Research progress on predicting radiation pneumonia based on four-dimensional computed tomography ventilation imaging in lung cancer radiotherapy_Journal of Biomedical Engineering

Authors：

LIU Yuyu ¹ , WANG Li ² , GAO Yanping ² , PAN Xiang ² , YUAN Meifang ² , HE Bingbing ¹ , BAI Han ² ,  LYU Wenbing ¹

1. School of Information Science and Engineering, Yunnan University, Kunming 650500, P. R. China;
2. Department of Radiotherapy, Peking University Cancer Hospital Yunnan/Yunnan Cancer Hospital/The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, P. R. China;

Corresponding author：

Keywords：

Lung cancer; Radiotherapy; Four-dimensional computed tomography ventilation imaging; Lung function; Radiation pneumonitis prediction

DOI：

10.7507/1001-5515.202405008

Video：

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Abstract Full text Figures/Tables Video References Cited by

Lung cancer is the leading cause of cancer-related deaths worldwide. Radiation pneumonitis is a major complication in lung cancer radiotherapy. Four-dimensional computed tomography (4DCT) imaging provides dynamic ventilation information, which is valuable for lung function assessment and radiation pneumonitis prevention. Many methods have been developed to calculate lung ventilation from 4DCT, but a systematic comparison is lacking. Prediction of radiation pneumonitis using 4DCT-based ventilation is still in an early stage, and no comprehensive review exists. This paper presented the first systematic comparison of functional lung ventilation algorithms based on 4DCT over the past 15 years, highlighting their clinical value and limitations. It then reviewed multimodal approaches combining 4DCT ventilation imaging, dose metrics, and clinical data for radiation pneumonitis prediction. Finally, it summarized current research and future directions of 4DCT in lung cancer radiotherapy, offering insights for clinical practice and further studies.

