Page 36 - ARNM-2-2

Advances in Radiotherapy
& Nuclear Medicine Generative AI enhances radiotherapy

16. Jebara T, editor. Generative versus discriminative learning. based pelvic synthetic CT generation technique for MRI-
In: Machine Learning. United States: Springer; 2004. based prostate proton treatment planning. Phys Med Biol.
p. 17-60. 2019;64(20):205022.
doi: 10.1007/978-1-4419-9011-2_2 doi: 10.1088/1361-6560/ab41af
17. Alankrita A, Mamta M, Gopi B. Generative adversarial 28. Edmund JM, Nyholm T. A review of substitute CT
network: An overview of theory and applications. Int J Inf generation for MRI-only radiation therapy. Radiat Oncol.
Manage Data Insights. 2021;1(1):100004. 2017;12(1):28.
doi: 10.1016/j.jjimei.2020.100004 doi: 10.1186/s13014-016-0747-y
18. Wei R, Mahmood A. Recent advances in variational 29. Dougeni E, Faulkner K, Panayiotakis G. A review of patient
autoencoders with representation learning for biomedical dose and optimisation methods in adult and paediatric CT
informatics: A survey. IEEE Access. 2021;9:4939-4956. scanning. Eur J Radiol. 2012;81(4):e665-e683.
doi: 10.1109/ACCESS.2020.3048309 doi: 10.1016/j.ejrad.2011.05.025
19. Brown T, Mann B, Ryder N, et al. Language models are 30. Olberg S, Zhang H, Kennedy WR, et al. Synthetic CT
few-shot learners. In: Larochelle H, Ranzato M, Hadsell R, reconstruction using a deep spatial pyramid convolutional
Balcan MF, Lin H, editors. Advances in Neural Information framework for MR-only breast radiotherapy. Med Phys.
Processing Systems. Vol. 33. Glasgow: Curran Associates Inc.; 2019;46(9):4135-4147.
2020. p. 1877-1901.
doi: 10.1002/mp.13716
20. Schramowski P, Turan C, Andersen N, Rothkopf C, 31. Bourbonne V, Jaouen V, Hognon C, et al. Dosimetric
Kersting K. Large pre-trained language models contain validation of a GAN-based pseudo-CT generation for
human-like biases of what is right and wrong to do. Nat MRI-only stereotactic brain radiotherapy. Cancers (Basel).
Mach Intell. 2022;4(3):258-268.
2021;13(5):1082.
doi: 10.1038/s42256-022-00458-8
doi: 10.3390/cancers13051082
21. Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B. 32. Maspero M, Savenije MH, Dinkla AM, et al. Dose evaluation
High-Resolution Image Synthesis with Latent Diffusion of fast synthetic-CT generation using a generative adversarial
Models. In: IEEE/CVF Conference on Computer Vision and network for general pelvis MR-only radiotherapy. Phys Med
Pattern Recognition (CVPR); 2022. p. 10674-10685.
Biol. 2018;63(18):185001.
doi: 10.1109/CVPR52688.2022.01042
doi: 10.1088/1361-6560/aada6d
22. Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic 33. Liao W, Pu Y. Dose-conditioned synthesis of radiotherapy
models. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, dose with auxiliary classifier generative adversarial network.
Lin H, editors. Advances in Neural Information Processing IEEE Access. 2021;9:87972-87981.
Systems. vol. 33. Glasgow: Curran Associates Inc.; 2020.
p. 6840-6851. doi: 10.1109/ACCESS.2021.3089369
23. Gomes C, Dietterich T, Barrett C, et al. Computational 34. Li X, Wang C, Sheng Y, et al. An artificial intelligence-driven
sustainability: Computing for a better world and a agent for real-time head-and-neck IMRT plan generation
sustainable future. Commun ACM. 2019;62(9):56-65. using conditional generative adversarial network (cGAN).
Med Phys. 2021;48(6):2714-2723.
doi: 10.1145/3339399
doi: 10.1002/mp.14770
24. Chatterjee D, Rao S. Computational sustainability: A socio-
technical perspective. ACM Comput Surv. 2021;53(5):1-29. 35. McIntosh C, Purdie TG. Voxel-based dose prediction with
multi-patient atlas selection for automated radiotherapy
doi: 10.1145/3409797
treatment planning. Phys Med Biol. 2017;62(2):415-431.
25. Van Wynsberghe A. Sustainable AI: AI for sustainability and doi: 10.1088/1361-6560/62/2/415
the sustainability of AI. AI Ethics. 2021;1:213-221.
36. Song Y, Hu J, Liu Y, et al. Dose prediction using a deep
doi: 10.1007/s43681-021-00043-6
neural network for accelerated planning of rectal cancer
26. Sorin V, Barash Y, Konen E, Klang E. Creating artificial radiotherapy. Radiother Oncol. 2020;149:111-116.
images for radiology applications using generative doi: 10.1016/j.radonc.2020.05.005
adversarial networks (GANs)-a systematic review. Acad
Radiol. 2020;27(8):1175-1185. 37. Ranjith CP, Puzhakkal N, Arunkrishnan MP, et al. Mean
parotid dose prediction model using machine learning
doi: 10.1016/j.acra.2019.12.024
regression method for intensity-modulated radiotherapy in
27. Liu Y, Lei Y, Wang Y, et al. Evaluation of a deep learning- head and neck cancer. Med Dosim. 2021;46(3):283-288.

Volume 2 Issue 2 (2024) 8 doi: 10.36922/arnm.3523