Cross2SynNet: cross-device–cross-modal synthesis of routine brain MRI sequences from CT with brain lesion

Dill T (2008) Contraindications to magnetic resonance imaging. Heart 94(7):943–948

Article  PubMed  CAS  Google Scholar 

Oliveri S, Pricolo P, Pizzoli S, Faccio F, Lampis V, Summers P, Petralia G, Pravettoni G (2018) Investigating cancer patient acceptance of Whole Body MRI. Clin Imaging 52:246–251

Article  PubMed  Google Scholar 

Jue J, Jason H, Neelam T, Andreas R, Sean BL, Joseph DO, Harini V (2019) Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation. Medical image computing and computer assisted intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI 22. Springer, Berlin, pp 221–229

Chapter  Google Scholar 

Saba L, Anzidei M, Piga M, Ciolina F, Mannelli L, Catalano C, Suri JS, Raz E (2014) Multi-modal CT scanning in the evaluation of cerebrovascular disease patients. Cardiovasc Diag Therapy 4(3):245

Google Scholar 

Boulanger M, Nunes J-C, Chourak H, Largent A, Tahri S, Acosta O, De Crevoisier R, Lafond C, Barateau A (2021) Deep learning methods to generate synthetic CT from MRI in radiotherapy: a literature review. Phys Med 89:265–281

Article  PubMed  CAS  Google Scholar 

Kong L, Lian C, Huang D, Hu Y, Zhou Q (2021) Breaking the dilemma of medical image-to-image translation. Adv Neural Inform Process Syst 34:1964–1978

Google Scholar 

Qin Z, Liu Z, Zhu P, Ling W (2022) Style transfer in conditional GANs for cross-modality synthesis of brain magnetic resonance images. Comput Biol Med 148:105928

Article  PubMed  Google Scholar 

Dar SU, Yurt M, Karacan L, Erdem A, Erdem E, Cukur T (2019) Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans Med Imaging 38(10):2375–2388

Article  PubMed  Google Scholar 

Benzakoun J, Deslys M-A, Legrand L, Hmeydia G, Turc G, Hassen WB, Charron S, Debacker C, Naggara O, Baron J-C (2022) Synthetic FLAIR as a substitute for FLAIR sequence in acute ischemic stroke. Radiology 303(1):153–159

Article  PubMed  Google Scholar 

Kalantar R, Messiou C, Winfield JM, Renn A, Latifoltojar A, Downey K, Sohaib A, Lalondrelle S, Koh D-M, Blackledge MD (2021) Ct-based pelvic t1-weighted mr image synthesis using unet, unet++ and cycle-consistent generative adversarial network (cycle-gan). Front Oncol 11:665807

Article  PubMed  PubMed Central  Google Scholar 

Chen RJ, Lu MY, Chen TY, Williamson DF, Mahmood F (2021) Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng 5(6):493–497

Article  PubMed  PubMed Central  Google Scholar 

Li W, Li Y, Qin W, Liang X, Xu J, Xiong J, Xie Y (2020) Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy. Quant Imaging Med Surg 10(6):1223

Article  PubMed  PubMed Central  Google Scholar 

Hu N, Zhang T, Wu Y, Tang B, Li M, Song B, Gong Q, Wu M, Gu S, Lui S (2022) Detecting brain lesions in suspected acute ischemic stroke with CT-based synthetic MRI using generative adversarial networks. Ann Transl Med 10(2):35

Article  PubMed  PubMed Central  Google Scholar 

Feng E, Qin P, Chai R, Zeng J, Wang Q, Meng Y, Wang P (2022) MRI generated from CT for acute ischemic stroke combining radiomics and generative adversarial networks. IEEE J Biomed Health Inform 26(12):6047–6057

Article  PubMed  Google Scholar 

Costa P, Galdran A, Meyer MI, Niemeijer M, Abràmoff M, Mendonça AM, Campilho A (2017) End-to-end adversarial retinal image synthesis. IEEE Trans Med Imaging 37(3):781–791

Article  PubMed  Google Scholar 

Tragakis A, Kaul C, Murray-Smith R, Husmeier D (2023) The fully convolutional transformer for medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp 3660–3669

Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. Springer, Berlin, pp 234–241

Google Scholar 

Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1125–1134

Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:14111784

Nie D, Trullo R, Lian J, Wang L, Petitjean C, Ruan S, Wang Q, Shen D (2018) Medical image synthesis with deep convolutional adversarial networks. IEEE Trans Biomed Eng 65(12):2720–2730

Article  PubMed  PubMed Central  Google Scholar 

Klein S, Staring M, Murphy K, Viergever MA, Pluim JP (2009) Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29(1):196–205

Article  PubMed  Google Scholar 

Orlenko A, Kofink D, Lyytikäinen L-P, Nikus K, Mishra P, Kuukasjärvi P, Karhunen PJ, Kähönen M, Laurikka JO, Lehtimäki T (2020) Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning. Bioinformatics 36(6):1772–1778

