Methods for Enhancing Image Quality of Soft Tissue Regions in synthetic CT Based on cone-beam CT_Journal of Biomedical Engineering

Authors：

FU Ziwei ^1,2 , ZHU Yechen ^1,2 ,  ZHANG Zijian ³ ,  GAO Xin ^2,4

1. Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei 230026, P. R. China;
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China;
3. Xiangya Hospital, Central South University, Changsha 410008, P. R. China;
4. Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Jinan 250101, P. R. China;

Corresponding author：

ZHANG Zijian, Email: wanzzj@csu.edu.cn; GAO Xin, Email: xingaosam@163.com

Keywords：

Adaptive radiation therapy; Cone-beam CT; Synthetic CT; Multi-task learning

DOI：

10.7507/1001-5515.202407078

Video：

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Abstract Full text Figures/Tables Video References Cited by

Synthetic CT (sCT) generated from CBCT has proven effective in artifact reduction and CT number correction, facilitating precise radiation dose calculation. However, the quality of different regions in sCT images is severely imbalanced, with soft tissue region exhibiting notably inferior quality compared to others. To address this imbalance, we proposed a Multi-Task Attention Network (MuTA-Net) based on VGG-16, specifically focusing the enhancement of image quality in soft tissue region of sCT. First, we introduced a multi-task learning strategy that divides the sCT generation task into three sub-tasks: global image generation, soft tissue region generation and bone region segmentation. This approach ensured the quality of overall sCT image while enhancing the network’s focus on feature extraction and generation for soft tissues region. The result of bone region segmentation task guided the fusion of sub-tasks results. Then, we designed an attention module to further optimize feature the network’s extraction capabilities. Finally, by employing a results fusion module, the results of three sub-tasks were integrated, generating a high-quality sCT image. Experimental results on head and neck CBCT demonstrated that the sCT images generated by the proposed MuTA-Net exhibited a 12.52% reduction in mean absolute error in soft tissue region, compared to the best performance among the three comparative methods, including ResNet, U-Net, and U-Net++. It can be seen that MuTA-Net is suitable for high-quality sCT image generation and has potential application value in the field of CBCT guided adaptive radiation therapy.

