Abstract: A neural network prediction has been provided for predicting radiation exposure and/or Air-Kerma at a predefined arbitrary distance during an x-ray exposure; and for predicting radiation exposure and/or Air-Kerma area product for a radiographic x-ray exposure. The Air-Kerma levels are predicted directly from the x-ray exposure parameters. The method or model is provided to predict the radiation exposure or Air-Kerma for an arbitrary radiographic x-ray exposure by providing input variables to identify the spectral characteristics of the x-ray beam, providing a neural net which has been trained to calculate the exposure or Air-Kerma value, and by scaling the neural net output by the calibrated tube efficiency, and the actual current through the x-ray tube and the duration of the exposure. The prediction for exposure/Air-Kerma further applies the actual source-to-object distance, and the prediction for exposure/Air-Kerma area product further applies the actual imaged field area at a source-to-image distance.

Abstract: An exposure management and control system and method for x-ray technique selection is provided. Optimal x-ray technique, which affects image quality, can be accurately predetermined to use for any application. A patient model is combined with a closed loop brightness control, using a parameter that does not affect image quality. This is used to create a control system that operates as a knowledge based control system. The x-ray system brightness control system comprises a first and second regulator. The first regulator is an image quality, or brightness, regulator. The brightness regulator can have multiple functions, such as peak mA control, power limiter, skin dose limiter, and an override function. The second regulator is a dose error regulator. The second regulator is independent of the first regulator, and cooperates with the first regulator to provide x-ray technique optimization independent of brightness control.