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Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D‐CT dataset for lung cancer patients by combining both continuous three‐dimensional (3D) motion captured by 4D‐MRI and the high spatial resolution captured by CT using the authors’ proposed approach.

A novel hybrid approach based on deformable image registration (DIR) and finite element method simulation was developed to fuse a static 3D‐CT volume (acquired under breath‐hold) and the 3D motion information extracted from 4D‐MRI dataset, creating a synthetic 4D‐CT dataset.

The …