1.	Jennifer D L S, Popple R, Agazaryan N, et al. Image guided radiation therapy (IGRT) technologies for radiation therapy localization and delivery. Int J Radiat Oncol Biol Phys, 2013, 87(1): 33-45.
2.	Boldrini L, D’Aviero A, De Felice F. et al. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). Radiol med, 2024, 129(1): 133-151.
3.	Morgan H E, Sher D J. Adaptive radiotherapy for head and neck cancer. Cancers Head Neck. 2020, 5: 1.
4.	Castelli J, Simon A, Lafond C, et al. Adaptive radiotherapy for head and neck cancer. Acta Oncol, 2018, 57(10): 1284-1292.
5.	中华医学会放射肿瘤治疗学分会放疗技术学组, 中国医师协会医学技师专业委员会, 林承光, 等. CT模拟定位技术临床操作指南中国专家共识(2021版). 中华放射肿瘤学杂志, 2021, 30(6): 535-542.
6.	Siewerdsen J H, Jaffray D A. Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter. Med Phys, 2001, 28(2): 220-231.
7.	Jarry G, Graham S A, Moseley D J, et al. Characterization of scattered radiation in kV CBCT images using Monte Carlo simulations. Med Phys, 2006, 33(11): 4320-4329.
8.	Richter A, Hu Q, Steglich D, et al. Investigation of the usability of cone beam CT data sets for dose calculation. Radiat Oncol, 2008, 3: 42.
9.	Yang Y, Schreibmann E, Li T, et al. Evaluation of on-board kV cone beam CT (CBCT) based dose calculation. Phys Med Biol, 2007, 52(3): 685-705.
10.	Yoo S, Yin F F. Dosimetric feasibility of cone-beam CT-based treatment planning compared to CT-based treatment planning. Int J Radiat Oncol Biol Phys, 2006, 66(5): 1553-1561.
11.	Schulze R, Heil U, Groß D, et al. Artefacts in CBCT: a review. Dentomaxillofac Rad, 2011, 40(5): 265-273.
12.	Meroni S, Mongioj V, Giandini T, et al. EP-1822: limits and potentialities of the use of CBCT for dose calculation in adaptive radiotherapy. Radiother Oncol, 2016, 119: S854-S855.
13.	Elstrøm U V, Olsen S R K, Muren L P, et al. The impact of CBCT reconstruction and calibration for radiotherapy planning in the head and neck region - a phantom study. Acta Oncol, 2014, 53(8): 1114-1124.
14.	Yu B, Wang Y, Wang L, et al. Medical image synthesis via deep learning. Adv Exp Med Biol, 2020, 1213: 23-44.
15.	Duan L, Ni X, Liu Q, et al. Unsupervised learning for deformable registration of thoracic CT and cone-beam CT based on multiscale features matching with spatially adaptive weighting. Med Phys, 2020, 47(11): 5632-5647.
16.	Liang X, Bibault J, Leroy T, et al. Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning. Med Phys, 2021, 48(4): 1764-1770.
17.	Wang H, Liu X, Kong L, et al. Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy. Strahlenther Onkol, 2023, 199(5): 485-497.
18.	Liu Y, Lei Y, Wang T, et al. CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy. Med Phys, 2020, 47(6): 2472-2483.
19.	Cao X, Yang J, Gao Y, et al. Dual-core steered non-rigid registration for multi-modal images via bi-directional image synthesis. Med Image Anal, 2017, 41: 18-31.
20.	Yang B, Chang Y, Liang Y, et al. A comparison study between CNN-based deformed planning CT and CycleGAN-based synthetic CT methods for improving iCBCT image quality. Front Oncol, 2022, 12: 896795.
21.	Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation// International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Munich: Springer, 2015: 234-241.
22.	Zhu J, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks// International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 2242-2251.
23.	Chen L, Liang X, Shen C, et al. Synthetic CT generation from CBCT images via deep learning. Med Phys, 2020, 47(3): 1115-1125.
24.	Liang X, Chen L, Nguyen D, et al. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy. Phys Med Biol, 2019, 64(12): 125002.
25.	Yuan N, Rao S, Chen Q, et al. Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following Image Gently Protocol using deep neural network. Med Phys, 2022, 49(5): 3263-3277.
26.	Liu Y, Liao S, Zhu Y, et al. Channel-spatial attention guided CycleGAN for CBCT-based synthetic CT generation to enable adaptive radiotherapy. IEEE T Comput Imag, 2024, 10: 818-831.
27.	Crawshaw M. Multi-task learning with deep neural networks: A survey. arXiv preprint arXiv, 2020: 2009.09796.
28.	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
29.	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 2014: 1409.1556.
30.	Wu Y, He K. Group normalization// Proceedings of the European Conference on Computer Vision (ECCV). Munich: Springer, 2018: 3-19.
31.	Tong T, Li G, Liu X, et al. Image super-resolution using dense skip connections// Proceedings of the IEEE International Conference on Computer Vision(ICCV). Venice: IEEE, 2017: 4799-4807.
32.	Cao Z, Gao X, Chang Y, et al. Improving synthetic CT accuracy by combining the benefits of multiple normalized preprocesses. J Appl Clin Med Phys, 2023, 24(8): e14004.

