Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer

• 발행기관: 대한방사선종양학회
• 저자명: Chirasak Khamfongkhruea Tipaporn Prakarnpilas Sangutid Thongsawad Aphisara Deeharing Thananya Chanpanya Thunpisit Mundee Pattarakan Suwanbut Kampheang Nimjaroen
• 간행물 정보: 『대한방사선종양학회지』제42권 제3호, 181~191쪽, 전체 11쪽
• 주제분류: 의약학 > 종양학
• 파일형태: PDF
• 발행일자: 2024.09.30

국문 초록

Purpose: To generate and investigate a supervised deep learning algorithm for creating synthetic computed tomography (sCT) images from kilovoltage cone-beam CT (kV-CBCT) images for adaptive radiation therapy (ART) in head and neck cancer (HNC). Materials and Methods: This study generated the supervised U-Net deep learning model using 3,491 image pairs from planning CT (pCT) and kV-CBCT datasets obtained from 40 HNC patients. The dataset was split into 80% for training and 20% for testing. The evaluation of the sCT images compared to pCT images focused on three aspects: Hounsfield units accuracy, assessed using mean absolute error (MAE) and root mean square error (RMSE); image quality, evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) between sCT and pCT images; and dosimetric accuracy, encompassing 3D gamma passing rates for dose distribution and percentage dose difference. Results: MAE, RMSE, PSNR, and SSIM showed improvements from their initial values of 53.15 ± 40.09, 153.99 ± 79.78, 47.91 ± 4.98 dB, and 0.97 ± 0.02 to 41.47 ± 30.59, 130.39 ± 78.06, 49.93 ± 6.00 dB, and 0.98 ± 0.02, respectively. Regarding dose evaluation, 3D gamma passing rates for dose distribution within sCT images under 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria, yielded passing rates of 92.1% ± 3.8%, 93.8% ± 3.0%, and 96.9% ± 2.0%, respectively. The sCT images exhibited minor variations in the percentage dose distribution of the investigated target and structure volumes. However, it is worth noting that the sCT images exhibited anatomical variations when compared to the pCT images. Conclusion: These findings highlight the potential of the supervised U-Net deep learningmodel in generating kV-CBCT-based sCT images for ART in patients with HNC.

목차

Introduction

Materials and Methods

Results

Discussion and Conclusion

Statement of Ethics

Conflict of Interest

Acknowledgment

Funding

Author Contributions

Data Availability Statement

References

키워드

Cone-beam computed tomography Synthetic CT Deep learning Supervised U-Net Adaptive radiation therapy Head and neck cancer