Article  PubMed  CAS  Google Scholar 

He J, You H, Sandström E, Nittinger E, Bjerrum EJ, Tyrchan C, Czechtizky W, Engkvist O (2021) Molecular optimization by capturing chemist’s intuition using deep neural networks. J Cheminform 13(1):1–17

Article  Google Scholar 

Weissenbacher D, Ge S, Klein A, O’Connor K, Gross R, Hennessy S, Gonzalez-Hernandez G (2021) Active neural networks to detect mentions of changes to medication treatment in social media. J Am Med Inform Assoc 28(12):2551–2561

Article  PubMed  PubMed Central  Google Scholar 

Yu B, Zhou L, Wang L, Shi Y, Fripp J, Bourgeat P (2019) Ea-GANs: edge-aware generative adversarial networks for cross-modality MR image synthesis. IEEE Trans Med Imaging 38(7):1750–1762

Article  PubMed  Google Scholar 

Dalmaz O, Yurt M, Çukur T (2022) ResViT: residual vision transformers for multimodal medical image synthesis. IEEE Trans Med Imaging 41(10):2598–2614

Article  PubMed  Google Scholar 

Wang T-C, Liu M-Y, Zhu J-Y, Tao A, Kautz J, Catanzaro B (2018) High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 8798–8807

Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980

Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

Article  ADS  PubMed  Google Scholar 

Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on computer vision. pp 2223–2232

Jiang J, Hu YC, Tyagi N, Zhang P, Rimner A, Deasy JO, Veeraraghavan H (2019) Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets. Med Phys 46(10):4392–4404

Article  PubMed  Google Scholar 

O’Connor JP (2017) Rethinking the role of clinical imaging. Elife 6:e30563

Article  PubMed  PubMed Central  Google Scholar 

Liu Y, Chen A, Shi H, Huang S, Zheng W, Liu Z, Zhang Q, Yang X (2021) CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy. Comput Med Imaging Graph 91:101953

Article  PubMed  Google Scholar 

Bahrami A, Karimian A, Arabi H (2021) Comparison of different deep learning architectures for synthetic CT generation from MR images. Phys Med 90:99–107

Article  PubMed  Google Scholar 

Yurt M, Dar SU, Erdem A, Erdem E, Oguz KK, Çukur T (2021) mustGAN: multi-stream generative adversarial networks for MR image synthesis. Med Image Anal 70:101944

Article  PubMed  Google Scholar 

Liu J, Pasumarthi S, Duffy B, Gong E, Datta K, Zaharchuk G (2023) One model to synthesize them all: multi-contrast multi-scale transformer for missing data imputation. IEEE Trans Med Imaging 42(9):2577–2591

Article  PubMed  Google Scholar 

Zhang X, He X, Guo J, Ettehadi N, Aw N, Semanek D, Posner J, Laine A, Wang Y (2021) PTNet: a high-resolution infant MRI synthesizer based on transformer. arXiv preprint arXiv:210513993

Dorent R, Haouchine N, Kogl F, Joutard S, Juvekar P, Torio E, Golby AJ, Ourselin S, Frisken S, Vercauteren T (2023) Unified brain MR-ultrasound synthesis using multi-modal hierarchical representations. International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 448–458

Google Scholar 

Oh H-J, Jeong W-K (2023) DiffMix: diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets. arXiv preprint arXiv:230614132

Du Y, Jiang Y, Tan S, Wu X, Dou Q, Li Z, Li G, Wan X (2023) ArSDM: colonoscopy images synthesis with adaptive refinement semantic diffusion models. International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 339–349

Google Scholar 

Özbey M, Dalmaz O, Dar SU, Bedel HA, Özturk Ş, Güngör A, Çukur T (2023) Unsupervised medical image translation with adversarial diffusion models. IEEE Trans Med Imaging 42(12):3524–3539

Article  PubMed  Google Scholar 

Wu H, Xiao B, Codella N, Liu M, Dai X, Yuan L, Zhang L (2021) Cvt: introducing convolutions to vision transformers. In: Proceedings of the IEEE/CVF international conference on computer vision. pp 22–31

Guo J, Han K, Wu H, Tang Y, Chen X, Wang Y, Xu C (2022) Cmt: convolutional neural networks meet vision transformers. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition. pp 12175–12185

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:5998–6008

Google Scholar 

Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:201011929

Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:210204306

Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2022) Swin-unet: Unet-like pure transformer for medical image segmentation. European Conference on computer vision. Springer, Berlin, pp 205–218

Google Scholar 

Nie D, Shen D (2020) Adversarial confidence learning for medical image segmentation and synthesis. Int J Comput Vision 128:2494–2513

Article  MathSciNet  Google Scholar 

Augustin M, Bammer R, Simbrunner J, Stollberger R, Hartung H-P, Fazekas F (2000) Diffusion-weighted imaging of patients with subacute cerebral ischemia: comparison with conventional and contrast-enhanced MR imaging. Am J Neuroradiol 21(9):1596–1602

PubMed  PubMed Central  CAS  Google Scholar 

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