Development of a patient-specific cone-beam computed tomography dose optimization model using machine learning in image-guided radiation therapy.

Abstract

Cone-beam computed tomography (CBCT) is commonly utilized in radiation therapy to visualize soft tissues and bone structures. This study aims to develop a machine learning model that predicts optimal, patient-specific CBCT doses that minimize radiation exposure while maintaining soft tissue image quality in prostate radiation therapy. Phantom studies evaluated the relationship between dose and two image quality metrics: image standard deviation (SD) and contrast-to-noise ratio (CNR). In a prostate-simulating phantom, CNR did not significantly decrease at doses above 40% compared to the 100% dose. Based on low-contrast resolution, this value was selected as the minimum clinical dose level. In clinical image analysis, both SD and CNR degraded with decreasing dose, consistent with the phantom findings. The structural similarity index between CBCT and planning computed tomography (CT) significantly decreased at doses below 60%, with a mean value of 0.69 at 40%. Previous studies suggest that this level may correspond to acceptable registration accuracy within the typical planning target volume margins applied in image-guided radiotherapy. A machine learning model was developed to predict CBCT doses using patient-specific metrics from planning CT scans and CBCT image quality parameters. Among the tested models, support vector regression achieved the highest accuracy, with an R² value of 0.833 and a root mean squared error of 0.0876, and was therefore adopted for dose prediction. These results support the feasibility of patient-specific CBCT imaging protocols that reduce radiation dose while maintaining clinically acceptable image quality for soft tissue registration.