  Purpose: Estimation of patient-specific organ doses is required for more
comprehensive dose metrics, such as effective dose. Currently, available
methods are performed retrospectively using the CT images themselves, which can
only be done after the scan. To optimize CT acquisitions before scanning, rapid
prediction of patient-specific organ dose is needed prospectively, using
available scout images. We, therefore, devise an end-to-end, fully-automated
deep learning solution to perform real-time, patient-specific, organ-level
dosimetric estimation of CT scans.
  Approach: We propose the Scout-Net model for CT dose prediction at six
different organs as well as for the overall patient body, leveraging the
routinely obtained frontal and lateral scout images of patients, before their
CT scans. To obtain reference values of the organ doses, we used Monte Carlo
simulation and 3D segmentation methods on the corresponding CT images of the
patients.
  Results: We validate our proposed Scout-Net model against real patient CT
data and demonstrate the effectiveness in estimating organ doses in real-time
(only 27 ms on average per scan). Additionally, we demonstrate the efficiency
(real-time execution), sufficiency (reasonable error rates), and robustness
(consistent across varying patient sizes) of the Scout-Net model.
  Conclusions: An effective, efficient, and robust Scout-Net model, once
incorporated into the CT acquisition plan, could potentially guide the
automatic exposure control for balanced image quality and radiation dose.

Computer Vision and Pattern Recognition