Supervised machine learning technique for reduction of radiation dose in computed tomography imaging

摘要

Substantial reduction of the radiation dose in computed tomography (CT) imaging is shown using a machine-learning dose-reduction technique. Techniques are provided that (1) enhance low-radiation dosage images, beyond just reducing noise, and (2) may be combined with other approaches, such as adaptive exposure techniques and iterative reconstruction, for radiation dose reduction.