of generative adversarial network-based fast MRI reconstruction. Philos Trans A Math Phys Eng Sci 379(2200):20200203. https://doi.org/10.1098/rsta.2020.0203
Article
PubMed
Google Scholar
Bourbonne V et al (2021) Dosimetric validation of a GAN-based pseudo-CT generation for MRI-only stereotactic brain radiotherapy. Cancers 13(5):1082
Article
PubMed
PubMed Central
Google Scholar
Zhang H, Shinomiya Y, Yoshida S (2021) 3D MRI reconstruction based on 2D generative adversarial network super-resolution. Sensors (Basel) 21(9):2978
Article
Google Scholar
Zhu J, Tan C, Yang J, Yang G, Lio P (2021) Arbitrary scale super-resolution for medical images. Int J Neural Syst 31(10):2150037
Article
PubMed
Google Scholar
Huang Y et al (2020) Super-resolution and inpainting with degraded and upgraded generative adversarial networks. vol 2021, pp 645–651
Han S, Carass A, Schär M, Calabresi PA, Prince JL (2021) Slice profile estimation from 2D MRI acquisition using generative adversarial networks. In: 2021 IEEE 18th international symposium on biomedical imaging (ISBI), pp 145–149
Gu Y et al (2020) MedSRGAN: medical images super-resolution using generative adversarial networks. Multimed Tools Appl 79(29):21815–21840
Article
Google Scholar
Goldfryd T,Gordon S, Raviv TR (2021) Deep semi-supervised bias field correction of Mr images. In: 2021 IEEE 18th international symposium on biomedical imaging (ISBI), pp 1836–1840
Arabi H, Zeng G, Zheng G, Zaidi H (2019) Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI. Eur J Nucl Med Mol Imaging 46(13):2746–2759
Article
PubMed
Google Scholar
Johnson PM, Drangova M (2019) Conditional generative adversarial network for 3D rigid-body motion correction in MRI. Magn Reson Med 82(3):901–910
PubMed
Google Scholar
Ran M et al (2019) Denoising of 3D magnetic resonance images using a residual encoder-decoder Wasserstein generative adversarial network. Med Image Anal 55:165–180
Article
PubMed
Google Scholar
Armanious K, Kumar V, Abdulatif S, Hepp T, Gatidis S, Yang B (2020) ipA-MedGAN: inpainting of arbitrary regions in medical imaging. In: 2020 IEEE international conference on image processing (ICIP), pp 3005–3009
Hagiwara A (2019) Improving the quality of synthetic FLAIR images with deep learning using a conditional generative adversarial network for pixel-by-pixel image translation. AJNR Am J Neuroradiol 40(2):224–230
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhang H, Wei ZX, Zhou JQ, Tian J (2020) Reconstructing the perceived faces from brain signals without large number of training samples*. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp 1108–1111
Emami H, Dong M, Glide-Hurst CK (2020) Attention-guided generative adversarial network to address atypical anatomy in synthetic CT generation. In: 2020 IEEE 21st international conference on information reuse and integration for data science (IRI), pp 188–193
Wang K, Tao J, Zhu J, Ye Z, Qiu B, Xu J (2019) Compressed sensing MRI reconstruction using generative adversarial network with enhanced antagonism. In: 2019 12th international conference on intelligent computation technology and automation (ICICTA), pp 282–285
Mozafari M, Reddy L, VanRullen R (2020) reconstructing natural scenes from fMRI patterns using BigBiGAN. In: 2020 international joint conference on neural networks (IJCNN), pp 1–8
Dar SUH, Yurt M, Shahdloo M, Ildız ME, Tınaz B, Çukur T (2020) Prior-guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks. IEEE J Sel Top Signal Process 14(6):1072–1087
Article
Google Scholar
Li Z, Tian Q et al (2022) High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN). Med Phys 49(2):1000–1014. https://doi.org/10.1002/mp.15427
Article
PubMed
Google Scholar
Shaul R, David I, Shitrit O, Raviv TR (2020) Subsampled brain MRI reconstruction by generative adversarial neural networks. Med Image Anal 65:101747
Article
PubMed
Google Scholar
Ren Z et al (2021) Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning. Neuroimage 228:117602
Article
PubMed
Google Scholar
Shen G, Dwivedi K, Majima K, Horikawa T, Kamitani Y (2019) End-to-end deep image reconstruction from human brain activity. Front Comput Neurosci 13:21
Article
PubMed
PubMed Central
Google Scholar
Lv J et al (2021) Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction. Comput Biol Med 134:104504
Article
PubMed
Google Scholar
Gu J, Li Z, Wang Y, Yang H, Qiao Z, Yu J (2019) Deep generative adversarial networks for thin-section infant MR image reconstruction. IEEE Access 7:68290–68304
Article
Google Scholar
Do WJ, Seo S, Han Y, Ye JC, Choi SH, Park SH (2020) Reconstruction of multicontrast MR images through deep learning. Med Phys 47(3):983–997
Article
PubMed
Google Scholar
Chen Y, Jakary A, Avadiappan S, Hess CP, Lupo JM (2020) QSMGAN: improved quantitative susceptibility mapping using 3D generative adversarial networks with increased receptive field. Neuroimage 207:116389
Article
PubMed
Google Scholar
Wegmayr V, Hörold M, Buhmann JM (2019) Generative aging of brain MRI for early prediction of MCI-AD conversion. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pp 1042–1046
Ali MB et al (2020) Domain mapping and deep learning from multiple MRI clinical datasets for prediction of molecular subtypes in low grade gliomas. Brain Sci 10(7):463
Article
PubMed Central
Google Scholar
Bessadok A, Mahjoub MA, Rekik I (2021) Brain multigraph prediction using topology-aware adversarial graph neural network. Med Image Anal 72:102090
Article
PubMed
Google Scholar
Ji J, Liu J, Han L, Wang F (2021) Estimating effective connectivity by recurrent generative adversarial networks. IEEE Trans Med Imaging 40:3326–3336
Article
PubMed
Google Scholar
Elazab A et al (2020) Glioma growth prediction via generative adversarial learning from multi-time points magnetic resonance images. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp 1750–1753
Wei W et al (2020) Predicting PET-derived myelin content from multisequence MRI for individual longitudinal analysis in multiple sclerosis. Neuroimage 223:117308
Article
CAS
PubMed
Google Scholar
Liu J, Ji J, Xun G, Yao L, Huai M, Zhang A (2020) EC-GAN: inferring brain effective connectivity via generative adversarial networks. In: AAAI—AAAI conference on artificial intelligence, pp 4852–4859
Zhao Y, Ma B, Jiang P, Zeng D, Wang X, Li S (2021) Prediction of alzheimer’s disease progression with multi-information generative adversarial network. IEEE J Biomed Health Inform 25(3):711–719
Article
PubMed
Google Scholar
Roychowdhury S, Roychowdhury S (2020) A modular framework to predict alzheimer’s disease progression using conditional generative adversarial networks. In: 2020 international joint conference on neural networks (IJCNN), pp 1–8
Rachmadi MF, C Valdés-Hernández MD, Makin S, Wardlaw JM, Komura T (2019) Predicting the evolution of white matter Hyperintensities in brain MRI using generative adversarial networks and irregularity map. In: International conference on medical image computing and computer-assisted intervention, pp 146–154
Elazab A et al (2020) GP-GAN: brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR images. Neural Netw 132:321–332
Article
PubMed
Google Scholar
Mahapatra D, Ge Z (2020) Training data independent image registration using generative adversarial networks and domain adaptation. Pattern Recognit 100:107109
Article
Google Scholar
Zheng Y et al (2021) SymReg-GAN: symmetric image registration with generative adversarial networks. IEEE transactions on pattern analysis and machine intelligence