Diffusion Model-Based Displacement Field Generation for 4D-CT Chest Image Generation.

Acquisition of data on respiratory motion and anatomical deformation at the individual level is essential for improving the accuracy of surgical procedures and radiotherapy. Although time-series imaging using three-dimensional (3D) computed tomography (CT) and deep learning-based image interpolation have been explored, the acquisition of multiple imaging volumes remains a significant burden on patients because of breath-holding requirements and additional radiation exposure. In this study, we propose a framework for four-dimensional (4D) CT image generation, with images being generated at different time phases from a single-phase 3D chest CT image using only the magnitude of displacement. To achieve this, we employ a conditional diffusion model to generate displacement vector fields (DVFs) and propose a model that incorporates the initial-phase CT image and the mean DVF of the target phase as guidance. We trained and tested the proposed model using 4D-CT images from 62 cases and evaluated its effectiveness by deforming the 3D-CT image at the end-expiration phase using the predicted DVF. The validity of our approach was confirmed through quantitative comparisons under multiple guidance scenarios.Clinical Relevance- The proposed diffusion model predicts Deformation Vector Fields (DVFs) that capture respiratory motion, thereby enabling the generation of 3D-CT images at different respiratory phases from a single-phase CT scan. This approach could be directly applied to radiotherapy planning and we expect that it could improve radiation targeting accuracy.