Minimum imaging dose for deep learning-based pelvic synthetic computed tomography generation from cone beam images.

ABSTRACT:

Background and purpose

Daily cone-beam computed tomography (CBCT) in image-guided radiotherapy administers radiation exposure and subjects patients to secondary cancer risk. Reducing imaging dose remains challenging as image quality deteriorates. We investigated three imaging dose levels by reducing projections and correcting images using two deep learning algorithms, aiming at identifying the lowest achievable imaging dose.

Materials and methods

CBCTs were reconstructed with 100%, 25%, 15% and 10% projections. Models were trained (30), validated (3) and tested (8) with prostate cancer patient datasets. We optimized and compared the performance of 1) a cycle generative adversarial network (cycleGAN) with residual connection and 2) a contrastive unpaired translation network (

SUBMITTER: Chan YCI 

PROVIDER: S-EPMC11519690 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature