CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
1.1. Radiotherapy
1.2. CBCT Image Quality Improvement
1.3. CT Synthesis Using Deep Learning
1.4. Study Objectives
2. Materials and Methods
2.1. Clinical Dataset
2.2. Data Preparation
2.3. Registration Network
2.4. Pix2Pix
2.5. CycleGAN
2.6. UNIT
2.7. Network Training
2.8. Evaluation Procedures
3. Results
3.1. Image Quality Evaluation with Quantitative Metrics
3.2. Image Quality Evaluation in Preserving Anatomy
3.3. Patient-Specific Differences
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristics | All | Training | Validation | Test |
---|---|---|---|---|
Patients (N) | 146 | 95 | 21 | 30 |
Sex (N) | ||||
− Male | 108 | 68 | 18 | 22 |
− Female | 38 | 27 | 3 | 8 |
Age (Mean ± SD [Range]) | 54.3 ± 15.2 | 53.3 ± 15.1 | 58.4 ± 15.3 | 54.7 ± 15.4 |
[11–89] | [11–87] | [23–85] | [22–89] | |
Body Mass Index (Mean ± SD [Range]) | 21.7 ± 4.4 | 22.2 ± 4.3 | 20.2 ± 3.9 | 21.1 ± 4.8 |
[12.7–37.9] | [12.7–34.0] | [13.9–30.5] | [15.6–37.9] | |
Diagnosis site (N) | ||||
− Oral cavity | 41 | 25 | 8 | 8 |
− Nasopharynx | 49 | 41 | 5 | 3 |
− Oropharynx | 15 | 6 | 3 | 6 |
− Hypopharynx | 5 | 2 | 1 | 2 |
− Larynx | 10 | 4 | 1 | 5 |
− Nasal cavity and paranasal sinuses | 18 | 9 | 3 | 6 |
− Salivary gland | 3 | 3 | - | - |
− Others | 5 | 5 | - | - |
Pathology results (N) | ||||
− Squamous cell carcinoma | 132 | 81 | 21 | 30 |
− Others | 14 | 14 | − | − |
TNM classification | ||||
− Primary tumours | ||||
∘ T1 | 14 | 11 | 2 | 1 |
∘ T2 | 28 | 19 | 4 | 5 |
∘ T3 | 46 | 27 | 7 | 12 |
∘ T4 | 58 | 38 | 8 | 12 |
− Regional lymph nodes | ||||
∘ N0 | 30 | 21 | 1 | 8 |
∘ N1 | 21 | 15 | 3 | 3 |
∘ N2 | 65 | 38 | 12 | 15 |
∘ N3 | 30 | 21 | 5 | 4 |
− Metastasis | ||||
∘ M0 | 146 | 95 | 21 | 30 |
∘ M1 | − | − | − | − |
Treatment | ||||
− Concurrent chemoradiotherapy | 108 | 59 | 20 | 29 |
− Post-operative radiotherapy | 26 | 26 | - | - |
− Definitive radiotherapy | 12 | 10 | 1 | 1 |
Period between pCT and CBCT in days | 16.7 ± 5.4 | 16.7± 5.4 | 16.3 ± 5.1 | 16.8 ± 5.8 |
(Mean ± SD [Range]) | [4–31] | [4–31] | [7–30] | [5–29] |
Comparison | MAE | RMSE | PSNR | SSIM |
---|---|---|---|---|
CBCT vs. pCT | 58.16 ± 25.17 | 160.40 ± 46.08 | 26.06 ± 2.44 | 0.8152 ± 0.0859 |
CBCT vs. dCT | 55.78 ± 26.15 | 148.32 ± 51.36 | 26.89 ± 2.93 | 0.8168 ± 0.0876 |
pCT vs. dCT | 35.84 ± 16.21 | 118.94 ± 39.30 | 28.76 ± 2.80 | 0.8938 ± 0.0483 |
Model | Target | RegNet | MAE | RMSE | PSNR | SSIM |
---|---|---|---|---|---|---|
Pix2Pix | pCT | No | 43.39 ± 14.43 | 134.33 ± 31.40 | 27.48 ± 1.99 | 0.8479 ± 0.0542 |
pCT | Yes | 41.62 ± 13.69 | 132.00 ± 34.96 | 27.71 ± 2.32 | 0.8578 ± 0.0513 | |
dCT | No | 43.34 ± 14.81 | 133.32 ± 33.68 | 27.60 ± 2.23 | 0.8566 ± 0.0515 | |
dCT | Yes | 40.46 ± 13.55 | 124.07 ± 31.22 | 28.20 ± 2.12 | 0.8635 ± 0.0467 | |
CycleGAN | pCT | No | ||||
pCT | Yes | |||||
dCT | No | 43.87 ± 15.23 | 139.85 ± 35.30 | 27.18 ± 2.20 | 0.8527 ± 0.0473 | |
dCT | Yes | 41.44 ± 15.53 | 124.67 ± 34.43 | 28.22 ± 2.38 | 0.8597 ± 0.0591 | |
UNIT | pCT | No | ||||
pCT | Yes | |||||
dCT | No | 40.46 ± 16.21 | 119.45 ± 37.28 | 28.67 ± 2.62 | 0.8630 ± 0.0527 | |
dCT | Yes | 37.21 ± 16.51 | 108.86 ± 38.13 | 29.55 ± 2.82 | 0.8791 ± 0.0547 |
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Suwanraksa, C.; Bridhikitti, J.; Liamsuwan, T.; Chaichulee, S. CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer. Cancers 2023, 15, 2017. https://doi.org/10.3390/cancers15072017
Suwanraksa C, Bridhikitti J, Liamsuwan T, Chaichulee S. CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer. Cancers. 2023; 15(7):2017. https://doi.org/10.3390/cancers15072017
Chicago/Turabian StyleSuwanraksa, Chitchaya, Jidapa Bridhikitti, Thiansin Liamsuwan, and Sitthichok Chaichulee. 2023. "CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer" Cancers 15, no. 7: 2017. https://doi.org/10.3390/cancers15072017
APA StyleSuwanraksa, C., Bridhikitti, J., Liamsuwan, T., & Chaichulee, S. (2023). CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer. Cancers, 15(7), 2017. https://doi.org/10.3390/cancers15072017