This study aims to develop a CT-to-MR conversion system to enhance and optimize Image-Guided Radiation Therapy (IGRT). A deep learning–based image translation model is designed to generate MR-like information from CT images. This approach enables improved soft tissue visualization during radiation therapy planning, supporting more precise treatment. Training and validation processes are applied to ensure image alignment and structural accuracy for clinical use. The proposed technology is expected to improve the efficiency of treatment planning and contribute to patient-specific precision radiation therapy.
Project Period
2021~2022
This study aims to develop a CT-to-MR conversion system to enhance and optimize Image-Guided Radiation Therapy (IGRT). A deep learning–based image translation model is designed to generate MR-like information from CT images. This approach enables improved soft tissue visualization during radiation therapy planning, supporting more precise treatment. Training and validation processes are applied to ensure image alignment and structural accuracy for clinical use. The proposed technology is expected to improve the efficiency of treatment planning and contribute to patient-specific precision radiation therapy.