1.	Hagihara M, Kato H, Yamashita M, et al. Lung cancer progression alters lung and gut microbiomes and lipid metabolism. Heliyon, 2023, 10(1): e23509.
2.	Leiter A, Veluswamy R R, Wisnivesky J P. The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol, 2023, 20(9): 624-639.
3.	Liang B, Lu X, Liu L, et al. Synergizing the interaction of single nucleotide polymorphisms with dosiomics features to build a dual-omics model for the prediction of radiation pneumonitis. Radiother Oncol, 2024, 196: 110261.
4.	徐倩文, 朱巍, 郝志强. 热疗在放射性肺炎中的研究进展. 中国现代医生, 2024, 62(1): 111-113.
5.	张瑞军. 肺癌放射治疗所致急性放射性肺炎的临床治疗分析. 中文科技期刊数据库(全文版)医药卫生, 2023(1): 86-89 .
6.	Ren G, Huang Y-H, Zhu J, et al. Medical Image Synthesis. Boca Raton: CRC Press, 2024: 71-88.
7.	Gu J, Qiu Q, Zhu J, et al. Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image. Med Phys, 2023, 50(12): 7779-7790.
8.	Gaudreault M, Korte J, Bucknell N, et al. Comparison of dual-energy CT with positron emission tomography for lung perfusion imaging in patients with non-small cell lung cancer. Phys Med Biol, 2023, 68(3): 035015.
9.	Bohr C. Ueber die lungenathmung. Skand Arch Physiol, 1891, 2(1): 236-268 .
10.	Jeong Y H, Lee H, Jang H J, et al. Predicting postoperative lung function using ventilation SPECT/CT in patients with lung cancer. J Thorac Dis, 2024, 16(2): 1054-1062.
11.	Huang Y S, Chen J L Y, Lan H T, et al. Xenon-enhanced ventilation computed tomography for functional lung avoidance radiation therapy in patients with lung cancer. Int J Radiat Oncol Biol Phys, 2023, 115(2): 356-365.
12.	Greffier J, Villani N, Defez D, et al. Spectral CT imaging: technical principles of dual-energy CT and multi-energy photon-counting CT. Diagn Interv Imaging, 2023, 104(4): 167-177.
13.	Wu Y, Li J, Ding L, et al. Differentiation of pathological subtypes and Ki-67 and TTF-1 expression by dual-energy CT (DECT) volumetric quantitative analysis in non-small cell lung cancer. Cancer Imaging, 2024, 24(1): 146.
14.	Yamamoto T, Kabus S, Bal M, et al. Four-dimensional computed tomography ventilation image-guided lung functional avoidance radiation therapy: A single-arm prospective pilot clinical trial. Int J Radiat Oncol Biol Phys, 2023, 115(5): 1144-1154.
15.	Bouchareb Y, Alsaadi A, Zabah J, et al. Technological advances in SPECT and SPECT/CT imaging. Diagnostics (Basel), 2024, 14(13): 1431.
16.	Aldosary G. Four-dimensional computed tomography (4DCT) in radiation oncology: A practical overview. Curr Radiol Rep, 2024, 12(7): 65-76.
17.	Baschnagel A M, Flakus M J, Wallat E M, et al. A phase 2 randomized clinical trial evaluating 4-dimensional computed tomography ventilation-based functional lung avoidance radiation therapy for non-small cell lung cancer. Int J Radiat Oncol Biol Phys, 2024, 119(5): 1393-1402.
18.	Flakus M J, Kent S P, Wallat E M, et al. Metrics of dose to highly ventilated lung are predictive of radiation-induced pneumonitis in lung cancer patients. Radiother Oncol, 2023, 182: 109553.
19.	Thor M, Lee C, Sun L, et al. An 18F-FDG PET/CT and mean lung dose model to predict early radiation pneumonitis in stage III non-small cell lung cancer patients treated with chemoradiation and immunotherapy. J Nucl Med, 2024, 65(4): 520-526.
20.	Niu L, Chu X, Yang X, et al. A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome. J Cancer Res Clin Oncol, 2023, 149(11): 8923-8934.
21.	Karmali D, Sowho M, Bose S, et al. Functional imaging for assessing regional lung ventilation in preclinical and clinical research. Front Med (Lausanne), 2023, 10: 1160292.
22.	钟文静, 李英, 崔海霞. 基于4DCT的肺功能成像在肺部肿瘤放射治疗中的应用进展. 中华放射肿瘤学杂志, 2023, 32(5): 481-487.
23.	Castillo R, Castillo E, Martinez J, et al. Ventilation from four-dimensional computed tomography: density versus Jacobian methods. Phys Med Biol, 2010, 55(16): 4661-4685.
24.	Du K, Bayouth J E, Cao K, et al. Reproducibility of registration‐based measures of lung tissue expansion. Med Phys, 2012, 39(3): 1595-1608.
25.	Zhang G Q, Zhang S X, Yu H, et al. Three-dimensional pulmonary ventilation imaging based on four-dimensional computed tomography at peak-exhale and peak-inhale phases. Chin J Tissue Eng Res, 2012, 16(52): 9807-9812.
26.	Li M, Castillo E, Zheng X L, et al. Modeling lung deformation: a combined deformable image registration method with spatially varying Young's modulus estimates. Med Phys, 2013, 40(8): 081902.
27.	Kipritidis J, Hofman M S, Siva S, et al. Estimating lung ventilation directly from 4D CT Hounsfield unit values. Med Phys, 2016, 43(1): 33-43.
28.	Hegi‐Johnson F, Keall P, Barber J, et al. Evaluating the accuracy of 4D‐CT ventilation imaging: first comparison with Technegas SPECT ventilation. Med Phys, 2017, 44(8): 4045-4055.
