CBCT‐Based synthetic CT image generation using conditional denoising diffusion probabilistic model

• Published in: Medical physics (Lancaster) Vol. 51; no. 3; pp. 1847 - 1859
• Main Authors: Peng, Junbo, Qiu, Richard L. J., Wynne, Jacob F., Chang, Chih‐Wei, Pan, Shaoyan, Wang, Tonghe, Roper, Justin, Liu, Tian, Patel, Pretesh R., Yu, David S., Yang, Xiaofeng
• Format: Journal Article
• Language: English
• Published: United States 01.03.2024
• Subjects: Bisacodyl - analogs & derivatives; CBCT; Cone-Beam Computed Tomography; diffusion model; Humans; Image Processing, Computer-Assisted - methods; Models, Statistical; Radiotherapy Planning, Computer-Assisted - methods; Spiral Cone-Beam Computed Tomography; synthetic CT; Tomography, X-Ray Computed; synthetic CT; diffusion model; CBCT
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1002/mp.16704

Background Daily or weekly cone‐beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image‐guided radiotherapy (IGRT) process, making it an ideal option for adaptive radiotherapy (ART) replanning. However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for quantitative applications such as organ segmentation and dose calculation. To enable the clinical practice of online ART, it is crucial to obtain CBCT scans with a quality comparable to that of a CT scan. Purpose This work aims to develop a conditional diffusion model to perform image translation from the CBCT to the CT distribution for the image quality improvement of CBCT. Methods The proposed method is a conditional denoising diffusion probabilistic model (DDPM) that utilizes a time‐embedded U‐net architecture with residual and attention blocks to gradually transform the white Gaussian noise sample to the target CT distribution conditioned on the CBCT. The model was trained on deformed planning CT (dpCT) and CBCT image pairs, and its feasibility was verified in brain patient study and head‐and‐neck (H&N) patient study. The performance of the proposed algorithm was evaluated using mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR) and normalized cross‐correlation (NCC) metrics on generated synthetic CT (sCT) samples. The proposed method was also compared to four other diffusion model‐based sCT generation methods. Results In the brain patient study, the MAE, PSNR, and NCC of the generated sCT were 25.99 HU, 30.49 dB, and 0.99, respectively, compared to 40.63 HU, 27.87 dB, and 0.98 of the CBCT images. In the H&N patient study, the metrics were 32.56 HU, 27.65 dB, 0.98 and 38.99 HU, 27.00, 0.98 for sCT and CBCT, respectively. Compared to the other four diffusion models and one Cycle generative adversarial network (Cycle GAN), the proposed method showed superior results in both visual quality and quantitative analysis. Conclusions The proposed conditional DDPM method can generate sCT from CBCT with accurate HU numbers and reduced artifacts, enabling accurate CBCT‐based organ segmentation and dose calculation for online ART.