1. Jennifer D L S, Popple R, Agazaryan N, et al. Image guided radiation therapy (IGRT) technologies for radiation therapy localization and delivery. Int J Radiat Oncol Biol Phys, 2013, 87(1): 33-45.
2. Boldrini L, D’Aviero A, De Felice F. et al. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). Radiol med, 2024, 129(1): 133-151.
3. Morgan H E, Sher D J. Adaptive radiotherapy for head and neck cancer. Cancers Head Neck. 2020, 5: 1.
4. Castelli J, Simon A, Lafond C, et al. Adaptive radiotherapy for head and neck cancer. Acta Oncol, 2018, 57(10): 1284-1292.
5. 中华医学会放射肿瘤治疗学分会放疗技术学组, 中国医师协会医学技师专业委员会, 林承光, 等. CT模拟定位技术临床操作指南中国专家共识(2021版). 中华放射肿瘤学杂志, 2021, 30(6): 535-542.
6. Siewerdsen J H, Jaffray D A. Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter. Med Phys, 2001, 28(2): 220-231.
7. Jarry G, Graham S A, Moseley D J, et al. Characterization of scattered radiation in kV CBCT images using Monte Carlo simulations. Med Phys, 2006, 33(11): 4320-4329.
8. Richter A, Hu Q, Steglich D, et al. Investigation of the usability of cone beam CT data sets for dose calculation. Radiat Oncol, 2008, 3: 42.
9. Yang Y, Schreibmann E, Li T, et al. Evaluation of on-board kV cone beam CT (CBCT) based dose calculation. Phys Med Biol, 2007, 52(3): 685-705.
10. Yoo S, Yin F F. Dosimetric feasibility of cone-beam CT-based treatment planning compared to CT-based treatment planning. Int J Radiat Oncol Biol Phys, 2006, 66(5): 1553-1561.
11. Schulze R, Heil U, Groß D, et al. Artefacts in CBCT: a review. Dentomaxillofac Rad, 2011, 40(5): 265-273.
12. Meroni S, Mongioj V, Giandini T, et al. EP-1822: limits and potentialities of the use of CBCT for dose calculation in adaptive radiotherapy. Radiother Oncol, 2016, 119: S854-S855.
13. Elstrøm U V, Olsen S R K, Muren L P, et al. The impact of CBCT reconstruction and calibration for radiotherapy planning in the head and neck region - a phantom study. Acta Oncol, 2014, 53(8): 1114-1124.
14. Yu B, Wang Y, Wang L, et al. Medical image synthesis via deep learning. Adv Exp Med Biol, 2020, 1213: 23-44.
15. Duan L, Ni X, Liu Q, et al. Unsupervised learning for deformable registration of thoracic CT and cone-beam CT based on multiscale features matching with spatially adaptive weighting. Med Phys, 2020, 47(11): 5632-5647.
16. Liang X, Bibault J, Leroy T, et al. Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning. Med Phys, 2021, 48(4): 1764-1770.
17. Wang H, Liu X, Kong L, et al. Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy. Strahlenther Onkol, 2023, 199(5): 485-497.
18. Liu Y, Lei Y, Wang T, et al. CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy. Med Phys, 2020, 47(6): 2472-2483.
19. Cao X, Yang J, Gao Y, et al. Dual-core steered non-rigid registration for multi-modal images via bi-directional image synthesis. Med Image Anal, 2017, 41: 18-31.
20. Yang B, Chang Y, Liang Y, et al. A comparison study between CNN-based deformed planning CT and CycleGAN-based synthetic CT methods for improving iCBCT image quality. Front Oncol, 2022, 12: 896795.
21. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation// International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Munich: Springer, 2015: 234-241.
22. Zhu J, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks// International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 2242-2251.
23. Chen L, Liang X, Shen C, et al. Synthetic CT generation from CBCT images via deep learning. Med Phys, 2020, 47(3): 1115-1125.
24. Liang X, Chen L, Nguyen D, et al. Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy. Phys Med Biol, 2019, 64(12): 125002.
25. Yuan N, Rao S, Chen Q, et al. Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following Image Gently Protocol using deep neural network. Med Phys, 2022, 49(5): 3263-3277.
26. Liu Y, Liao S, Zhu Y, et al. Channel-spatial attention guided CycleGAN for CBCT-based synthetic CT generation to enable adaptive radiotherapy. IEEE T Comput Imag, 2024, 10: 818-831.
27. Crawshaw M. Multi-task learning with deep neural networks: A survey. arXiv preprint arXiv, 2020: 2009.09796.
28. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
29. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 2014: 1409.1556.
30. Wu Y, He K. Group normalization// Proceedings of the European Conference on Computer Vision (ECCV). Munich: Springer, 2018: 3-19.
31. Tong T, Li G, Liu X, et al. Image super-resolution using dense skip connections// Proceedings of the IEEE International Conference on Computer Vision(ICCV). Venice: IEEE, 2017: 4799-4807.
32. Cao Z, Gao X, Chang Y, et al. Improving synthetic CT accuracy by combining the benefits of multiple normalized preprocesses. J Appl Clin Med Phys, 2023, 24(8): e14004.

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