29.	Jafari P, Yaremko B P, Parraga G, et al. 4DCT ventilation map construction using biomechanics-based image registration and enhanced air segmentation// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019: 6263-6266.
30.	Castillo E, Vinogradskiy Y, Castillo R. Robust HU‐based CT ventilation from an integrated mass conservation formulation. Med Phys, 2019, 46(11): 5036-5046.
31.	Szmul A, Matin T, Gleeson F V, et al. Patch-based lung ventilation estimation using multi-layer supervoxels. Comput Med Imaging Graph, 2019, 74: 49-60.
32.	薛鹏. 肺部 4D CT 图像配准及其在呼吸运动估计和通气量估计中的应用研究. 济南: 山东大学, 2021.
33.	Zhong Y, Vinogradskiy Y, Chen L, et al. Technical note: Deriving ventilation imaging from 4DCT by deep convolutional neural network. Med Phys, 2019, 46(5): 2323-2329.
34.	Liu Z, Miao J, Huang P, et al. A deep learning method for producing ventilation images from 4DCT: First comparison with technegas SPECT ventilation. Med Phys, 2020, 47(3): 1249-1257.
35.	Porter E M, Myziuk N K, Quinn T J, et al. Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning. Phys Med Biol, 2021, 66(17): 175005.
36.	Xue P, Fu Y, Zhang J, et al. Effective lung ventilation estimation based on 4D CT image registration and supervoxels. Biomed Signal Proces, 2023, 79(1): 104074.
37.	Chen Z, Huang Y H, Kong F M, et al. A super-voxel-based method for generating surrogate lung ventilation images from CT. Front Physiol, 2023, 14: 1085158.
38.	Hou Z, Kong Y, Wu J, et al. A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning. Jpn J Radiol, 2024, 42(7): 765-776.
39.	Ma P, Chen Z, Huang Y H, et al. Motion and anatomy dual aware lung ventilation imaging by integrating Jacobian map and average CT image using dual path fusion network. Med Phys, 2025, 52(1): 246-256.
40.	Huang Y H, Li Z, Xiong T, et al. Constructing surrogate lung ventilation maps from 4-dimensional computed tomography-derived subregional respiratory dynamics. Int J Radiat Oncol Biol Phys, 2025, 121(5): 1328-1338.
41.	Flakus M J, Wuschner A E, Wallat E M, et al. Quantifying robustness of CT-ventilation biomarkers to image noise. Front Physiol, 2023, 14: 1040028.
42.	Guerrero T, Sanders K, Castillo E, et al. Dynamic ventilation imaging from four-dimensional computed tomography. Phys Med Biol, 2006, 51(4): 777-791.
43.	Reinhardt J M, Ding K, Cao K, et al. Registration-based estimates of local lung tissue expansion compared to xenon CT measures of specific ventilation. Med Image Anal, 2008, 12(6): 752-763.
44.	Vinogradskiy Y, Castillo R, Castillo E, et al. Use of 4-dimensional computed tomography-based ventilation imaging to correlate lung dose and function with clinical outcomes. Int J Radiat Oncol Biol Phys, 2013, 86(2): 366-371.
45.	Patton T J, Gerard S E, Shao W, et al. Quantifying ventilation change due to radiation therapy using 4DCT Jacobian calculations. Med Phys, 2018, 45(10): 4483-4492.
46.	Bai H, Li W, Xia Y, et al. Preliminary study on the effect of 4DCT-ventilation-weighted dose on the radiation induced pneumonia probability (RIPP). Dose Response, 2021, 19(3): 15593258211017753.
47.	王静霄, 胡玲静, 韩文静, 等. 影像组学中特征选择方法的应用与进展. 国际医学放射学杂志, 2024, 47(6): 730-735.
48.	Katsuta Y, Kadoya N, Mouri S, et al. Prediction of radiation pneumonitis with machine learning using 4D-CT based dose-function features. J Radiat Res, 2022, 63(1): 71-79.
49.	Katsuta Y, Kadoya N, Kajikawa T, et al. Radiation pneumonitis prediction model with integrating multiple dose-function features on 4DCT ventilation images. Phys Med, 2023, 105: 102505.
50.	Neupane T, Castillo E, Chen Y, et al. Predicting radiation pneumonitis with robust 4DCT-ventilation and 4DCT-perfusion imaging using prospective lung cancer clinical trial data. Int J Radiat Oncol Biol Phys, 2024, 120(2): S141.
51.	Liu C, Liu H, Li Y, et al. Establishing a 4D-CT lung function related volumetric dose model to reduce radiation pneumonia. Sci Rep, 2024, 14(1): 12589.
52.	Wallat E M, Wuschner A E, Flakus M J, et al. Predicting pulmonary ventilation damage after radiation therapy for nonsmall cell lung cancer using a ResNet generative adversarial network. Med Phys, 2023, 50(5): 3199-3209.
53.	Zhang Z, Wang Z, Luo T, et al. Computed tomography and radiation dose images-based deep-learning model for predicting radiation pneumonitis in lung cancer patients after radiation therapy. Radiother Oncol, 2023, 182: 109581.
54.	Bourbonne V, Da-Ano R, Jaouen V, et al. Radiomics analysis of 3D dose distributions to predict toxicity of radiotherapy for lung cancer. Radiother Oncol, 2021, 155: 144-150.
55.	王郅翔, 王冠杰, 王清鑫, 等. 迁移学习在放射性肺炎预测中的预训练模型微调研究. 数字医学与健康, 2023, 1(2): 102-106.
56.	刘倩倩, 姚升宇, 陈旭明, 等. 4D-CT呼吸信号采集方式对运动肿瘤靶区勾画的影响. 中国辐射卫生, 2023, 32(1): 35-39.

1. Hagihara M, Kato H, Yamashita M, et al. Lung cancer progression alters lung and gut microbiomes and lipid metabolism. Heliyon, 2023, 10(1): e23509.
2. Leiter A, Veluswamy R R, Wisnivesky J P. The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol, 2023, 20(9): 624-639.
3. Liang B, Lu X, Liu L, et al. Synergizing the interaction of single nucleotide polymorphisms with dosiomics features to build a dual-omics model for the prediction of radiation pneumonitis. Radiother Oncol, 2024, 196: 110261.
4. 徐倩文, 朱巍, 郝志强. 热疗在放射性肺炎中的研究进展. 中国现代医生, 2024, 62(1): 111-113.
5. 张瑞军. 肺癌放射治疗所致急性放射性肺炎的临床治疗分析. 中文科技期刊数据库(全文版)医药卫生, 2023(1): 86-89 .
6. Ren G, Huang Y-H, Zhu J, et al. Medical Image Synthesis. Boca Raton: CRC Press, 2024: 71-88.
7. Gu J, Qiu Q, Zhu J, et al. Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image. Med Phys, 2023, 50(12): 7779-7790.
8. Gaudreault M, Korte J, Bucknell N, et al. Comparison of dual-energy CT with positron emission tomography for lung perfusion imaging in patients with non-small cell lung cancer. Phys Med Biol, 2023, 68(3): 035015.
9. Bohr C. Ueber die lungenathmung. Skand Arch Physiol, 1891, 2(1): 236-268 .
10. Jeong Y H, Lee H, Jang H J, et al. Predicting postoperative lung function using ventilation SPECT/CT in patients with lung cancer. J Thorac Dis, 2024, 16(2): 1054-1062.
11. Huang Y S, Chen J L Y, Lan H T, et al. Xenon-enhanced ventilation computed tomography for functional lung avoidance radiation therapy in patients with lung cancer. Int J Radiat Oncol Biol Phys, 2023, 115(2): 356-365.
12. Greffier J, Villani N, Defez D, et al. Spectral CT imaging: technical principles of dual-energy CT and multi-energy photon-counting CT. Diagn Interv Imaging, 2023, 104(4): 167-177.
13. Wu Y, Li J, Ding L, et al. Differentiation of pathological subtypes and Ki-67 and TTF-1 expression by dual-energy CT (DECT) volumetric quantitative analysis in non-small cell lung cancer. Cancer Imaging, 2024, 24(1): 146.
14. Yamamoto T, Kabus S, Bal M, et al. Four-dimensional computed tomography ventilation image-guided lung functional avoidance radiation therapy: A single-arm prospective pilot clinical trial. Int J Radiat Oncol Biol Phys, 2023, 115(5): 1144-1154.
15. Bouchareb Y, Alsaadi A, Zabah J, et al. Technological advances in SPECT and SPECT/CT imaging. Diagnostics (Basel), 2024, 14(13): 1431.
16. Aldosary G. Four-dimensional computed tomography (4DCT) in radiation oncology: A practical overview. Curr Radiol Rep, 2024, 12(7): 65-76.
17. Baschnagel A M, Flakus M J, Wallat E M, et al. A phase 2 randomized clinical trial evaluating 4-dimensional computed tomography ventilation-based functional lung avoidance radiation therapy for non-small cell lung cancer. Int J Radiat Oncol Biol Phys, 2024, 119(5): 1393-1402.
18. Flakus M J, Kent S P, Wallat E M, et al. Metrics of dose to highly ventilated lung are predictive of radiation-induced pneumonitis in lung cancer patients. Radiother Oncol, 2023, 182: 109553.
19. Thor M, Lee C, Sun L, et al. An 18F-FDG PET/CT and mean lung dose model to predict early radiation pneumonitis in stage III non-small cell lung cancer patients treated with chemoradiation and immunotherapy. J Nucl Med, 2024, 65(4): 520-526.
20. Niu L, Chu X, Yang X, et al. A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome. J Cancer Res Clin Oncol, 2023, 149(11): 8923-8934.
21. Karmali D, Sowho M, Bose S, et al. Functional imaging for assessing regional lung ventilation in preclinical and clinical research. Front Med (Lausanne), 2023, 10: 1160292.
22. 钟文静, 李英, 崔海霞. 基于4DCT的肺功能成像在肺部肿瘤放射治疗中的应用进展. 中华放射肿瘤学杂志, 2023, 32(5): 481-487.
23. Castillo R, Castillo E, Martinez J, et al. Ventilation from four-dimensional computed tomography: density versus Jacobian methods. Phys Med Biol, 2010, 55(16): 4661-4685.
24. Du K, Bayouth J E, Cao K, et al. Reproducibility of registration‐based measures of lung tissue expansion. Med Phys, 2012, 39(3): 1595-1608.
25. Zhang G Q, Zhang S X, Yu H, et al. Three-dimensional pulmonary ventilation imaging based on four-dimensional computed tomography at peak-exhale and peak-inhale phases. Chin J Tissue Eng Res, 2012, 16(52): 9807-9812.
26. Li M, Castillo E, Zheng X L, et al. Modeling lung deformation: a combined deformable image registration method with spatially varying Young's modulus estimates. Med Phys, 2013, 40(8): 081902.
27. Kipritidis J, Hofman M S, Siva S, et al. Estimating lung ventilation directly from 4D CT Hounsfield unit values. Med Phys, 2016, 43(1): 33-43.
28. Hegi‐Johnson F, Keall P, Barber J, et al. Evaluating the accuracy of 4D‐CT ventilation imaging: first comparison with Technegas SPECT ventilation. Med Phys, 2017, 44(8): 4045-4055.
29. Jafari P, Yaremko B P, Parraga G, et al. 4DCT ventilation map construction using biomechanics-based image registration and enhanced air segmentation// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019: 6263-6266.
30. Castillo E, Vinogradskiy Y, Castillo R. Robust HU‐based CT ventilation from an integrated mass conservation formulation. Med Phys, 2019, 46(11): 5036-5046.
31. Szmul A, Matin T, Gleeson F V, et al. Patch-based lung ventilation estimation using multi-layer supervoxels. Comput Med Imaging Graph, 2019, 74: 49-60.
32. 薛鹏. 肺部 4D CT 图像配准及其在呼吸运动估计和通气量估计中的应用研究. 济南: 山东大学, 2021.
33. Zhong Y, Vinogradskiy Y, Chen L, et al. Technical note: Deriving ventilation imaging from 4DCT by deep convolutional neural network. Med Phys, 2019, 46(5): 2323-2329.
34. Liu Z, Miao J, Huang P, et al. A deep learning method for producing ventilation images from 4DCT: First comparison with technegas SPECT ventilation. Med Phys, 2020, 47(3): 1249-1257.
35. Porter E M, Myziuk N K, Quinn T J, et al. Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning. Phys Med Biol, 2021, 66(17): 175005.
36. Xue P, Fu Y, Zhang J, et al. Effective lung ventilation estimation based on 4D CT image registration and supervoxels. Biomed Signal Proces, 2023, 79(1): 104074.
37. Chen Z, Huang Y H, Kong F M, et al. A super-voxel-based method for generating surrogate lung ventilation images from CT. Front Physiol, 2023, 14: 1085158.
38. Hou Z, Kong Y, Wu J, et al. A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning. Jpn J Radiol, 2024, 42(7): 765-776.
39. Ma P, Chen Z, Huang Y H, et al. Motion and anatomy dual aware lung ventilation imaging by integrating Jacobian map and average CT image using dual path fusion network. Med Phys, 2025, 52(1): 246-256.
