MR-to-CT Synthesis in MR-only Radiotherapy Based on Deep Learning

Motivation: MRI-only radiotherapy requires synthesizing MR images into CT-equivalent images to calculate the radiation dose. However, the current synthesis methods are limited when applied to small anatomical regions, such as tumors. Goal(s): Our goal was to develop a novel MR-to-CT synthesis algorithm that produces better results for small anatomical structures. Approach: We introduced a multi-branch hybrid perceptual generative model incorporating an attention mechanism to synthesize different scales anatomical structures. Results: Our proposed algorithms yield favorable results for small anatomical structures based on physician feedback and quantitative assessments. Impact: The proposed synthesis algorithm simplifies and speeds up MRI-only radiotherapy workflow. It is also applicable to other fields of medical image synthesis, such as multi-modal image diagnosis treatment.