Self-contained deep learning-based boosting of 4D cone-beam CT reconstruction.
Journal:
Medical physics
PMID:
33063329
Abstract
PURPOSE: Four-dimensional cone-beam computed tomography (4D CBCT) imaging has been suggested as a solution to account for interfraction motion variability of moving targets like lung and liver during radiotherapy (RT) of moving targets. However, due to severe sparse view sampling artifacts, current 4D CBCT data lack sufficient image quality for accurate motion quantification. In the present paper, we introduce a deep learning-based framework for boosting the image quality of 4D CBCT image data that can be combined with any CBCT reconstruction approach and clinical 4D CBCT workflow.