1 |
吴伟伟, 李韶今, 尹 慧, 等. 局部晚期鼻咽癌调强放疗中解剖结构改变及剂量分布变化研究[J]. 中华放射医学与防护杂志, 2017, 37(11): 826-31.
|
2 |
Zhao SH, Han J, Yang ZY, et al. Anatomical and dosimetric variations during volumetric modulated arc therapy in patients with locally advanced nasopharyngeal carcinoma after induction therapy: implications for adaptive radiation therapy[J]. Clin Transl Radiat Oncol, 2024, 49: 100861.
|
3 |
Liu YZ, Lei Y, Wang TH, et al. CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy[J]. Med Phys, 2020, 47(6): 2472-83.
|
4 |
Liang X, Chen LY, Nguyen D, et al. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy[J]. Phys Med Biol, 2019, 64(12): 125002.
|
5 |
Chen LY, Liang X, Shen CY, et al. Synthetic CT generation from CBCT images via deep learning[J]. Med Phys, 2020, 47(3): 1115-25.
|
6 |
全科润, 程品晶, 陈榕钦, 等. 基于循环生成对抗网络的鼻咽癌CBCT图像修正[J]. 中国医学物理学杂志, 2021, 38(5): 582-6.
|
7 |
亓孟科, 李永宝, 吴艾茜, 等. 基于生成对抗网络的鼻咽癌患者伪CT合成方法研究[J]. 中华放射肿瘤学杂志, 2020, 29(4): 267-72.
|
8 |
Hansen DC, Landry G, Kamp F, et al. ScatterNet: a convolutional neural network for cone-beam CT intensity correction[J]. Med Phys, 2018, 45(11): 4916-26.
|
9 |
周 琼, 李永武, 王 奇, 等. 基于形变配准和伪CT的鼻咽癌自适应放疗剂量评估[J]. 中国医学物理学杂志, 2019, 36(8): 892-7.
|
10 |
Rusanov B, Hassan GM, Reynolds M, et al. Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: a systematic review[J]. Med Phys, 2022, 49(9): 6019-54.
|
11 |
Kida S, Kaji, Nawa K, et al. Visual enhancement of Cone-beam CT by use of CycleGAN[J]. Med Phys, 2020, 47(3): 998-1010.
|
12 |
Liu JW, Yan H, Cheng HL, et al. CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation[J]. Quant Imaging Med Surg, 2021, 11(12): 4820-34.
|
13 |
Wang TH, Lei Y, Fu YB, et al. Machine learning in quantitative PET: a review of attenuation correction and low-count image reconstruction methods[J]. Phys Med, 2020, 76: 294-306.
|
14 |
潘 丹, 贾龙飞, 曾 安. 生成式对抗网络在医学图像处理中的应用[J]. 生物医学工程学杂志, 2018, 35(6): 970-6.
|
15 |
Ronneberger O. Invited talk: U-net convolutional networks for biomedical image segmentation[M]//Informatik aktuell. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017: 3.
|
16 |
He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 27-30, 2016. Las Vegas, NV, USA. IEEE, 2016: 770-8.
|
17 |
Isola P, Zhu JY, Zhou T, et al. Image-to-image translation with conditional adversarial networks[J]. IEEE TPAMI, 2021, 43(12): 4254-67.
|
18 |
Wang R, Wu ZX, Weng ZJ, et al. Cross-domain contrastive learning for unsupervised domain adaptation[J]. IEEE Trans Multimedia, 2023, 25: 1665-73.
|
19 |
Wang Z, Bovik AC, Sheikh HR, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4): 600-12.
|
20 |
Galić I, Habijan M, Leventić H, et al. Machine learning empowering personalized medicine: a comprehensive review of medical image analysis methods[J]. Electronics, 2023, 12(21): 4411.
|
21 |
Huynh E, Hosny A, Guthier C, et al. Artificial intelligence in radiation oncology[J]. Nat Rev Clin Oncol, 2020, 17(12): 771-81.
|
22 |
Wendling M, Morrow A, Hoggarth M, et al. An efficient protocol for radiotherapy quality control with machine learning[J]. Med Phys, 2020, 47(4): 1526-34.
|
23 |
Lei Y, Tang XY, Higgins K, et al. Learning-based CBCT correction using alternating random forest based on auto-context model[J]. Med Phys, 2019, 46(2): 601-18.
|
24 |
Jiang J, Sharfo AWM, Mak RH, et al. Development and validation of an MRI‐only synthetic CT generation method using cycle‐consistent generative adversarial networks for prostate radiotherapy[J]. Med Phys, 2021, 48(1): 416-29.
|
25 |
Thummerer A, Zaffino P, Meijers A, et al. Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy[J]. Phys Med Biol, 2020, 65(9): 095002.
|
26 |
Pulliam KB, Huang JY, Howell RM, et al. Comparison of 2D and 3D gamma analyses[J]. Med Phys, 2014, 41(2): 021710.
|
27 |
孙鸿飞, 倪昕晔, 杨建华. 基于深度学习方法的伪CT图像合成技术研究及在放疗中的应用进展[J]. 中华放射医学与防护杂志, 2021, 41(3): 222-8.
|
28 |
Lei Y, Harms J, Wang TH, et al. MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks[J]. Med Phys, 2019, 46(8): 3565-81.
|
29 |
Liu Y, Lei Y, Wang Y, et al. Evaluation of a deep learning-based synthetic CT generation method for MRI-only breast radiotherapy[J]. Phys Med Biol, 2020, 65(8): 085020.
|
30 |
Maspero M, Savenije MHF, Dinkla AM, et al. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy[J]. Phys Med Biol, 2018, 63(18): 185001.
|
31 |
Kim J, Park S, Yu H, et al. Deep learning-based synthetic CT generation from MR images for PET attenuation correction: A systematic review and meta-analysis[J]. IEEE T Radiat Plasma, 2022, 6(3): 273-287.
|
32 |
Sonke JJ, Aznar M, Rasch C. Adaptive radiotherapy for anatomical changes[J]. Semin Radiat Oncol, 2019, 29(3): 245-57.
|
33 |
Dona Lemus OM, Cao MS, Cai B, et al. Adaptive radiotherapy: next-generation radiotherapy[J]. Cancers, 2024, 16(6): 1206.
|