Radiology

Medical imaging

Artificial intelligence

Medical physics

Computer science

Computed tomography

Image-guided radiation therapy

Computer vision

Medicine

Medical Physics

Cone-beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on-treatment CBCT) for accurate patient setup in image-guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as dose calculation and treatment planning. Motivated by the promising performance of deep learning in medical imaging, we propose a deep U-net-based approach that synthesizes CT-like images with accurate numbers from planning CT, while keeping the same anatomical structure as on-treatment CBCT.

Synthetic CT generation from CBCT images via deep learning