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1.发明公开Neural network prediction for radiographic x-ray exposures 审中-公开Vorhersagung durch neuronales NetzwerkfürRöntgenaufnahmen
公开(公告)号：EP0979027A2
公开(公告)日：2000-02-09
申请号：EP99306158.9
申请日：1999-08-03
CPC分类号： H05G1/28
摘要： 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 (36,38,40) to identify the spectral characteristics of the x-ray beam, providing a neural net (32) which has been trained to calculate the exposure or Air-Kerma value, and by scaling (34) the neural net output by the calibrated tube efficiency (52), and the actual current through the x-ray tube and the duration of the exposure. The prediction for exposure/Air-Kerma further applies (50) the actual source-toobject distance, and the prediction for exposure/AirKerma area product further applies (54) the actual imaged field area at a source-to-image distance.
摘要翻译： 已经提供了神经网络预测用于在x射线曝光期间以预定义的任意距离预测辐射暴露和/或空气 - 凯尔马（Air-Kerma） 并用于预测放射线照射和/或Air-Kerma区域产品用于放射X光曝光。 可以直接从x射线曝光参数预测空气凯尔玛水平。 提供该方法或模型以通过提供输入变量（36,38,40）来识别X射线束的光谱特征来预测用于任意射线照相X射线曝光的辐射暴露或空气 - 凯尔马，提供神经网络 （32），其已经被训练以计算暴露或空气凯马值，并且通过校准管效率（52）和通过X射线管的实际电流来缩放（34）神经网络输出和持续时间 曝光。 曝光/空气 - 凯尔马的预测进一步适用（50）实际的源对象距离，并且对于曝光/ AirKerma区域产品的预测进一步应用（54）源到图像距离处的实际成像场区域。
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公开(公告)号：EP0979027A3
公开(公告)日：2001-08-29
申请号：EP99306158.9
申请日：1999-08-03
CPC分类号： H05G1/28
摘要： 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 (36,38,40) to identify the spectral characteristics of the x-ray beam, providing a neural net (32) which has been trained to calculate the exposure or Air-Kerma value, and by scaling (34) the neural net output by the calibrated tube efficiency (52), and the actual current through the x-ray tube and the duration of the exposure. The prediction for exposure/Air-Kerma further applies (50) the actual source-toobject distance, and the prediction for exposure/AirKerma area product further applies (54) the actual imaged field area at a source-to-image distance.
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