40. Huang Y H, Li Z, Xiong T, et al. Constructing surrogate lung ventilation maps from 4-dimensional computed tomography-derived subregional respiratory dynamics. Int J Radiat Oncol Biol Phys, 2025, 121(5): 1328-1338.
41. Flakus M J, Wuschner A E, Wallat E M, et al. Quantifying robustness of CT-ventilation biomarkers to image noise. Front Physiol, 2023, 14: 1040028.
42. Guerrero T, Sanders K, Castillo E, et al. Dynamic ventilation imaging from four-dimensional computed tomography. Phys Med Biol, 2006, 51(4): 777-791.
43. Reinhardt J M, Ding K, Cao K, et al. Registration-based estimates of local lung tissue expansion compared to xenon CT measures of specific ventilation. Med Image Anal, 2008, 12(6): 752-763.
44. Vinogradskiy Y, Castillo R, Castillo E, et al. Use of 4-dimensional computed tomography-based ventilation imaging to correlate lung dose and function with clinical outcomes. Int J Radiat Oncol Biol Phys, 2013, 86(2): 366-371.
45. Patton T J, Gerard S E, Shao W, et al. Quantifying ventilation change due to radiation therapy using 4DCT Jacobian calculations. Med Phys, 2018, 45(10): 4483-4492.
46. Bai H, Li W, Xia Y, et al. Preliminary study on the effect of 4DCT-ventilation-weighted dose on the radiation induced pneumonia probability (RIPP). Dose Response, 2021, 19(3): 15593258211017753.
47. 王静霄, 胡玲静, 韩文静, 等. 影像组学中特征选择方法的应用与进展. 国际医学放射学杂志, 2024, 47(6): 730-735.
48. Katsuta Y, Kadoya N, Mouri S, et al. Prediction of radiation pneumonitis with machine learning using 4D-CT based dose-function features. J Radiat Res, 2022, 63(1): 71-79.
49. Katsuta Y, Kadoya N, Kajikawa T, et al. Radiation pneumonitis prediction model with integrating multiple dose-function features on 4DCT ventilation images. Phys Med, 2023, 105: 102505.
50. Neupane T, Castillo E, Chen Y, et al. Predicting radiation pneumonitis with robust 4DCT-ventilation and 4DCT-perfusion imaging using prospective lung cancer clinical trial data. Int J Radiat Oncol Biol Phys, 2024, 120(2): S141.
51. Liu C, Liu H, Li Y, et al. Establishing a 4D-CT lung function related volumetric dose model to reduce radiation pneumonia. Sci Rep, 2024, 14(1): 12589.
52. Wallat E M, Wuschner A E, Flakus M J, et al. Predicting pulmonary ventilation damage after radiation therapy for nonsmall cell lung cancer using a ResNet generative adversarial network. Med Phys, 2023, 50(5): 3199-3209.
53. Zhang Z, Wang Z, Luo T, et al. Computed tomography and radiation dose images-based deep-learning model for predicting radiation pneumonitis in lung cancer patients after radiation therapy. Radiother Oncol, 2023, 182: 109581.
54. Bourbonne V, Da-Ano R, Jaouen V, et al. Radiomics analysis of 3D dose distributions to predict toxicity of radiotherapy for lung cancer. Radiother Oncol, 2021, 155: 144-150.
55. 王郅翔, 王冠杰, 王清鑫, 等. 迁移学习在放射性肺炎预测中的预训练模型微调研究. 数字医学与健康, 2023, 1(2): 102-106.
56. 刘倩倩, 姚升宇, 陈旭明, 等. 4D-CT呼吸信号采集方式对运动肿瘤靶区勾画的影响. 中国辐射卫生, 2023, 32(1): 35-39.

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Latest ArticlesResearch progress on predicting radiation pneumonia based on four-dimensional computed tomography ventilation imaging in lung cancer radiotherapy

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