CBCT-Based Synthetic CT Generation Using Conditional Flow Matching Model

Abstract: Daily or weekly cone-beam computed tomography (CBCT) is employed in image-guided radiotherapy (IGRT) for precise patient alignment. However, its clinical utility in quantitative tasks is hindered by severe artifacts and inaccurate Hounsfeld unit (HU). It is essential to enhance CBCT image quality to a level comparable with that of conventional CT scans. This study proposed a conditional flow matching model that gradually transforms a sample from normal distribution to the corresponding CT sample conditioned on the input CBCT image. The proposed model was trained using CBCT and deformed planning CT (dpCT) image pairs in a supervised learning scheme. The feasibility of the conditional flow matching model was verified using studies of brain, head-and-neck (HN), and lung patients. The quantitative performance was evaluated using three metrics, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The proposed flow matching model was also compared to other flow matching and diffusion-based generative models for sCT generation. The proposed flow matching model effectively reduced multiple types of artifacts on CBCT images in all the studies. In the study of brain patient, the MAE, PSNR, and NCC of the sCT were improved to 26.02 HU, 32.35 dB, and 0.99, respectively, from 40.63 HU, 27.87 dB, and 0.98 on the CBCT images. In the study of HN patient, the metrics were improved to 33.17 HU, 28.68 dB, 0.98 from 38.99 HU, 27.00 dB, 0.98. In the lung patient study, the metrics were 25.09 HU, 32.81 dB, 0.99 and 32.90 HU, 30.48 dB, 0.98 for sCT and CBCT, respectively. The proposed conditional flow matching model effectively synthesizes high-quality CT-like images from CBCT, achieving accurate HU representation and artifact reduction. This enables more reliable organ segmentation and dose calculation in CBCT-guided online ART workflows.