Machine Learning-Based X-Ray Projection Interpolation for Improved 4D-CBCT Reconstruction.

ABSTRACT: Goal: Respiration-correlated cone-beam computed tomography (4D-CBCT) is an X-ray-based imaging modality that uses reconstruction algorithms to produce time-varying volumetric images of moving anatomy over a cycle of respiratory motion. The quality of the produced images is affected by the number of CBCT projections available for reconstruction. Interpolation techniques have been used to generate intermediary projections to be used, along with the original projections, for reconstruction. Transfer learning is a powerful approach that harnesses the ability to reuse pre-trained models in solving new problems. Methods: Several state-of-the-art pre-trained deep learning models, used for video frame interpolation, are utilized in this work to generate intermediary projections. More

SUBMITTER: Ramesh J 

PROVIDER: S-EPMC11573399 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature